Deep Learning-Based Projection Angle Estimation for Lumbar Oblique Radiography: A Two-Stage Object Detection Approach Using Vertebral–Pedicle Ratio Analysis
Riria Yamamoto, Kaori Tsutsumi, Takaaki Yoshimura, Hiroyuki Sugimori
Applied Sciences, Mar. 2026, [Peer-reviewed], [Corresponding author]
Scientific journal

Privacy-Aware Continual Self-Supervised Learning on Multi-Window Chest Computed Tomography for Domain-Shift Robustness
Ren Tasai, Guang Li, Ren Togo, Takahiro Ogawa, Kenji Hirata, Minghui Tang, Takaaki Yoshimura, Hiroyuki Sugimori, Noriko Nishioka, Yukie Shimizu, Kohsuke Kudo, Miki Haseyama
Bioengineering, Dec. 2025, [Peer-reviewed]
Scientific journal

Superior performance of three-dimensional to two-dimensional convolutional neural network for predicting airflow limitation in patients with chronic obstructive pulmonary disease.
Kaoruko Shimizu, Hiroyuki Sugimori, Naoya Tanabe, Nobuyasu Wakazono, Yoichi M Ito, Hironi Makita, Susumu Sato, Masaharu Nishimura, Toyohiro Hirai, Satoshi Konno
Respiratory investigation, 63, 6, 1316, 1325, 03 Nov. 2025, [Peer-reviewed], [International Magazine]
English, Scientific journal, BACKGROUND: Chronic obstructive pulmonary disease (COPD) may be inconsistent with the severity of airflow limitation. This causes COPD underdiagnosis, necessitating approaches that facilitate timely diagnosis and intervention. Combining deep learning models (based on medical imaging) with regression methods improves numerical functional predictions. We aimed to evaluate and compare the prediction performance of two deep learning-based models (two-dimensional [2D]-convolutional neural network (CNN) and three-dimensional [3D]-CNN) for the percentage predicted forced expiratory volume in 1 s (%FEV1) in patients with COPD. METHODS: ResNet18-based regression prediction models were constructed for %FEV1 based on 200 computed tomography (CT) datasets. Five-fold cross-validation was performed to develop the predictive models, which were externally validated using 20 data points. In addition, 200 internal CT datasets were assessed using commercial software to develop a regression model for predicting airway (% wall area) and parenchymal indices (% low-attenuation volume). RESULTS: The 3D-CNN model demonstrated superior performance with an average root mean squared error (RMSE) of 10.73 and a correlation coefficient of 0.88, compared with that of the 2D-CNN model (RMSE: 16.76, correlation coefficient: 0.66) during internal validation. In the external validation approach, the 3D-CNN model maintained a performance (RMSE: 11.48, correlation coefficient: 0.59) better than that of the 2D-CNN model (RMSE: 12.38, correlation coefficient: 0.47), with both models outperforming the commercial software analysis (RMSE: 23.18). CONCLUSIONS: Volumetric analysis using 3D-CNN may sufficiently capture the complex structural features of COPD in CT images. Further studies are required to validate these models with larger datasets and determine their validity for longitudinal applications.

Development of an XAI-Enhanced Deep-Learning Algorithm for Automated Decision-Making on Shoulder-Joint X-Ray Retaking
Konatsu Sekiura, Takaaki Yoshimura, Hiroyuki Sugimori
Applied Sciences, Sep. 2025, [Peer-reviewed], [Corresponding author]
Scientific journal

Feasibility and Acceptability of a Deep-Learning-Based Nipple Trauma Assessment System for Postpartum Breastfeeding Support
Maya Nakamura, Hiroyuki Sugimori, Yasuhiko Ebina
Healthcare, Aug. 2025, [Peer-reviewed]
Scientific journal

Artificial intelligence-integrated video analysis of vessel area changes and instrument motion for microsurgical skill assessment.
Taku Sugiyama, Minghui Tang, Hiroyuki Sugimori, Marin Sakamoto, Miki Fujimura
Scientific reports, 15, 1, 27898, 27898, 31 Jul. 2025, [Peer-reviewed], [International Magazine]
English, Scientific journal, Mastering microsurgical skills is essential for neurosurgical trainees. Video-based analysis of target tissue changes and surgical instrument motion provides an objective, quantitative method for assessing microsurgical proficiency, potentially enhancing training and patient safety. This study evaluates the effectiveness of an artificial intelligence (AI)-based video analysis model in assessing microsurgical performance and examines the correlation between AI-derived parameters and specific surgical skill components. A dual AI framework was developed, integrating a semantic segmentation model for artificial blood vessel analysis with an instrument tip-tracking algorithm. These models quantified dynamic vessel area fluctuation, tissue deformation error count, instrument path distance, and normalized jerk index during a single-stitch end-to-side anastomosis task performed by 14 surgeons with varying experience levels. The AI-derived parameters were validated against traditional criteria-based rating scales assessing instrument handling, tissue respect, efficiency, suture handling, suturing technique, operation flow, and overall performance. Rating scale scores correlated with microsurgical experience, exhibiting a bimodal distribution that classified performance into good and poor groups. Video-based parameters showed strong correlations with various skill categories. Receiver operating characteristic analysis demonstrated that combining these parameters improved the discrimination of microsurgical performance. The proposed method effectively captures technical microsurgical skills and can assess performance.

Role of Silver Nipple Protectors in Treating Nipple Trauma: A Non-Randomized Comparative Trial.
Maya Nakamura, Hiroyuki Sugimori, Yoko Asaka, Yasuhiko Ebina
Journal of human lactation : official journal of International Lactation Consultant Association, 8903344251342564, 8903344251342564, 19 Jun. 2025, [Peer-reviewed], [International Magazine]
English, Scientific journal, BACKGROUND: Breastfeeding is crucial for infant health, but nipple trauma remains a common challenge. In particular, nipple trauma can lead to the onset of mastitis and psychological distress for mothers. Silver nipple protectors have been suggested to alleviate this issue, but detailed research is needed. RESEARCH AIM: This study aims to clarify the effectiveness of silver nipple protectors in treating nipple trauma in Japanese women. METHODS: A non-randomized comparative trial compared 47 participants (94 nipples) using silver nipple protectors with a control group of 50 participants (100 nipples) from historical data. The assessment included chronological changes in nipple condition, level of nipple pain, and safety, among other factors. Data collection spanned from 2023 to 2024. RESULTS: The group using silver protectors experienced fewer instances of severe nipple trauma and showed a higher frequency of healing patterns. Many of these patterns involved a transition from mild erythema or swelling toward a healing state. Specifically, the level of nipple pain on the 4th postpartum day was significantly lower. No safety issues from the use of silver protectors were noted. CONCLUSION: This study suggests that silver nipple protectors may prevent the occurrence of severe nipple trauma and are safe and beneficial for Japanese women. Future research should focus on the mechanism of silver protectors, their long-term effects, regional differences, practical challenges for implementation, and comparisons with other common treatments.

Continual Self-supervised Learning Considering Medical Domain Knowledge in Chest CT Images
Ren Tasai, Guang Li, Ren Togo, Minghui Tang, Takaaki Yoshimura, Hiroyuki Sugimori, Kenji Hirata, Takahiro Ogawa, Kohsuke Kudo, Miki Haseyama
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1, 5, IEEE, 06 Apr. 2025
International conference proceedings

Rapid Right Coronary Artery Extraction from CT Images via Global–Local Deep Learning Method Based on GhostNet
Yanjun Li, Takaaki Yoshimura, Hiroyuki Sugimori
Electronics, 14, 7, 1399, 1399, MDPI AG, 31 Mar. 2025, [Peer-reviewed], [Corresponding author]
Scientific journal, The right coronary artery plays a crucial role in cardiac function and its accurate extraction and 3D reconstruction from CT images are essential for diagnosing and treating coronary artery disease. This study proposes a novel, automated, deep learning pipeline that integrates a transformer-based network with GhostNet to improve segmentation and 3D reconstruction. The dataset comprised CT images from 32 patients, with the segmentation model effectively extracting vascular cross-sections, achieving an F1 score of 0.887 and an Intersection over Union of 0.797. Meanwhile, the proposed model achieved an inference speed of 7.03 ms, outperforming other state-of-the-art networks used for comparison, making it highly suitable for real-time clinical applications. Compared to conventional methods, the proposed approach demonstrates superior segmentation performance while maintaining computational efficiency. The results indicate that this framework has the potential to significantly improve diagnostic accuracy and interventional planning for coronary artery disease. Future work will focus on expanding dataset diversity, refining real-time processing capabilities, and extending the methodology to other vascular structures.

Automated Coronary Artery Identification in CT Angiography: A Deep Learning Approach Using Bounding Boxes
Marin Sakamoto, Takaaki Yoshimura, Hiroyuki Sugimori
Applied Sciences, 15, 6, 3113, 3113, MDPI AG, 13 Mar. 2025, [Peer-reviewed], [Corresponding author]
Scientific journal, Introduction: Ischemic heart disease represents one of the main causes of mortality and morbidity, requiring accurate, noninvasive imaging. Coronary Computed Tomography Angiography (CCTA) offers a detailed coronary assessment but can be labor-intensive and operator-dependent. Methods: We developed a bounding box-based object detection method using deep learning to identify the right coronary artery (RCA), left anterior descending artery (LCA-LAD), and left circumflex artery (LCA-CX) in the CCTA cross-sections. A total of 19,047 images, which were recorded from 52 patients, underwent a five-fold cross-validation. The evaluation metrics included Average Precision (AP), Intersection over Union (IoU), Dice Similarity Coefficient (DSC), and Mean Absolute Error (MAE) to achieve both detection accuracy and spatial localization precision. Results: The mean AP scores for RCA, LCA-LAD, and LCA-CX were 0.71, 0.70, and 0.61, respectively. IoU and DSC indicated a better overlap for LCA-LAD, whereas LCA-CX was more challenging to detect. The MAE analysis showed the largest centroid deviation in RCA, highlighting variable performance across the artery classes. Discussion: These findings demonstrate the feasibility of automated coronary artery detection, potentially reducing observer variability and expediting CCTA analysis. They also highlight the need to refine the approach for complex anatomical variants or calcified plaques. Conclusion: A bounding box-based approach can thereby streamline clinical workflows by localizing major coronary arteries. Future research with diverse datasets and advanced visualization techniques may further enhance diagnostic accuracy and efficiency.

Improving Cerebrovascular Imaging with Deep Learning: Semantic Segmentation for Time-of-Flight Magnetic Resonance Angiography Maximum Intensity Projection Image Enhancement
Tomonari Yamada, Takaaki Yoshimura, Shota Ichikawa, Hiroyuki Sugimori
Applied Sciences, 15, 6, 3034, 3034, MDPI AG, 11 Mar. 2025, [Peer-reviewed], [Corresponding author]
Scientific journal, Magnetic Resonance Angiography (MRA) is widely used for cerebrovascular assessment, with Time-of-Flight (TOF) MRA being a common non-contrast imaging technique. However, maximum intensity projection (MIP) images generated from TOF-MRA often include non-essential vascular structures such as external carotid branches, requiring manual editing for accurate visualization of intracranial arteries. This study proposes a deep learning-based semantic segmentation approach to automate the removal of these structures, enhancing MIP image clarity while reducing manual workload. Using DeepLab v3+, a convolutional neural network model optimized for segmentation accuracy, the method achieved an average Dice Similarity Coefficient (DSC) of 0.9615 and an Intersection over Union (IoU) of 0.9261 across five-fold cross-validation. The developed system processed MRA datasets at an average speed of 16.61 frames per second, demonstrating real-time feasibility. A dedicated software tool was implemented to apply the segmentation model directly to DICOM images, enabling fully automated MIP image generation. While the model effectively removed most external carotid structures, further refinement is needed to improve venous structure suppression. These results indicate that deep learning can provide an efficient and reliable approach for automated cerebrovascular image processing, with potential applications in clinical workflows and neurovascular disease diagnosis.

Application of 9-Channel Pseudo-Color Maps in Deep Learning for Intracranial Hemorrhage Detection
Shimpei sato, Daisuke Oura, Hiroyuki Sugimori
Multimodal Technologies and Interaction, Feb. 2025, [Peer-reviewed], [Corresponding author]
Scientific journal

Reproducing Gradient Field Application to Localize Magnetic Resonance Signals Using MRI Simulation
Noriyuki Tawara, Hiroyuki Sugimori, Kojiro Yamaguchi
Investigative Magnetic Resonance Imaging, 29, 4, 225, 225, XMLink, 2025, [Peer-reviewed]
Scientific journal

Functional range of motion for basic seated activities of daily living tasks.
Yuji Inagaki, Tomoya Ishida, Hiroyuki Sugimori, Takaaki Yoshimura, Akihiro Watanabe, Yumene Naito, Daisuke Sawamura
Frontiers in sports and active living, 7, 1646326, 1646326, 2025, [Peer-reviewed], [International Magazine]
English, Scientific journal, INTRODUCTION: Efficient performance of activities of daily living (ADLs) requires coordinated movement across multiple upper-limb joints. However, current assessments of joint range of motion (ROM) during ADLs often rely on subjective evaluation and lack precise quantitative data. The functional ROM required for upper-limb movements in a seated position remains unclear, despite its clinical relevance for older adults and individuals with mobility limitations who frequently perform ADLs while seated. Additionally, little is known about how joint-motion requirements differ across similar ADL tasks, such as eating with a spoon versus chopsticks or washing the top versus the back of the head. To address these issues, we aimed to establish standardized ROM values for common upper-limb-related ADLs using three-dimensional motion analysis to enhance rehabilitation goal setting. METHODS: Thirty-one healthy adults (14 women; mean age 22.9 ± 1.9 years) completed six seated ADLs-face washing; hair washing (top, back); chopstick or spoon eating; bottled-water drinking. Marker-based motion capture (International society of biomechanics guidelines) recorded kinematics. Descriptive statistics and paired t-tests (p < 0.05) assessed task differences. RESULTS: Significant differences in upper limb and neck joint angles were observed across ADL tasks. Shoulder elevation was highest during back hair washing (105.0° ± 14.6°) and lowest when eating with chopsticks (39.2° ± 10.9°). Elbow flexion peaked during face washing (122.3° ± 5.2°) and back hair washing (127.9° ± 5.7°), reflecting the need for close hand-to-face contact. Wrist extension was greatest during face washing (-28.7° ± 8.5°), while a significant difference was found between chopstick (-13.7° ± 12.5°) and spoon use (-5.6° ± 5.3°, p = 0.005), indicating task-specific hand control demands. Neck flexion also varied significantly between hair washing conditions (back > top, p < 0.001). Furthermore, when eating with a bowl rather than with a plate, participants showed significantly greater shoulder elevation, elbow flexion, and forearm rotation (p < 0.01), suggesting increased ROM demands shaped by Japanese eating customs. DISCUSSION: These reference ROMs offer objective targets for seated-ADL rehabilitation and assistive-device design. validation in older adults and clinical populations is warranted to confirm applicability and guide goal setting.

Automatic Aortic Valve Extraction Using Deep Learning with Contrast-Enhanced Cardiac CT Images
Soichiro Inomata, Takaaki Yoshimura, Minghui Tang, Shota Ichikawa, Hiroyuki Sugimori
Journal of Cardiovascular Development and Disease, 12, 1, 3, 3, MDPI AG, 25 Dec. 2024, [Peer-reviewed], [Corresponding author]
Scientific journal, Purpose: This study evaluates the use of deep learning techniques to automatically extract and delineate the aortic valve annulus region from contrast-enhanced cardiac CT images. Two approaches, namely, segmentation and object detection, were compared to determine their accuracy. Materials and Methods: A dataset of 32 contrast-enhanced cardiac CT scans was analyzed. The segmentation approach utilized the DeepLabv3+ model, while the object detection approach employed YOLOv2. The dataset was augmented through rotation and scaling, and five-fold cross-validation was applied. The accuracy of both methods was evaluated using the Dice similarity coefficient (DSC), and their performance in estimating the aortic valve annulus area was compared. Results: The object detection approach achieved a mean DSC of 0.809, significantly outperforming the segmentation approach, which had a mean DSC of 0.711. Object detection also demonstrated higher precision and recall, with fewer false positives and negatives. The aortic valve annulus area estimation had a mean error of 2.55 mm. Conclusions: Object detection showed superior performance in identifying the aortic valve annulus region, suggesting its potential for clinical application in cardiac imaging. The results highlight the promise of deep learning in improving the accuracy and efficiency of preoperative planning for cardiovascular interventions.

Development of Nipple Trauma Evaluation System With Deep Learning.
Maya Nakamura, Hiroyuki Sugimori, Yasuhiko Ebina
Journal of human lactation : official journal of International Lactation Consultant Association, 8903344241303867, 8903344241303867, 24 Dec. 2024, [Peer-reviewed], [International Magazine]
English, Scientific journal, BACKGROUND: No research has been conducted on the use of deep learning for breastfeeding support. RESEARCH AIM: This study aims to develop a nipple trauma evaluation system using deep learning. METHODS: We used an exploratory data analysis approach to develop a deep-learning model for medical imaging. Employing object detection and classification, this Japanese study retrieved 753 images from a previous study. The classification protocol, based on the "seven signs of nipple trauma associated with breastfeeding," categorized the images into eight classes. For practical purposes, the eight original classes were consolidated into four broader categories: "None," "Minor," "Moderate," and "Severe," using data augmentation procedures that were consistent with the original classification system. The Precision, Recall, Overall Accuracy, and Area Under the Curve (AUC) were calculated, and the model's efficiency was evaluated using Frames Per Second (FPS). RESULTS: The object detector's high mean average precision and frames per second rate for nipple and areola detection, confirmed exceptional accuracy. The eight-class image classifier returned notable AUC values, with fissures, peeling, purpura, and scabbing exceeding 0.8. The highest average recall and precision was for scabbing, and the lowest for blistering. The four-class classifier accurately predicted severe conditions, with an average AUC > 0.7, whereas categories without classifications and those deemed minor had lower recall and precision rates. CONCLUSIONS: A sophisticated deep learning system detects and classifies nipple trauma automatically, potentially aiding breastfeeding caregivers through objective image assessment and operational improvements. ABSTRACT IN JAPANESE: ： におけるのにするはわれていない。： は、をいたシステムのをとした。： では、をいたモデルをするため、データアプローチをいた。およびのをい、でわれたでされた753のをした。「にうの７」にづき、を8クラスにした。をし、4つのカテゴリ「なし」、「」、「」、「」の4つのカテゴリにし、のシステムにするデータをった。、、Overall Accuracy、AUC（）をし、モデルのはFPS（Frames Per Second）でした。： におけるいmAP（）とFPSがされ、およびのがされた。8クラスのは、、、、で0.8をえるなAUCがられた。とがもかったのはであり、でもかった。4クラスのはのをにし、AUCは0.7をえたが、なしやとされるカテゴリはとがいとなった。： をしたこのなシステムは、のとをでうことができ、なをじて、のとをサポートするなツールとなりる。Back Translation Completed by Hiroko Hongo, MSW, PhD, IBCLC.

Lung Cancer Classification Using Masked Autoencoder Pretrained on J-MID Database
Ren Tasai, Guang Li, Ren Togo, Minghui Tang, Takaaki Yoshimura, Hiroyuki Sugimori, Kenji Hirata, Takahiro Ogawa, Kohsuke Kudo, Miki Haseyama
2024 IEEE 13th Global Conference on Consumer Electronics (GCCE), 456, 457, IEEE, 29 Oct. 2024
International conference proceedings

A Hessian-Based Deep Learning Preprocessing Method for Coronary Angiography Image Analysis
Yanjun Li, Takaaki Yoshimura, Yuto Horima, Hiroyuki Sugimori
Electronics, Sep. 2024, [Peer-reviewed], [Corresponding author]
Scientific journal

Assessment of accuracy and repeatability of quantitative parameter mapping in MRI
Yuya Hirano, Kinya Ishizaka, Hiroyuki Sugimori, Yo Taniguchi, Tomoki Amemiya, Yoshitaka Bito, Kohsuke Kudo
Radiological Physics and Technology, Springer Science and Business Media LLC, 28 Aug. 2024, [Peer-reviewed]
Scientific journal, Abstract

We aimed to evaluate the accuracy and repeatability of the T1, T2*, and proton density (PD) values obtained by quantitative parameter mapping (QPM) using the ISMRM/NIST MRI system phantom and compared them with computer simulations. We compared the relaxation times and PD obtained through QPM with the reference values of the ISMRM/NIST MRI system phantom and conventional methods. Furthermore, we evaluated the presence or absence of influences other than noise in T1 and T2* values obtained by QPM by comparing the obtained coefficient of variation (CV) with simulation results. The T1, T2*, and PD values by QPM showed a strong correlation with the measured values and the referenced values. The simulated CVs of QPM calculated for each sphere showed similar trends to those of the actual scans.

Artificial Intelligence for Patient Safety and Surgical Education in Neurosurgery
Taku Sugiyama, Hiroyuki Sugimori, Mighui Tang, Miki Fujimura
JMA Journal, Aug. 2024, [Peer-reviewed]

The Effectiveness of Semi-Supervised Learning Techniques in Identifying Calcifications in X-ray Mammography and the Impact of Different Classification Probabilities
Miu Sakaida, Takaaki Yoshimura, Minghui Tang, Shota Ichikawa, Hiroyuki Sugimori, Kenji Hirata, Kohsuke Kudo
Applied Sciences, 14, 14, 5968, 5968, MDPI AG, 09 Jul. 2024, [Peer-reviewed], [Corresponding author]
Scientific journal, Identifying calcifications in mammograms is crucial for early breast cancer detection, and semi-supervised learning, which utilizes a small dataset for supervised learning combined with deep learning, is anticipated to be an effective approach for automating this identification process. This study explored the impact of semi-supervised learning on identifying mammographic calcifications by including 712 mammographic images from 252 patients in public datasets. Initially, 212 mammogram images were segmented into patches and classified visually for calcification presence. A subset of these patches, derived from 169 mammogram images, was used to train a ResNet50-based classifier. The classifier was evaluated using patches generated from 43 mammograms as a test data set. Additionally, 500 more mammogram images were processed into patches and analyzed using the trained ResNet50 model, with semi-supervised learning applied to patches exceeding certain classification probabilities. This process aimed to enhance the classifier’s accuracy and achieve improvements over the initial model. The findings indicated that semi-supervised learning significantly benefits the accuracy of calcification detection in mammography, underscoring its utility in enhancing diagnostic methodologies.

Deep learning-based correction for time truncation in cerebral computed tomography perfusion.
Shota Ichikawa, Makoto Ozaki, Hideki Itadani, Hiroyuki Sugimori, Yohan Kondo
Radiological physics and technology, 11 Jun. 2024, [Peer-reviewed], [Domestic magazines]
English, Scientific journal, Cerebral computed tomography perfusion (CTP) imaging requires complete acquisition of contrast bolus inflow and washout in the brain parenchyma; however, time truncation undoubtedly occurs in clinical practice. To overcome this issue, we proposed a three-dimensional (two-dimensional + time) convolutional neural network (CNN)-based approach to predict missing CTP image frames at the end of the series from earlier acquired image frames. Moreover, we evaluated three strategies for predicting multiple time points. Seventy-two CTP scans with 89 frames and eight slices from a publicly available dataset were used to train and test the CNN models capable of predicting the last 10 image frames. The prediction strategies were single-shot prediction, recursive multi-step prediction, and direct-recursive hybrid prediction.Single-shot prediction predicted all frames simultaneously, while recursive multi-step prediction used prior predictions as input for subsequent steps, and direct-recursive hybrid prediction employed separate models for each step with prior predictions as input for the next step. The accuracies of the predicted image frames were evaluated in terms of image quality, bolus shape, and clinical perfusion parameters. We found that the image quality metrics were superior when multiple CTP images were predicted simultaneously rather than recursively. The bolus shape also showed the highest correlation (r = 0.990, p < 0.001) and the lowest variance (95% confidence interval, -453.26-445.53) in the single-shot prediction. For all perfusion parameters, the single-shot prediction had the smallest absolute differences from ground truth. Our proposed approach can potentially minimize time truncation errors and support the accurate quantification of ischemic stroke.

Artificial Intelligence Quantification of Enhanced Synovium Throughout the Entire Hand in Rheumatoid Arthritis on Dynamic Contrast-Enhanced MRI.
Yijun Mao, Kiko Imahori, Wanxuan Fang, Hiroyuki Sugimori, Shinji Kiuch, Kenneth Sutherland, Tamotsu Kamishima
Journal of magnetic resonance imaging : JMRI, 28 May 2024, [Peer-reviewed], [International Magazine]
English, Scientific journal, BACKGROUND: Challenges persist in achieving automatic and efficient inflammation quantification using dynamic contrast-enhanced (DCE) MRI in rheumatoid arthritis (RA) patients. PURPOSE: To investigate an automatic artificial intelligence (AI) approach and an optimized dynamic MRI protocol for quantifying disease activity in RA in whole hands while excluding arterial pixels. STUDY TYPE: Retrospective. SUBJECTS: Twelve RA patients underwent DCE-MRI with 27 phases for creating the AI model and tested on images with a variable number of phases from 35 RA patients. FIELD STRENGTH/SEQUENCE: 3.0 T/DCE T1-weighted gradient echo sequence (mDixon, water image). ASSESSMENT: The model was trained with various DCE-MRI time-intensity number of phases. Evaluations were conducted for similarity between AI segmentation and manual outlining in 51 ROIs with synovitis. The relationship between synovial volume via AI segmentation with rheumatoid arthritis magnetic resonance imaging scoring (RAMRIS) across whole hands was then evaluated. The reference standard was determined by an experienced musculoskeletal radiologist. STATISTICAL TEST: Area under the curve (AUC) of receiver operating characteristic (ROC), Dice and Spearman's rank correlation coefficients, and interclass correlation coefficient (ICC). A P-value <0.05 was considered statistically significant. RESULTS: A minimum of 15 phases (acquisition time at least 2.5 minutes) was found to be necessary. AUC ranged from 0.941 ± 0.009 to 0.965 ± 0.009. The Dice score was 0.557-0.615. Spearman's correlation coefficients between the AI model and ground truth were 0.884-0.927 and 0.736-0.831, for joint ROIs and whole hands, respectively. The Spearman's correlation coefficient for the additional test set between the model trained with 15 phases and RAMRIS was 0.768. CONCLUSION: The AI-based classification model effectively identified synovitis pixels while excluding arteries. The optimal performance was achieved with at least 15 phases, providing a quantitative assessment of inflammatory activity in RA while minimizing acquisition time. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Fully automatic quantification for hand synovitis in rheumatoid arthritis using pixel-classification-based segmentation network in DCE-MRI.
Wanxuan Fang, Yijun Mao, Haolin Wang, Hiroyuki Sugimori, Shinji Kiuch, Kenneth Sutherland, Tamotsu Kamishima
Japanese journal of radiology, 24 May 2024, [Peer-reviewed], [Domestic magazines]
English, Scientific journal, PURPOSE: A classification-based segmentation method is proposed to quantify synovium in rheumatoid arthritis (RA) patients using a deep learning (DL) method based on time-intensity curve (TIC) analysis in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). MATERIALS AND METHODS: This retrospective study analyzed a hand MR dataset of 28 RA patients (six males, mean age 53.7 years). A researcher, under expert guidance, used in-house software to delineate regions of interest (ROIs) for hand muscles, bones, and synovitis, generating a dataset with 27,255 pixels with corresponding TICs (muscle: 11,413, bone: 8502, synovitis: 7340). One experienced musculoskeletal radiologist performed ground truth segmentation of enhanced pannus in the joint bounding box on the 10th DCE phase, or around 5 min after contrast injection. Data preprocessing included median filtering for noise reduction, phase-only correlation algorithm for motion correction, and contrast-limited adaptive histogram equalization for improved image contrast and noise suppression. TIC intensity values were normalized using zero-mean normalization. A DL model with dilated causal convolution and SELU activation function was developed for enhanced pannus segmentation, tested using leave-one-out cross-validation. RESULTS: 407 joint bounding boxes were manually segmented, with 129 synovitis masks. On the pixel-based level, the DL model achieved sensitivity of 85%, specificity of 98%, accuracy of 99% and precision of 84% for enhanced pannus segmentation, with a mean Dice score of 0.73. The false-positive rate for predicting cases without synovitis was 0.8%. DL-measured enhanced pannus volume strongly correlated with ground truth at both pixel-based (r = 0.87, p < 0.001) and patient-based levels (r = 0.84, p < 0.001). Bland-Altman analysis showed the mean difference for hand joints at the pixel-based and patient-based levels were -9.46 mm3 and -50.87 mm3, respectively. CONCLUSION: Our DL-based DCE-MRI TIC shape analysis has the potential for automatic segmentation and quantification of enhanced synovium in the hands of RA patients.

Assessment of changes in vessel area during needle manipulation in microvascular anastomosis using a deep learning-based semantic segmentation algorithm: A pilot study.
Minghui Tang, Taku Sugiyama, Ren Takahari, Hiroyuki Sugimori, Takaaki Yoshimura, Katsuhiko Ogasawara, Kohsuke Kudo, Miki Fujimura
Neurosurgical review, 47, 1, 200, 200, 09 May 2024, [Peer-reviewed], [International Magazine]
English, Scientific journal, Appropriate needle manipulation to avoid abrupt deformation of fragile vessels is a critical determinant of the success of microvascular anastomosis. However, no study has yet evaluated the area changes in surgical objects using surgical videos. The present study therefore aimed to develop a deep learning-based semantic segmentation algorithm to assess the area change of vessels during microvascular anastomosis for objective surgical skill assessment with regard to the "respect for tissue." The semantic segmentation algorithm was trained based on a ResNet-50 network using microvascular end-to-side anastomosis training videos with artificial blood vessels. Using the created model, video parameters during a single stitch completion task, including the coefficient of variation of vessel area (CV-VA), relative change in vessel area per unit time (ΔVA), and the number of tissue deformation errors (TDE), as defined by a ΔVA threshold, were compared between expert and novice surgeons. A high validation accuracy (99.1%) and Intersection over Union (0.93) were obtained for the auto-segmentation model. During the single-stitch task, the expert surgeons displayed lower values of CV-VA (p < 0.05) and ΔVA (p < 0.05). Additionally, experts committed significantly fewer TDEs than novices (p < 0.05), and completed the task in a shorter time (p < 0.01). Receiver operating curve analyses indicated relatively strong discriminative capabilities for each video parameter and task completion time, while the combined use of the task completion time and video parameters demonstrated complete discriminative power between experts and novices. In conclusion, the assessment of changes in the vessel area during microvascular anastomosis using a deep learning-based semantic segmentation algorithm is presented as a novel concept for evaluating microsurgical performance. This will be useful in future computer-aided devices to enhance surgical education and patient safety.

Development of a Method for Estimating the Angle of Lumbar Spine X-ray Images Using Deep Learning with Pseudo X-ray Images Generated from Computed Tomography
Ryuma Moriya, Takaaki Yoshimura, Minghui Tang, Shota Ichikawa, Hiroyuki Sugimori
Applied Sciences, 14, 9, 3794, 3794, MDPI AG, 29 Apr. 2024, [Peer-reviewed], [Corresponding author], [International Magazine]
English, Scientific journal, Background and Objectives: In lumbar spine radiography, the oblique view is frequently utilized to assess the presence of spondylolysis and the morphology of facet joints. It is crucial to instantly determine whether the oblique angle is appropriate for the evaluation and the necessity of retakes after imaging. This study investigates the feasibility of using a convolutional neural network (CNN) to estimate the angle of lumbar oblique images. Since there are no existing lumbar oblique images with known angles, we aimed to generate synthetic lumbar X-ray images at arbitrary angles from computed tomography (CT) images and to estimate the angles of these images using a trained CNN. Methods: Synthetic lumbar spine X-ray images were created from CT images of 174 individuals by rotating the lumbar spine from 0° to 60° in 5° increments. A line connecting the center of the spinal canal and the spinous process was used as the baseline to define the shooting angle of the synthetic X-ray images based on how much they were tilted from the baseline. These images were divided into five subsets and trained using ResNet50, a CNN for image classification, implementing 5-fold cross-validation. The models were trained for angle estimation regression and image classification into 13 classes at 5° increments from 0° to 60°. For model evaluation, mean squared error (MSE), root mean squared error (RMSE), and the correlation coefficient (r) were calculated for regression analysis, and the area under the curve (AUC) was calculated for classification. Results: In the regression analysis for angles from 0° to 60°, the MSE was 14.833 degree2, the RMSE was 3.820 degrees, and r was 0.981. The average AUC for the 13-class classification was 0.953. Conclusion: The CNN developed in this study was able to estimate the angle of an lumbar oblique image with high accuracy, suggesting its usefulness.

Deep learning to assess right ventricular ejection fraction from two-dimensional echocardiograms in precapillary pulmonary hypertension.
Michito Murayama, Hiroyuki Sugimori, Takaaki Yoshimura, Sanae Kaga, Hideki Shima, Satonori Tsuneta, Aoi Mukai, Yui Nagai, Shinobu Yokoyama, Hisao Nishino, Junichi Nakamura, Takahiro Sato, Ichizo Tsujino
Echocardiography (Mount Kisco, N.Y.), 41, 4, e15812, Apr. 2024, [Peer-reviewed], [International Magazine]
English, Scientific journal, BACKGROUND: Precapillary pulmonary hypertension (PH) is characterized by a sustained increase in right ventricular (RV) afterload, impairing systolic function. Two-dimensional (2D) echocardiography is the most performed cardiac imaging tool to assess RV systolic function; however, an accurate evaluation requires expertise. We aimed to develop a fully automated deep learning (DL)-based tool to estimate the RV ejection fraction (RVEF) from 2D echocardiographic videos of apical four-chamber views in patients with precapillary PH. METHODS: We identified 85 patients with suspected precapillary PH who underwent cardiac magnetic resonance imaging (MRI) and echocardiography. The data was divided into training (80%) and testing (20%) datasets, and a regression model was constructed using 3D-ResNet50. Accuracy was assessed using five-fold cross validation. RESULTS: The DL model predicted the cardiac MRI-derived RVEF with a mean absolute error of 7.67%. The DL model identified severe RV systolic dysfunction (defined as cardiac MRI-derived RVEF < 37%) with an area under the curve (AUC) of .84, which was comparable to the AUC of RV fractional area change (FAC) and tricuspid annular plane systolic excursion (TAPSE) measured by experienced sonographers (.87 and .72, respectively). To detect mild RV systolic dysfunction (defined as RVEF ≤ 45%), the AUC from the DL-predicted RVEF also demonstrated a high discriminatory power of .87, comparable to that of FAC (.90), and significantly higher than that of TAPSE (.67). CONCLUSION: The fully automated DL-based tool using 2D echocardiography could accurately estimate RVEF and exhibited a diagnostic performance for RV systolic dysfunction comparable to that of human readers.

Imaging of 17O-labeled Water Using Fast T2 Mapping with T2-preparation: A Phantom Study.
Hiroyuki Kameda, Yumi Nakada, Yuta Urushibata, Hiroyuki Sugimori, Takaaki Fujii, Naoya Kinota, Daisuke Kato, Minghui Tang, Keita Sakamoto, Kohsuke Kudo
Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine, 15 Mar. 2024, [Peer-reviewed], [Domestic magazines]
English, Scientific journal, 17O-labeled water is a T2-shortening contrast agent used in proton MRI and is a promising method for visualizing cerebrospinal fluid (CSF) dynamics because it provides long-term tracking of water molecules. However, various external factors reduce the accuracy of 17O-concentration measurements using conventional signal-intensity-based methods. In addition, T2 mapping, which is expected to provide a stable assessment, is generally limited to temporal-spatial resolution. We developed the T2-prepared based on T2 mapping used in cardiac imaging to adapt to long T2 values and tested whether it could accurately measure 17O-concentration in the CSF using a phantom. The results showed that 17O-concentration in a fluid mimicking CSF could be evaluated with an accuracy comparable to conventional T2-mapping (Carr-Purcell-Meiboom-Gill multi-echo spin-echo method). This method allows 17O-imaging with a high temporal resolution and stability in proton MRI. This imaging technique may be promising for visualizing CSF dynamics using 17O-labeled water.

A Preprocessing Method for Coronary Artery Stenosis Detection Based on Deep Learning
Yanjun Li, Takaaki Yoshimura, Yuto Horima, Hiroyuki Sugimori
Algorithms, 17, 3, 119, 119, MDPI AG, 13 Mar. 2024, [Peer-reviewed], [Corresponding author]
Scientific journal, The detection of coronary artery stenosis is one of the most important indicators for the diagnosis of coronary artery disease. However, stenosis in branch vessels is often difficult to detect using computer-aided systems and even radiologists because of several factors, such as imaging angle and contrast agent inhomogeneity. Traditional coronary artery stenosis localization algorithms often only detect aortic stenosis and ignore branch vessels that may also cause major health threats. Therefore, improving the localization of branch vessel stenosis in coronary angiographic images is a potential development property. In this study, we propose a preprocessing approach that combines vessel enhancement and image fusion as a prerequisite for deep learning. The sensitivity of the neural network to stenosis features is improved by enhancing the blurry features in coronary angiographic images. By validating five neural networks, such as YOLOv4 and R-FCN-Inceptionresnetv2, our proposed method can improve the performance of deep learning network applications on the images from six common imaging angles. The results showed that the proposed method is suitable as a preprocessing method for coronary angiographic image processing based on deep learning and can be used to amend the recognition ability of the deep model for fine vessel stenosis.

Estimating Body Weight From Measurements From Different Single-Slice Computed Tomography Levels: An Evaluation of Total Cross-Sectional Body Area Measurements and Deep Learning.
Shota Ichikawa, Hiroyuki Sugimori
Journal of computer assisted tomography, 27 Feb. 2024, [Peer-reviewed], [Corresponding author], [International Magazine]
English, Scientific journal, OBJECTIVE: This study aimed to evaluate the correlation between the estimated body weight obtained from 2 easy-to-perform methods and the actual body weight at different computed tomography (CT) levels and determine the best reference site for estimating body weight. METHODS: A total of 862 patients from a public database of whole-body positron emission tomography/CT studies were retrospectively analyzed. Two methods for estimating body weight at 10 single-slice CT levels were evaluated: a linear regression model using total cross-sectional body area and a deep learning-based model. The accuracy of body weight estimation was evaluated using the mean absolute error (MAE), root mean square error (RMSE), and Spearman rank correlation coefficient (ρ). RESULTS: In the linear regression models, the estimated body weight at the T5 level correlated best with the actual body weight (MAE, 5.39 kg; RMSE, 7.01 kg; ρ = 0.912). The deep learning-based models showed the best accuracy at the L5 level (MAE, 6.72 kg; RMSE, 8.82 kg; ρ = 0.865). CONCLUSIONS: Although both methods were feasible for estimating body weight at different single-slice CT levels, the linear regression model using total cross-sectional body area at the T5 level as an input variable was the most favorable method for single-slice CT analysis for estimating body weight.

Deep learning-based video-analysis of instrument motion in microvascular anastomosis training.
Taku Sugiyama, Hiroyuki Sugimori, Minghui Tang, Yasuhiro Ito, Masayuki Gekka, Haruto Uchino, Masaki Ito, Katsuhiko Ogasawara, Miki Fujimura
Acta neurochirurgica, 166, 1, 6, 6, 12 Jan. 2024, [Peer-reviewed], [Lead author], [International Magazine]
English, Scientific journal, PURPOSE: Attaining sufficient microsurgical skills is paramount for neurosurgical trainees. Kinematic analysis of surgical instruments using video offers the potential for an objective assessment of microsurgical proficiency, thereby enhancing surgical training and patient safety. The purposes of this study were to develop a deep-learning-based automated instrument tip-detection algorithm, and to validate its performance in microvascular anastomosis training. METHODS: An automated instrument tip-tracking algorithm was developed and trained using YOLOv2, based on clinical microsurgical videos and microvascular anastomosis practice videos. With this model, we measured motion economy (procedural time and path distance) and motion smoothness (normalized jerk index) during the task of suturing artificial blood vessels for end-to-side anastomosis. These parameters were validated using traditional criteria-based rating scales and were compared across surgeons with varying microsurgical experience (novice, intermediate, and expert). The suturing task was deconstructed into four distinct phases, and parameters within each phase were compared between novice and expert surgeons. RESULTS: The high accuracy of the developed model was indicated by a mean Dice similarity coefficient of 0.87. Deep learning-based parameters (procedural time, path distance, and normalized jerk index) exhibited correlations with traditional criteria-based rating scales and surgeons' years of experience. Experts completed the suturing task faster than novices. The total path distance for the right (dominant) side instrument movement was shorter for experts compared to novices. However, for the left (non-dominant) side, differences between the two groups were observed only in specific phases. The normalized jerk index for both the right and left sides was significantly lower in the expert than in the novice groups, and receiver operating characteristic analysis showed strong discriminative ability. CONCLUSION: The deep learning-based kinematic analytic approach for surgical instruments proves beneficial in assessing performance in microvascular anastomosis. Moreover, this methodology can be adapted for use in clinical settings.

深層学習を用いた心尖部四腔像からの右室駆出率の推定
村山 迪史, 加賀 早苗, 向井 葵, 永井 優衣, 杉森 博行, 吉村 高明, 島 秀起, 常田 慧徳, 西野 久雄, 中村 順一, 佐藤 隆博, 辻野 一三
超音波検査技術抄録集, 49, S172, S172, 一般社団法人 日本超音波検査学会, 2024
Japanese

Magnetic Resonance Water Tracer Imaging Using 17 O-Labeled Water.
Hiroyuki Kameda, Naoya Kinota, Daisuke Kato, Takaaki Fujii, Taisuke Harada, Yuji Komaki, Hiroyuki Sugimori, Tomohiro Onodera, Moyoko Tomiyasu, Takayuki Obata, Kohsuke Kudo
Investigative radiology, 59, 1, 92, 103, 01 Jan. 2024, [Peer-reviewed], [International Magazine]
English, Scientific journal, Magnetic resonance imaging (MRI) is a crucial imaging technique for visualizing water in living organisms. Besides proton MRI, which is widely available and enables direct visualization of intrinsic water distribution and dynamics in various environments, MR-WTI (MR water tracer imaging) using 17 O-labeled water has been developed, benefiting from the many advancements in MRI software and hardware that have substantially improved the signal-to-noise ratio and made possible faster imaging. This cutting-edge technique allows the generation of novel and valuable images for clinical use. This review elucidates the studies related to MRI water tracer techniques centered around 17 O-labeled water, explaining the fundamental principles of imaging and providing clinical application examples. Anticipating continued progress in studies involving isotope-labeled water, this review is expected to contribute to elucidating the pathophysiology of various diseases related to water dynamics abnormalities and establishing novel imaging diagnostic methods for associated diseases.

Energy spectrum measurement of scattered X-rays during IVR procedure.
Joma Oikawa, Jun Sakai, Yusuke Fujiwara, Kota Tsurusawa, Daisuke Shimao, Hiroyuki Date, Hiroyuki Sugimori
Radiation protection dosimetry, 30 Nov. 2023, [Peer-reviewed], [Corresponding author], [International Magazine]
English, Scientific journal, With the increase of the number of interventional radiology (IVR) procedures, the occupational exposure of operators and medical staff has attracted keen attention. The energy of scattered radiation in medical clinical sites is important for estimating the biological effects of occupational exposure. Recent years have seen many reports on the dose of scattered radiation by IVR, but few on the energy spectrum. In this study, the energy spectrum of scattered X-rays was measured by using a cadmium telluride (CdTe) semiconductor detector during IVR on several neurosurgical and cardiovascular cases. The cumulated spectra in each case were compared. The spectra showed little changes among neurosurgical cases and relatively large changes among cardiovascular cases. This was assumed to be due to the change of X-ray tube voltage and tube angle was larger in cardiovascular cases. The resulting energy spectra will be essential for the assessment of detailed biological effects of occupational exposure.

The montage method improves the classification of suspected acute ischemic stroke using the convolution neural network and brain MRI.
Daisuke Oura, Masayuki Gekka, Hiroyuki Sugimori
Radiological physics and technology, 07 Nov. 2023, [Peer-reviewed], [Corresponding author], [Domestic magazines]
English, Scientific journal, This study investigated the usefulness of the montage method that combines four different magnetic resonance images into one images for automatic acute ischemic stroke (AIS) diagnosis with deep learning method. The montage image was consisted from diffusion weighted image (DWI), fluid attenuated inversion recovery (FLAIR), arterial spin labeling (ASL), and apparent diffusion coefficient (ASL). The montage method was compared with pseudo color map (pCM) which was consisted from FLAIR, ASL and ADC. 473 AIS patients were classified into four categories: mechanical thrombectomy, conservative therapy, hemorrhage, and other diseases. The results showed that the montage image significantly outperformed pCM in terms of accuracy (montage image = 0.76 ± 0.01, pCM = 0.54 ± 0.05) and the area under the curve (AUC) (montage image = 0.94 ± 0.01, pCM = 0.76 ± 0.01). This study demonstrates the usefulness of the montage method and its potential for overcoming the limitations of pCM.

Development of a Mammography Calcification Detection Algorithm Using Deep Learning with Resolution-Preserved Image Patch Division
Miu Sakaida, Takaaki Yoshimura, Minghui Tang, Shota Ichikawa, Hiroyuki Sugimori
Algorithms, 16, 10, 483, 483, MDPI AG, 18 Oct. 2023, [Peer-reviewed], [Corresponding author]
Scientific journal, Convolutional neural networks (CNNs) in deep learning have input pixel limitations, which leads to lost information regarding microcalcification when mammography images are compressed. Segmenting images into patches retains the original resolution when inputting them into the CNN and allows for identifying the location of calcification. This study aimed to develop a mammographic calcification detection method using deep learning by classifying the presence of calcification in the breast. Using publicly available data, 212 mammograms from 81 women were segmented into 224 × 224-pixel patches, producing 15,049 patches. These were visually classified for calcification and divided into five subsets for training and evaluation using fivefold cross-validation, ensuring image consistency. ResNet18, ResNet50, and ResNet101 were used for training, each of which created a two-class calcification classifier. The ResNet18 classifier achieved an overall accuracy of 96.0%, mammogram accuracy of 95.8%, an area under the curve (AUC) of 0.96, and a processing time of 0.07 s. The results of ResNet50 indicated 96.4% overall accuracy, 96.3% mammogram accuracy, an AUC of 0.96, and a processing time of 0.14 s. The results of ResNet101 indicated 96.3% overall accuracy, 96.1% mammogram accuracy, an AUC of 0.96, and a processing time of 0.20 s. This developed method offers quick, accurate calcification classification and efficient visualization of calcification locations.

Automated detection of internal fruit rot in Hass avocado via deep learning-based semantic segmentation of X-ray images
Takahiro Matsui, Hiroyuki Sugimori, Shige Koseki, Kento Koyama
Postharvest Biology and Technology, 203, Sep. 2023, [Peer-reviewed]
Scientific journal

Estimation of Left and Right Ventricular Ejection Fractions from cine-MRI Using 3D-CNN
Soichiro Inomata, Takaaki Yoshimura, Minghui Tang, Shota Ichikawa, Hiroyuki Sugimori
Sensors, 23, 14, 6580, 6580, MDPI AG, 21 Jul. 2023, [Peer-reviewed], [Corresponding author]
Scientific journal, Cardiac function indices must be calculated using tracing from short-axis images in cine-MRI. A 3D-CNN (convolutional neural network) that adds time series information to images can estimate cardiac function indices without tracing using images with known values and cardiac cycles as the input. Since the short-axis image depicts the left and right ventricles, it is unclear which motion feature is captured. This study aims to estimate the indices by learning the short-axis images and the known left and right ventricular ejection fractions and to confirm the accuracy and whether each index is captured as a feature. A total of 100 patients with publicly available short-axis cine images were used. The dataset was divided into training:test = 8:2, and a regression model was built by training with the 3D-ResNet50. Accuracy was assessed using a five-fold cross-validation. The correlation coefficient, MAE (mean absolute error), and RMSE (root mean squared error) were determined as indices of accuracy evaluation. The mean correlation coefficient of the left ventricular ejection fraction was 0.80, MAE was 9.41, and RMSE was 12.26. The mean correlation coefficient of the right ventricular ejection fraction was 0.56, MAE was 11.35, and RMSE was 14.95. The correlation coefficient was considerably higher for the left ventricular ejection fraction. Regression modeling using the 3D-CNN indicated that the left ventricular ejection fraction was estimated more accurately, and left ventricular systolic function was captured as a feature.

Non-Invasive Estimation of Gleason Score by Semantic Segmentation and Regression Tasks Using a Three-Dimensional Convolutional Neural Network
Takaaki Yoshimura, Keisuke Manabe, Hiroyuki Sugimori
Applied Sciences, 13, 14, 8028, 8028, MDPI AG, 09 Jul. 2023, [Peer-reviewed], [Corresponding author]
Scientific journal, The Gleason score (GS) is essential in categorizing prostate cancer risk using biopsy. The aim of this study was to propose a two-class GS classification (< and ≥GS 7) methodology using a three-dimensional convolutional neural network with semantic segmentation to predict GS non-invasively using multiparametric magnetic resonance images (MRIs). Four training datasets of T2-weighted images and apparent diffusion coefficient maps with and without semantic segmentation were used as test images. All images and lesion information were selected from a training cohort of the Society of Photographic Instrumentation Engineers, the American Association of Physicists in Medicine, and the National Cancer Institute (SPIE–AAPM–NCI) PROSTATEx Challenge dataset. Precision, recall, overall accuracy and area under the receiver operating characteristics curve (AUROC) were calculated from this dataset, which comprises publicly available prostate MRIs. Our data revealed that the GS ≥ 7 precision (0.73 ± 0.13) and GS < 7 recall (0.82 ± 0.06) were significantly higher using semantic segmentation (p < 0.05). Moreover, the AUROC in segmentation volume was higher than that in normal volume (ADCmap: 0.70 ± 0.05 and 0.69 ± 0.08, and T2WI: 0.71 ± 0.07 and 0.63 ± 0.08, respectively). However, there were no significant differences in overall accuracy between the segmentation and normal volume. This study generated a diagnostic method for non-invasive GS estimation from MRIs.

Tissue Acceleration as a Novel Metric for Surgical Performance During Carotid Endarterectomy.
Taku Sugiyama, Masaki Ito, Hiroyuki Sugimori, Minghui Tang, Toshitaka Nakamura, Katsuhiko Ogasawara, Hitoshi Matsuzawa, Naoki Nakayama, Sanju Lama, Garnette R Sutherland, Miki Fujimura
Operative neurosurgery (Hagerstown, Md.), 04 Jul. 2023, [Peer-reviewed], [International Magazine]
English, Scientific journal, BACKGROUND AND OBJECTIVES: Gentle tissue handling to avoid excessive motion of affected fragile vessels during surgical dissection is essential for both surgeon proficiency and patient safety during carotid endarterectomy (CEA). However, a void remains in the quantification of these aspects during surgery. The video-based measurement of tissue acceleration is presented as a novel metric for the objective assessment of surgical performance. This study aimed to evaluate whether such metrics correlate with both surgeons' skill proficiency and adverse events during CEA. METHODS: In a retrospective study including 117 patients who underwent CEA, acceleration of the carotid artery was measured during exposure through a video-based analysis. Tissue acceleration values and threshold violation error frequencies were analyzed and compared among the surgeon groups with different surgical experience (3 groups: novice, intermediate, and expert). Multiple patient-related variables, surgeon groups, and video-based surgical performance parameters were compared between the patients with and without adverse events during CEA. RESULTS: Eleven patients (9.4%) experienced adverse events after CEA, and the rate of adverse events significantly correlated with the surgeon group. The mean maximum tissue acceleration and number of errors during surgical tasks significantly decreased from novice, to intermediate, to expert surgeons, and stepwise discriminant analysis showed that the combined use of surgical performance factors could accurately discriminate between surgeon groups. The multivariate logistic regression analysis revealed that the number of errors and vulnerable carotid plaques were associated with adverse events. CONCLUSION: Tissue acceleration profiles can be a novel metric for the objective assessment of surgical performance and the prediction of adverse events during surgery. Thus, this concept can be introduced into futuristic computer-aided surgeries for both surgical education and patient safety.

Deep learning-based body weight from scout images can be an alternative to actual body weight in CT radiation dose management.
Shota Ichikawa, Hideki Itadani, Hiroyuki Sugimori
Journal of applied clinical medical physics, 24, 8, e14080, 19 Jun. 2023, [Peer-reviewed], [Corresponding author], [International Magazine]
English, Scientific journal, PURPOSE: Accurate body weight measurement is essential to promote computed tomography (CT) dose optimization; however, body weight cannot always be measured prior to CT examination, especially in the emergency setting. The aim of this study was to investigate whether deep learning-based body weight from chest CT scout images can be an alternative to actual body weight in CT radiation dose management. METHODS: Chest CT scout images and diagnostic images acquired for medical checkups were collected from 3601 patients. A deep learning model was developed to predict body weight from scout images. The correlation between actual and predicted body weight was analyzed. To validate the use of predicted body weight in radiation dose management, the volume CT dose index (CTDIvol ) and the dose-length product (DLP) were compared between the body weight subgroups based on actual and predicted body weight. Surrogate size-specific dose estimates (SSDEs) acquired from actual and predicted body weight were compared to the reference standard. RESULTS: The median actual and predicted body weight were 64.1 (interquartile range: 56.5-72.4) and 64.0 (56.3-72.2) kg, respectively. There was a strong correlation between actual and predicted body weight (ρ = 0.892, p < 0.001). The CTDIvol and DLP of the body weight subgroups were similar based on actual and predicted body weight (p < 0.001). Both surrogate SSDEs based on actual and predicted body weight were not significantly different from the reference standard (p = 0.447 and 0.410, respectively). CONCLUSION: Predicted body weight can be an alternative to actual body weight in managing dose metrics and simplifying SSDE calculation. Our proposed method can be useful for CT radiation dose management in adult patients with unknown body weight.

Predicting Mechanical Thrombectomy Outcome and Time Limit through ADC Value Analysis: A Comprehensive Clinical and Simulation Study Using Machine Learning
Daisuke Oura, Soichiro Takamiya, Riku Ihara, Yoshimasa Niiya, Hiroyuki Sugimori
Diagnostics, Jun. 2023, [Peer-reviewed], [Corresponding author]
Scientific journal

Development of Chest X-ray Image Evaluation Software Using the Deep Learning Techniques
Kousuke Usui, Takaaki Yoshimura, Shota Ichikawa, Hiroyuki Sugimori
Applied Sciences, 13, 11, 6695, 6695, MDPI AG, May 2023, [Peer-reviewed], [Corresponding author]
Scientific journal, Although the widespread use of digital imaging has enabled real-time image display, images in chest X-ray examinations can be confirmed by the radiologist’s eyes. Considering the development of deep learning (DL) technology, its application will make it possible to immediately determine the need for a retake, which is expected to further improve examination throughput. In this study, we developed software for evaluating chest X-ray images to determine whether a repeat radiographic examination is necessary, based on the combined application of DL technologies, and evaluated its accuracy. The target population was 4809 chest images from a public database. Three classification models (CLMs) for lung field defects, obstacle shadows, and the location of obstacle shadows and a semantic segmentation model (SSM) for the lung field regions were developed using a fivefold cross validation. The CLM was evaluated using the overall accuracy in the confusion matrix, the SSM was evaluated using the mean intersection over union (mIoU), and the DL technology-combined software was evaluated using the total response time on this software (RT) per image for each model. The results of each CLM with respect to lung field defects, obstacle shadows, and obstacle shadow location were 89.8%, 91.7%, and 91.2%, respectively. The mIoU of the SSM was 0.920, and the software RT was 3.64 × 10−2 s. These results indicate that the software can immediately and accurately determine whether a chest image needs to be re-scanned.

Rapid and Reliable Steatosis Rat Model Shrsp5-Dmcr for Cold Storage Experiment.
Moto Fukai, Hiroyuki Sugimori, Sodai Sakamoto, Kengo Shibata, Hiroyuki Kameda, Takahisa Ishikawa, Norio Kawamura, Masato Fujiyoshi, Sunao Fujiyoshi, Kohsuke Kudo, Tsuyoshi Shimamura, Akinobu Taketomi
Transplantation proceedings, 55, 4, 1032, 1035, 10 Apr. 2023, [Peer-reviewed], [International Magazine]
English, Scientific journal, Interventions for liver grafts with moderate macrovesicular steatosis have been important in enlarging donor pools. Here, we tested a high-fat and cholesterol (HFC) diet to create a steatosis model for cold hepatic preservation and reperfusion experiments. The aim of the present study was to assess the steatosis model's reliability and to show the resulting graft's quality for cold preservation and reperfusion experiment. Male SHRSP5-Dmcr rats were raised with an HFC diet for up to 2 weeks. The fat content was evaluated using magnetic resonance imaging (MRI) proton density fat fraction (PDFF). The nonalcoholic fatty liver disease activity score (NAS) was evaluated after excision. Steatosis created by 2 weeks of HFC diet was subjected to 24-hour cold storage in the University of Wisconsin and the original test solution (new sol.). Grafts were applied to isolated perfused rat livers for simulating reperfusion. The NAS were 2.2 (HFC 5 days), 3.3 (HFC 1 week), and 5.0 (HFC 2 weeks). Ballooning and fibrosis were not observed in any group. An MRI-PDFF showed 0.2 (HFC 0 days), 12.0 (HFC 1 week), and 18.9 (HFC 2 weeks). The NAS and MRI-PDFF values correlated. Many indices in the isolated perfused rat liver experiment tended to improve in the new sol. group but were insufficient. Although the new sol. failed to be effective, it acted at multiple sites under difficult conditions. In conclusion, the HFC diet for 2 weeks in SHRSP5-Dmcr rats, together with MRI-PDFF evaluation, is a reliable method for creating simple steatosis and provides good-quality cold preservation and reperfusion experiments.

Acquisition time reduction in pediatric 99m Tc-DMSA planar imaging using deep learning.
Shota Ichikawa, Hiroyuki Sugimori, Koki Ichijiri, Takaaki Yoshimura, Akio Nagaki
Journal of applied clinical medical physics, 24, 6, e13978, 05 Apr. 2023, [Peer-reviewed], [Corresponding author], [International Magazine]
English, Scientific journal, PURPOSE: Given the potential risk of motion artifacts, acquisition time reduction is desirable in pediatric 99m Tc-dimercaptosuccinic acid (DMSA) scintigraphy. The aim of this study was to evaluate the performance of predicted full-acquisition-time images from short-acquisition-time pediatric 99m Tc-DMSA planar images with only 1/5th acquisition time using deep learning in terms of image quality and quantitative renal uptake measurement accuracy. METHODS: One hundred and fifty-five cases that underwent pediatric 99m Tc-DMSA planar imaging as dynamic data for 10 min were retrospectively collected for the development of three deep learning models (DnCNN, Win5RB, and ResUnet), and the generation of full-time images from short-time images. We used the normalized mean squared error (NMSE), peak signal-to-noise ratio (PSNR), and structural similarity index metrics (SSIM) to evaluate the accuracy of the predicted full-time images. In addition, the renal uptake of 99m Tc-DMSA was calculated, and the difference in renal uptake from the reference full-time images was assessed using scatter plots with Pearson correlation and Bland-Altman plots. RESULTS: The predicted full-time images from the deep learning models showed a significant improvement in image quality compared to the short-time images with respect to the reference full-time images. In particular, the predicted full-time images obtained by ResUnet showed the lowest NMSE (0.4 [0.4-0.5] %) and the highest PSNR (55.4 [54.7-56.1] dB) and SSIM (0.997 [0.995-0.997]). For renal uptake, an extremely high correlation was achieved in all short-time and three predicted full-time images (R2  > 0.999 for all). The Bland-Altman plots showed the lowest bias (-0.10) of renal uptake in ResUnet, while short-time images showed the lowest variance (95% confidence interval: -0.14, 0.45) of renal uptake. CONCLUSIONS: Our proposed method is capable of producing images that are comparable to the original full-acquisition-time images, allowing for a reduction of acquisition time/injected dose in pediatric 99m Tc-DMSA planar imaging.

Quality Assurance of Chest X-ray Images with a Combination of Deep Learning Methods
Daisuke Oura, Shinpe Sato, Yuto Honma, Shiho Kuwajima, Hiroyuki Sugimori
Applied Sciences, Feb. 2023, [Peer-reviewed], [Corresponding author]
Scientific journal

Prediction of body weight from chest radiographs using deep learning with a convolutional neural network.
Shota Ichikawa, Hideki Itadani, Hiroyuki Sugimori
Radiological physics and technology, 16, 1, 127, 134, 13 Jan. 2023, [Peer-reviewed], [Corresponding author], [Domestic magazines]
English, Scientific journal, Accurate body weights are not necessarily available in routine clinical practice. This study aimed to investigate whether body weight can be predicted from chest radiographs using deep learning. Deep-learning models with a convolutional neural network (CNN) were trained and tested using chest radiographs from 85,849 patients. The CNN models were evaluated by calculating the mean absolute error (MAE) and Spearman's rank correlation coefficient (ρ). The MAEs of the CNN models were 2.63 kg and 3.35 kg for female and male patients, respectively. The predicted body weight was significantly correlated with the actual body weight (ρ = 0.917, p < 0.001 for females; ρ = 0.915, p < 0.001 for males). The body weight was predicted using chest radiographs by applying deep learning. Our method is potentially useful for radiation dose management, determination of the contrast medium dose, and estimation of the specific absorption rate in patients with unknown body weights.

Age Estimation from Brain Magnetic Resonance Images Using Deep Learning Techniques in Extensive Age Range
Kousuke Usui, Takaaki Yoshimura, Minghui Tang, Hiroyuki Sugimori
Applied Sciences, 13, 3, 1753, 1753, MDPI AG, Jan. 2023, [Peer-reviewed], [Corresponding author]
Scientific journal, Estimation of human age is important in the fields of forensic medicine and the detection of neurodegenerative diseases of the brain. Particularly, the age estimation methods using brain magnetic resonance (MR) images are greatly significant because these methods not only are noninvasive but also do not lead to radiation exposure. Although several age estimation methods using brain MR images have already been investigated using deep learning, there are no reports involving younger subjects such as children. This study investigated the age estimation method using T1-weighted (sagittal plane) two-dimensional brain MR imaging (MRI) of 1000 subjects aged 5–79 (31.64 ± 18.04) years. This method uses a regression model based on ResNet-50, which estimates the chronological age (CA) of unknown brain MR images by training brain MR images corresponding to the CA. The correlation coefficient, coefficient of determination, mean absolute error, and root mean squared error were used as the evaluation indices of this model, and the results were 0.9643, 0.9299, 5.251, and 6.422, respectively. The present study showed the same degree of correlation as those of related studies, demonstrating that age estimation can be performed for a wide range of ages with higher estimation accuracy.

Prostatic urinary tract visualization with super-resolution deep learning models.
Takaaki Yoshimura, Kentaro Nishioka, Takayuki Hashimoto, Takashi Mori, Shoki Kogame, Kazuya Seki, Hiroyuki Sugimori, Hiroko Yamashina, Yusuke Nomura, Fumi Kato, Kohsuke Kudo, Shinichi Shimizu, Hidefumi Aoyama
PloS one, 18, 1, e0280076, 2023, [Peer-reviewed], [International Magazine]
English, Scientific journal, In urethra-sparing radiation therapy, prostatic urinary tract visualization is important in decreasing the urinary side effect. A methodology has been developed to visualize the prostatic urinary tract using post-urination magnetic resonance imaging (PU-MRI) without a urethral catheter. This study investigated whether the combination of PU-MRI and super-resolution (SR) deep learning models improves the visibility of the prostatic urinary tract. We enrolled 30 patients who had previously undergone real-time-image-gated spot scanning proton therapy by insertion of fiducial markers. PU-MRI was performed using a non-contrast high-resolution two-dimensional T2-weighted turbo spin-echo imaging sequence. Four different SR deep learning models were used: the enhanced deep SR network (EDSR), widely activated SR network (WDSR), SR generative adversarial network (SRGAN), and residual dense network (RDN). The complex wavelet structural similarity index measure (CW-SSIM) was used to quantitatively assess the performance of the proposed SR images compared to PU-MRI. Two radiation oncologists used a 1-to-5 scale to subjectively evaluate the visibility of the prostatic urinary tract. Cohen's weighted kappa (k) was used as a measure of agreement of inter-operator reliability. The mean CW-SSIM in EDSR, WDSR, SRGAN, and RDN was 99.86%, 99.89%, 99.30%, and 99.67%, respectively. The mean prostatic urinary tract visibility scores of the radiation oncologists were 3.70 and 3.53 for PU-MRI (k = 0.93), 3.67 and 2.70 for EDSR (k = 0.89), 3.70 and 2.73 for WDSR (k = 0.88), 3.67 and 2.73 for SRGAN (k = 0.88), and 4.37 and 3.73 for RDN (k = 0.93), respectively. The results suggest that SR images using RDN are similar to the original images, and the SR deep learning models subjectively improve the visibility of the prostatic urinary tract.

Predicting the response to pulmonary vasodilator therapy in systemic sclerosis with pulmonary hypertension by using quantitative chest CT.
Keita Ninagawa, Masaru Kato, Yasuka Kikuchi, Hiroyuki Sugimori, Michihito Kono, Yuichiro Fujieda, Ichizo Tsujino, Tatsuya Atsumi
Modern rheumatology, 33, 4, 758, 767, 02 Sep. 2022, [Peer-reviewed], [International Magazine]
English, Scientific journal, OBJECTIVE: Systemic sclerosis (SSc) is associated with pulmonary vascular disease (PVD) and interstitial lung disease (ILD), making it difficult to differentiate pulmonary arterial hypertension and pulmonary hypertension (PH) due to lung diseases and/or hypoxia and to decide treatments. We aimed to predict the response to pulmonary vasodilators in patients with SSc and PH. METHODS: 84 SSc patients were included with 47 having PH. Chest CT was evaluated using a software to calculate abnormal lung volume (ALV). To define the response to vasodilators, Δ mean pulmonary artery pressure (mPAP)/basal mPAP was used (cut-off value: 10%). The predictive value was evaluated by using receiver operating characteristic curve. RESULTS: The mean (±SD) value of ALV was 26.8 (±32.2) %. A weak correlation was observed between ALV and forced vital capacity (FVC) (R = -0.46). The predictive value of ALV (area under curve; AUC = 0.74) was superior to that of FVC (AUC = 0.62) for the response to vasodilators. No hemodynamic parameters differed between patients with high and low ALV, whereas survival was worse in high ALV. CONCLUSION: Quantitative chest CT well predicted the response to vasodilators in patients with SSc and PH. Our results suggest its utility in differentiating the dominance of PVD or ILD.

Simultaneous depiction of clot and MRA using 1 min phase contrast angiography in acute ischemic patients.
Daisuke Oura, Masayuki Gekka, Yutaka Morishima, Yoshimasa Niiya, Riku Ihara, Thubasa Ebina, Hiroyuki Sugimori
Magnetic resonance imaging, 14 Aug. 2022, [Peer-reviewed], [Last author], [International Magazine]
English, Scientific journal, [Background and Purpose] Clot location and range predict clinical outcomes for acute ischemic stroke (AIS). We developed a new technique for visualizing occlusion clots, namely, the DEpicting blood clot and MRA using Phase contrast angiography with Image Calculation for Thrombectomy (DEPICT) method. The purpose of this study was to assess the clinical usefulness of DEPICT. [Methods] We used DEPICT in 36 AIS patients to obtain MRA and black blood images with 1-min phase contrast angiography (PCA). We created the black blood images by subtracting the MRA from the T1WI using the source image of PCA. We evaluated the motion artifact, detectability of clot, and precision in location and range compared these to that of susceptibility vessel sign in T2*WI and measured contrast ration (CR) of clot between the cistern and brain tissue. Motion artifact was visually evaluated using a 3-point scale. Detectability and precision of the location and range of occlusion clots were assessed by comparison with findings from digital subtraction angiography (DSA). Gwet's AC1 and kappa statistic were used to assess inter-observer agreement. [Results] DEPICT showed significant robustness for motion artifact compared with T2*WI (p = 0.0026, Wilcoxon signed-rank test). DEPICT showed 100% detectability for the clot. Further, DEPICT showed higher Gwet's AC1 and kappa statistic values with DSA than T2*WI. CR demonstrated a positive value. [Conclusions] DEPICT technique based on 1-min PCA offers both MRA and black blood T1W images that can be used to accurately evaluate both location and range of the clot.

Toward automatic reformation at the orbitomeatal line in head computed tomography using object detection algorithm.
Shota Ichikawa, Hideki Itadani, Hiroyuki Sugimori
Physical and engineering sciences in medicine, 45, 3, 835, 845, 06 Jul. 2022, [Peer-reviewed], [Corresponding author], [International Magazine]
English, Scientific journal, Consistent cross-sectional imaging is desirable to accurately detect lesions and facilitate follow-up in head computed tomography (CT). However, manual reformation causes image variations among technologists and requires additional time. We therefore developed a system that reformats head CT images at the orbitomeatal (OM) line and evaluated the system performance using real-world clinical data. Retrospective data were obtained for 681 consecutive patients who underwent non-contrast head CT. The datasets were randomly divided into one of three sets for training, validation, or testing. Four landmarks (bilateral eyes and external auditory canal) were detected with the trained You Look Only Once (YOLO)v5 model, and the head CT images were reformatted at the OM line. The precision, recall, and mean average precision at the intersection over union threshold of 0.5 were computed in the validation sets. The reformation quality in testing sets was evaluated by three radiological technologists on a qualitative 4-point scale. The precision, recall, and mean average precision of the trained YOLOv5 model for all categories were 0.688, 0.949, and 0.827, respectively. In our environment, the mean implementation time was 23.5 ± 2.4 s for each case. The qualitative evaluation in the testing sets showed that post-processed images of automatic reformation had clinically useful quality with scores 3 and 4 in 86.8%, 91.2%, and 94.1% for observers 1, 2, and 3, respectively. Our system demonstrated acceptable quality in reformatting the head CT images at the OM line using an object detection algorithm and was highly time efficient.

Establishment of a New Qualitative Evaluation Method for Articular Cartilage by Dynamic T2w MRI Using a Novel Contrast Medium as a Water Tracer
Yoshiaki Hosokawa, Tomohiro Onodera, Kentaro Homan, Jun Yamaguchi, Kohsuke Kudo, Hiroyuki Kameda, Hiroyuki Sugimori, Norimasa Iwasaki
CARTILAGE, 13, 3, Jul. 2022, [Peer-reviewed]
English, Scientific journal

Evaluation of Image Classification for Quantifying Mitochondrial Morphology using Deep Learning.
Kaori Tsutsumi, Keima Tokunaga, Shun Saito, Tatsuya Sasase, Hiroyuki Sugimori
Endocrine, metabolic & immune disorders drug targets, 01 Jul. 2022, [Peer-reviewed], [Corresponding author], [International Magazine]
English, Scientific journal, BACKGROUND: Mitochondrial morphology reversibly changes between division and fusion. As these changes (mitochondrial dynamics) reflect the cellular condition, they are one of the simplest indicators of cell state and predictors of cell fate. However, it is currently difficult to classify them using a simple and objective method. OBJECTIVE: The present study aimed to evaluate mitochondrial morphology using Deep Learning (DL) technique. METHODS: Mitochondrial images stained by MitoTracker were acquired from HeLa and MC3T3-E1 cells using fluorescent microscopy and visually classified into four groups based on fission or fusion. The intra- and inter-rater reliabilities for visual classification were excellent [ (ICC(1,3), 0.961 for rater 1; and 0.981 for rater 2) and ICC(1,3), respectively]. The images were divided into test and train images, and a 50-layer ResNet CNN architecture (ResNet-50) using MATLAB software was used to train the images. The datasets were trained five times based on five-fold cross-validation. RESULT: The mean of the overall accuracy for classifying mitochondrial morphology was 0.73±0.10 in HeLa. For the classification of mixed images containing two types of cell lines, the overall accuracy using mixed images of both cell lines for training was higher (0.74±0.01) than that using different cell lines for training. CONCLUSION: We developed a classifier to categorize mitochondrial morphology using DL.

Medical Radiation Exposure Reduction in PET via Super-Resolution Deep Learning Model.
Takaaki Yoshimura, Atsushi Hasegawa, Shoki Kogame, Keiichi Magota, Rina Kimura, Shiro Watanabe, Kenji Hirata, Hiroyuki Sugimori
Diagnostics (Basel, Switzerland), 12, 4, 31 Mar. 2022, [Peer-reviewed], [Corresponding author], [International Magazine]
English, Scientific journal, In positron emission tomography (PET) imaging, image quality correlates with the injected [18F]-fluorodeoxyglucose (FDG) dose and acquisition time. If image quality improves from short-acquisition PET images via the super-resolution (SR) deep learning technique, it is possible to reduce the injected FDG dose. Therefore, the aim of this study was to clarify whether the SR deep learning technique could improve the image quality of the 50%-acquisition-time image to the level of that of the 100%-acquisition-time image. One-hundred-and-eight adult patients were enrolled in this retrospective observational study. The supervised data were divided into nine subsets for nested cross-validation. The mean peak signal-to-noise ratio and structural similarity in the SR-PET image were 31.3 dB and 0.931, respectively. The mean opinion scores of the 50% PET image, SR-PET image, and 100% PET image were 3.41, 3.96, and 4.23 for the lung level, 3.31, 3.80, and 4.27 for the liver level, and 3.08, 3.67, and 3.94 for the bowel level, respectively. Thus, the SR-PET image was more similar to the 100% PET image and subjectively improved the image quality, as compared to the 50% PET image. The use of the SR deep-learning technique can reduce the injected FDG dose and thus lower radiation exposure.

Artificial intelligence for nuclear medicine in oncology.
Kenji Hirata, Hiroyuki Sugimori, Noriyuki Fujima, Takuya Toyonaga, Kohsuke Kudo
Annals of nuclear medicine, 36, 2, 123, 132, 14 Jan. 2022, [Peer-reviewed], [Domestic magazines]
English, Scientific journal, As in all other medical fields, artificial intelligence (AI) is increasingly being used in nuclear medicine for oncology. There are many articles that discuss AI from the viewpoint of nuclear medicine, but few focus on nuclear medicine from the viewpoint of AI. Nuclear medicine images are characterized by their low spatial resolution and high quantitativeness. It is noted that AI has been used since before the emergence of deep learning. AI can be divided into three categories by its purpose: (1) assisted interpretation, i.e., computer-aided detection (CADe) or computer-aided diagnosis (CADx). (2) Additional insight, i.e., AI provides information beyond the radiologist's eye, such as predicting genes and prognosis from images. It is also related to the field called radiomics/radiogenomics. (3) Augmented image, i.e., image generation tasks. To apply AI to practical use, harmonization between facilities and the possibility of black box explanations need to be resolved.

Quantitative magnetic resonance imaging for evaluating of the cerebrospinal fluid kinetics with 17O-labeled water tracer: A preliminary report.
Hiroyuki Sugimori, Hiroyuki Kameda, Taisuke Harada, Kinya Ishizaka, Masayoshi Kajiyama, Tasuku Kimura, Niki Udo, Masaaki Matsushima, Azusa Nagai, Masahiro Wakita, Ichiro Kusumi, Ichiro Yabe, Kohsuke Kudo
Magnetic resonance imaging, 87, 77, 85, 27 Dec. 2021, [Peer-reviewed], [Lead author], [International Magazine]
English, Scientific journal, The aim of this study was to evaluate the feasibility of kinetic analysis of cerebrospinal fluid (CSF) using 17O-labeled water tracer. Four subjects (two idiopathic normal pressure hydrocephalus (iNPH) and two possible AD dementia patients) were prospectively included. Injectable formulation of 17O-labeled water containing 10 mol% of H217O (PSO17), was intrathecally administered to the subjects with the lateral decubitus position between the 3rd and 4th lumbar vertebrae. MRI acquisitions were performed in four-time points, before PSO17 administration, 1, 8, and 24 h after PSO17 administration. The 3-dimensional fast spin echo sequence was used. After image registration for all four-time points data, polygonal regions of interest (ROIs) were set in the 14 regions to obtain the signal intensity of CSF. Each signal intensity within the ROI was converted to 17O concentration [%]. The peak concentration at one hour after administration, the slope of concentration changes after PSO17 administration [%/s], and the root mean square error (RMSE) for evaluating the performance of a fitting were calculated. There was no significant difference in peak concentration between the iNPH and AD group. The slope in the AD group (-2.25 ± 1.62 × 10-3 [%/h]) was significantly smaller than in the iNPH group (-1.21 ± 2.31 × 10-3 [%/h]), which suggests the speed of CSF clearance in the iNPH group was slower than AD group. The RMSE indicating the fit to the concentration change in the AD group (4.86 ± 4.74 × 10-3) was also significantly smaller than in the iNPH group (8.64 ± 7.56 × 10-3). The kinetic evaluation of CSF using 17O-labeled water was feasible, and this preliminary study suggests that the differentiation of iNPH and possible AD dementia can be achieved using this method.

Development of Detection and Volumetric Methods for the Triceps of the Lower Leg Using Magnetic Resonance Images with Deep Learning
Yusuke Asami, Takaaki Yoshimura, Keisuke Manabe, Tomonari Yamada, Hiroyuki Sugimori
Applied Sciences, 11, 24, 12006, 12006, MDPI AG, Dec. 2021, [Peer-reviewed], [Corresponding author]
Scientific journal, Purpose: A deep learning technique was used to analyze the triceps surae muscle. The devised interpolation method was used to determine muscle’s volume and verify the usefulness of the method. Materials and Methods: Thirty-eight T1-weighted cross-sectional magnetic resonance images of the triceps of the lower leg were divided into three classes, i.e., gastrocnemius lateralis (GL), gastrocnemius medialis (GM), and soleus (SOL), and the regions of interest (ROIs) were manually defined. The supervised images were classified as per each patient. A total of 1199 images were prepared. Six different datasets separated patient-wise were prepared for K-fold cross-validation. A network model of the DeepLabv3+ was used for training. The images generated by the created model were divided as per each patient and classified into each muscle types. The model performance and the interpolation method were evaluated by calculating the Dice similarity coefficient (DSC) and error rates of the volume of the predicted and interpolated images, respectively. Results: The mean DSCs for the predicted images were >0.81 for GM and SOL and 0.71 for GL. The mean error rates for volume were approximately 11% for GL, SOL, and total error and 23% for GL. DSCs in the interpolated images were >0.8 for all muscles. The mean error rates of volume were <10% for GL, SOL, and total error and 18% for GM. There was no significant difference between the volumes obtained from the supervised images and interpolated images. Conclusions: Using the semantic segmentation of the deep learning technique, the triceps were detected with high accuracy and the interpolation method used in this study to find the volume was useful.

Evaluation of Visualizing the Prostatic Urinary Tract in MRI With a Super Resolution Deep Learning Model for Urethra Sparing Radiotherapy
T. Yoshimura, K. Nishioka, T. Hashimoto, S. Kogame, K. Seki, H. Sugimori, H. Yamashina, F. Kato, H. Aoyama, K. Kudo, S. Shimizu
International Journal of Radiation Oncology*Biology*Physics, 111, 3, e121, e122, Elsevier BV, Nov. 2021, [Peer-reviewed]
Scientific journal

Construction of super-rapid brain MRA using oblique transverse acquisition phase contrast angiography with tilted optimized non-saturated excitation pulse.
Daisuke Oura, Riku Ihara, Eiichirou Myo, Shinpei Sato, Hiroyuki Sugimori
Magnetic resonance imaging, 85, 193, 201, 27 Oct. 2021, [Peer-reviewed], [Corresponding author], [International Magazine]
English, Scientific journal, [Background] Magnetic resonance angiography (MRA) is one of the most important sequences to estimate a cerebrovascular disease. We often encounter poor image quality due to slow arterial flow related to aging and motion artifact caused by disturbance of consciousness. We focused on phase contrast angiography (PCA) to overcome these difficulties. PCA can reduce scan time drastically by combining transverse acquisition and partial slab setting covering entire brain arteries. However, transverse acquisition in PCA has a large difference in signal intensity between proximal and distal vessels. Therefore, we apply tilted optimized non-saturated excitation (TONE) to improve image quality. [Purpose] The purpose of this study to investigate the usefulness of TONE for PCA. [Method] We estimated the efficacy of TONE in transverse acquisition PCA using measurement of signal intensity in arteries. We compared image quality among 1 min PCA with/without TONE and time-of flight (TOF)-MRA, by visual. [Result] TONE improved the signal inhomogeneity in entire brain arteries. PCA with TONE (5°-9°) demonstrated the highest image quality. [Conclusion] Oblique transverse acquisition PCA with TONE provides superior image quality compared with TOF with similar scan time. TONE improved image quality by the homogenizing signal intensity of vessels from proximal to distal in oblique transvers acquisition PCA. Our MRA can be performed in about 1 min and provides sufficient quality to estimate brain vessels.

A deep-learning method using computed tomography scout images for estimating patient body weight.
Shota Ichikawa, Misaki Hamada, Hiroyuki Sugimori
Scientific reports, 11, 1, 15627, 15627, 02 Aug. 2021, [Peer-reviewed], [Corresponding author], [International Magazine]
English, Scientific journal, Body weight is an indispensable parameter for determination of contrast medium dose, appropriate drug dosing, or management of radiation dose. However, we cannot always determine the accurate patient body weight at the time of computed tomography (CT) scanning, especially in emergency care. Time-efficient methods to estimate body weight with high accuracy before diagnostic CT scans currently do not exist. In this study, on the basis of 1831 chest and 519 abdominal CT scout images with the corresponding body weights, we developed and evaluated deep-learning models capable of automatically predicting body weight from CT scout images. In the model performance assessment, there were strong correlations between the actual and predicted body weights in both chest (ρ = 0.947, p < 0.001) and abdominal datasets (ρ = 0.869, p < 0.001). The mean absolute errors were 2.75 kg and 4.77 kg for the chest and abdominal datasets, respectively. Our proposed method with deep learning is useful for estimating body weights from CT scout images with clinically acceptable accuracy and potentially could be useful for determining the contrast medium dose and CT dose management in adult patients with unknown body weight.

A Comparative Evaluation of Computed Tomography Images for the Classification of Spirometric Severity of the Chronic Obstructive Pulmonary Disease with Deep Learning.
Hiroyuki Sugimori, Kaoruko Shimizu, Hironi Makita, Masaru Suzuki, Satoshi Konno
Diagnostics (Basel, Switzerland), 11, 6, 21 May 2021, [Peer-reviewed], [Lead author], [International Magazine]
English, Scientific journal, Recently, deep learning applications in medical imaging have been widely applied. However, whether it is sufficient to simply input the entire image or whether it is necessary to preprocess the setting of the supervised image has not been sufficiently studied. This study aimed to create a classifier trained with and without preprocessing for the Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification using CT images and to evaluate the classification accuracy of the GOLD classification by confusion matrix. According to former GOLD 0, GOLD 1, GOLD 2, and GOLD 3 or 4, eighty patients were divided into four groups (n = 20). The classification models were created by the transfer learning of the ResNet50 network architecture. The created models were evaluated by confusion matrix and AUC. Moreover, the rearranged confusion matrix for former stages 0 and ≥1 was evaluated by the same procedure. The AUCs of original and threshold images for the four-class analysis were 0.61 ± 0.13 and 0.64 ± 0.10, respectively, and the AUCs for the two classifications of former GOLD 0 and GOLD ≥ 1 were 0.64 ± 0.06 and 0.68 ± 0.12, respectively. In the two-class classification by threshold image, recall and precision were over 0.8 in GOLD ≥ 1, and in the McNemar-Bowker test, there was some symmetry. The results suggest that the preprocessed threshold image can be possibly used as a screening tool for GOLD classification without pulmonary function tests, rather than inputting the normal image into the convolutional neural network (CNN) for CT image learning.

Factors That Affect Symptoms of Injection Site Infection among Japanese Patients Who Self-Inject Insulin for Diabetes
Yuko Yoshida, Masuko Sumikawa, Hiroyuki Sugimori, rika yano
Healthcare, 9, 4, Apr. 2021, [Peer-reviewed], [International Magazine]
English, Scientific journal, In Japan, skin disinfection is typically considered necessary before an insulin injection to prevent infection at the injection site. This cross-sectional study evaluated factors that influenced symptoms of injection site infection among 238 Japanese patients who self-injected insulin for diabetes between October 2015 and January 2016. A structured questionnaire was used to collect data regarding skin disinfection practices, infection symptoms at the injection site, frequency of injections, environment at the time of injection, and hygiene habits. The majority of patients (83.2%) performed skin disinfection before the self-injection. Logistic regression analysis revealed that infection at the injection site was positively associated with skin disinfection before injection, age, and performing injections outside home. It was speculated that omitting skin disinfection before administering subcutaneous insulin injection was not the factor that affected the symptoms of injection site infection. The greatest contributor to infection symptoms was injections performed outside the home. Future studies focusing on the environment, in which patients administer insulin injections, to assess its influence on symptoms of injection site infections are warranted.

Visualizing the urethra by magnetic resonance imaging without usage of a catheter for radiotherapy of prostate cancer
Takaaki Yoshimura, Kentaro Nishioka, Takayuki Hashimoto, Taro Fujiwara, Kinya Ishizaka, Hiroyuki Sugimori, Shoki Kogame, Kazuya Seki, Hiroshi Tamura, Sodai Tanaka, Yuto Matsuo, Yasuhiro Dekura, Fumi Kato, Hidefumi Aoyama, Shinichi Shimizu
Physics and Imaging in Radiation Oncology, 18, 1, 4, Apr. 2021, [Peer-reviewed]
Scientific journal

Improvement in the Convolutional Neural Network for Computed Tomography Images
Keisuke Manabe, Yusuke Asami, Tomonari Yamada, Hiroyuki Sugimori
Applied Sciences, 11, 4, Feb. 2021, [Peer-reviewed], [Corresponding author]
Scientific journal

Classification of type of brain magnetic resonance images with deep learning technique.
Hiroyuki Sugimori, Hiroyuki Hamaguchi, Taro Fujiwara, Kinya Ishizaka
Magnetic resonance imaging, 21 Dec. 2020, [Peer-reviewed], [Lead author, Corresponding author], [International Magazine]
English, Scientific journal

Development of a Deep Learning-Based Algorithm to Detect the Distal End of a Surgical Instrument
Hiroyuki Sugimori, Taku Sugiyama, Naoki Nakayama, Akemi Yamashita, Katsuhiko Ogasawara
Applied Sciences, 10, 12, Jun. 2020, [Peer-reviewed], [Lead author]
English, Scientific journal

Development of Combination Methods for Detecting Malignant Uptakes Based on Physiological Uptake Detection Using Object Detection With PET-CT MIP Images.
Masashi Kawakami, Kenji Hirata, Sho Furuya, Kentaro Kobayashi, Hiroyuki Sugimori, Keiichi Magota, Chietsugu Katoh
Frontiers in medicine, 7, 616746, 616746, 2020, [Peer-reviewed], [Corresponding author], [International Magazine]
English, Scientific journal, Deep learning technology is now used for medical imaging. YOLOv2 is an object detection model using deep learning. Here, we applied YOLOv2 to FDG-PET images to detect the physiological uptake on the images. We also investigated the detection precision of abnormal uptake by a combined technique with YOLOv2. Using 3,500 maximum intensity projection (MIP) images of 500 cases of whole-body FDG-PET examinations, we manually drew rectangular regions of interest with the size of each physiological uptake to create a dataset. Using YOLOv2, we performed image training as transfer learning by initial weight. We evaluated YOLOv2's physiological uptake detection by determining the intersection over union (IoU), average precision (AP), mean average precision (mAP), and frames per second (FPS). We also developed a combination method for detecting abnormal uptake by subtracting the YOLOv2-detected physiological uptake. We calculated the coverage rate, false-positive rate, and false-negative rate by comparing the combination method-generated color map with the abnormal findings identified by experienced radiologists. The APs for physiological uptakes were: brain, 0.993; liver, 0.913; and bladder, 0.879. The mAP was 0.831 for all classes with the IoU threshold value 0.5. Each subset's average FPS was 31.60 ± 4.66. The combination method's coverage rate, false-positive rate, and false-negative rate for detecting abnormal uptake were 0.9205 ± 0.0312, 0.3704 ± 0.0213, and 0.1000 ± 0.0774, respectively. The physiological uptake of FDG-PET on MIP images was quickly and precisely detected using YOLOv2. The combination method, which can be utilized the characteristics of the detector by YOLOv2, detected the radiologist-identified abnormalities with a high coverage rate. The detectability and fast response would thus be useful as a diagnostic tool.

Computed diffusion-weighted imaging for differentiating synovial proliferation from joint effusion in hand arthritis
Yuki Tanaka, Motoshi Fujimori, Koichi Murakami, Hiroyuki Sugimori, Nozomi Oki, Takatoshi Aoki, Tamotsu Kamishima
Rheumatology International, 39, 12, 2111, 2118, 01 Dec. 2019, [Peer-reviewed], [International Magazine]
English, Scientific journal

Intravoxel incoherent motion MRI for discrimination of synovial proliferation in the hand arthritis: A prospective proof-of-concept study
Motoshi Fujimori, Koichi Murakami, Hiroyuki Sugimori, Yutong Lu, Kenneth Sutherland, Nozomi Oki, Takatoshi Aoki, Tamotsu Kamishima
Journal of Magnetic Resonance Imaging, 50, 4, 1199, 1206, 01 Oct. 2019, [Peer-reviewed]
Scientific journal

Automatic detection of a standard line for brain magnetic resonance imaging using deep learning
Hiroyuki Sugimori, Masashi Kawakami
Applied Sciences (Switzerland), 9, 18, 01 Sep. 2019, [Peer-reviewed], [Lead author, Corresponding author]
English, Scientific journal

Magnetic resonance imaging T1 and T2 mapping provide complementary information on the bone mineral density regarding cancellous bone strength in the femoral head of postmenopausal women with osteoarthritis
Kaori Endo, Masahiko Takahata, Hiroyuki Sugimori, Satoshi Yamada, Shigeru Tadano, Jeffrey Wang, Masahiro Todoh, Yoichi M. Ito, Daisuke Takahashi, Kohsuke Kudo, Norimasa Iwasaki
Clinical Biomechanics, 65, 13, 18, May 2019, [Peer-reviewed], [International Magazine]
English, Scientific journal

Evaluating the overall accuracy of additional learning and automatic classification system for CT images
Hiroyuki Sugimori
Applied Sciences (Switzerland), 9, 4, 17 Feb. 2019, [Peer-reviewed], [Lead author, Corresponding author]
English, Scientific journal

Reduced Myocardial Flow Reserve Is Associated with Subendocardial Infarction and Coronary Stenosis in Patients with Coronary Artery Disease: A Perfusion MRI Study
Osamu Manabe, Noriko Oyama-Manabe, Masanao Naya, Masahiko Obara, Yasuka Kikuchi, Tadao Aikawa, Yuuki Tomiyama, Hiroyuki Sugimori, Chietsugu Katoh, Nagara Tamaki, Toshihisa Anzai
Cardiovascular Imaging Asia, 3, 1, 8, 8, Asian Society of Cardiovascular Imaging, 2019
Scientific journal

Assessment of coronary flow velocity reserve in the left main trunk using phase-contrast MR imaging at 3T: Comparison with ¹⁵O-labeled water positron emission tomography
Yasuka Kikuchi, Masanao Naya, Noriko Oyama Manabe, Osamu Manabe, Hiroyuki Sugimori, Kohsuke Kudo, Fumi Kato, Tadao Aikawa, Hiroyuki Tsutsui, Nagara Tamaki, Hiroki Shirato
Magnetic Resonance in Medical Sciences, 18, 2, 134, 141, 2019, [Peer-reviewed], [Domestic magazines]
English, Scientific journal

Three-dimensional pseudo-continuous arterial spin-labeling using turbo-spin echo with pseudo-steady state readout: A comparison with other major readout methods
Suzuko Aoike, Hiroyuki Sugimori, Noriyuki Fujima, Yuriko Suzuki, Yukie Shimizu, Akira Suwa, Kinya Ishizaka, Kohsuke Kudo
Magnetic Resonance in Medical Sciences, 18, 2, 170, 177, 2019, [Peer-reviewed], [Domestic magazines]
English

Quantification of hand synovitis in rheumatoid arthritis: Arterial mask subtraction reinforced with mutual information can improve accuracy of pixel-by-pixel time–intensity curve shape analysis in dynamic MRI
Yuto Kobayashi, Tamotsu Kamishima, Hiroyuki Sugimori, Shota Ichikawa, Atsushi Noguchi, Michihito Kono, Toshitake Iiyama, Kenneth Sutherland, Tatsuya Atsumi
Journal of Magnetic Resonance Imaging, 48, 3, 687, 694, Sep. 2018, [Peer-reviewed]
Scientific journal

Classification of Computed Tomography Images in Different Slice Positions Using Deep Learning
Hiroyuki Sugimori
Journal of Healthcare Engineering, 2018, 1753480, 2018, [Peer-reviewed], [Lead author, Corresponding author]
Scientific journal

Composite assessment of power Doppler ultrasonography and MRI in rheumatoid arthritis: A pilot study of predictive value in radiographic progression after one year
Motoshi Fujimori, Tamotsu Kamishima, Masaru Kato, Yumika Seno, Kenneth Sutherland, Hiroyuki Sugimori, Mutsumi Nishida, Tatsuya Atsumi
British Journal of Radiology, 91, 1086, 20170748, 2018, [Peer-reviewed]
Scientific journal

Acceleration of ASL-based time-resolved MR angiography by acquisition of control and labeled images in the same shot (ACTRESS)
Yuriko Suzuki, Noriyuki Fujima, Tetsuo Ogino, James Alastair Meakin, Akira Suwa, Hiroyuki Sugimori, Marc Van Cauteren, Matthias J.P. van Osch
Magnetic Resonance in Medicine, 79, 1, 224, 233, Jan. 2018, [Peer-reviewed]
English, Scientific journal

Fast acceleration of ASL-based time-resolved magnetic resonance angiography by acquisition of control and labeled images in the same shot (fast ACTRESS): An optimization study
Hiroyuki Sugimori, Noriyuki Fujima, Yuriko Suzuki, Hiroyuki Hamaguchi, Kinya Ishizaka, Kohsuke Kudo
Magnetic Resonance Imaging, 43, 136, 143, Nov. 2017, [Peer-reviewed], [Lead author]
English, Scientific journal

Pixel-by-pixel arterial spin labeling blood flow pattern variation analysis for discrimination of rheumatoid synovitis: A pilot study
Taro Sakashita, Tamotsu Kamishima, Hiroyuki Sugimori, Minghui Tang, Atsushi Noguchi, Michihito Kono, Kenneth Sutherland, Tatsuya Atsumi
Magnetic Resonance in Medical Sciences, 16, 1, 78, 83, 2017, [Peer-reviewed]
English

Effect of respiratory and cardiac gating on the major diffusion-imaging metrics
Hiroyuki Hamaguchi, Khin Khin Tha, Hiroyuki Sugimori, Mitsuhiro Nakanishi, Shin Nakagawa, Taro Fujiwara, Hirokazu Yoshida, Sayaka Takamori, Hiroki Shirato
Neuroradiology Journal, 29, 4, 254, 259, 01 Aug. 2016, [Peer-reviewed], [International Magazine]
English, Scientific journal

Accurate quantitative assessment of synovitis in rheumatoid arthritis using pixel-by-pixel, time-intensity curve shape analysis
Taro Sakashita, Tamotsu Kamishima, Yuto Kobayashi, Hiroyuki Sugimori, Minghui Tang, Kenneth Sutherland, Atsushi Noguchi, Michihito Kono, Tatsuya Atsumi
British Journal of Radiology, 89, 1061, 2016, [Peer-reviewed]
English, Scientific journal

Evaluation of cerebral blood flow using multi-phase pseudo continuous arterial spin labeling at 3-tesla
Hiroyuki Sugimori, Noriyuki Fujima, Yuriko Suzuki, Hiroyuki Hamaguchi, Motomichi Sakata, Kohsuke Kudo
Magnetic Resonance Imaging, 33, 10, 1338, 1344, Dec. 2015, [Peer-reviewed], [Lead author]
English, Scientific journal

Quantification of myocardial blood flow with dynamic perfusion 3.0 Tesla MRI: Validation with ¹⁵o-water PET
Yuuki Tomiyama, Osamu Manabe, Noriko Oyama-Manabe, Masanao Naya, Hiroyuki Sugimori, Kenji Hirata, Yuki Mori, Hiroyuki Tsutsui, Kohsuke Kudo, Nagara Tamaki, Chietsugu Katoh
Journal of Magnetic Resonance Imaging, 42, 3, 754, 762, 01 Sep. 2015, [Peer-reviewed], [International Magazine]
English, Scientific journal

FDG PET/CT diagnostic criteria may need adjustment based on MRI to estimate the presurgical risk of extrapelvic infiltration in patients with uterine endometrial cancer
Satoko Sudo, Naoya Hattori, Osamu Manabe, Fumi Kato, Rie Mimura, Keiichi Magota, Hiroyuki Sugimori, Kenji Hirata, Noriaki Sakuragi, Nagara Tamaki
European Journal of Nuclear Medicine and Molecular Imaging, 42, 5, 676, 684, 05 Mar. 2015, [Peer-reviewed], [International Magazine]
English, Scientific journal

Bilateral breast MRI by use of dual-source parallel radiofrequency excitation and image-based shimming at 3 Tesla: improvement in homogeneity on fat-suppression imaging
Kinya Ishizaka, Fumi Kato, Satoshi Terae, Suzuko Mito, Noriko Oyama-Manabe, Tamotsu Kamishima, Mitsuhiro Nakanishi, Hiroyuki Sugimori, Hiroyuki Hamaguchi, Hiroki Shirato
Radiological Physics and Technology, 8, 1, 4, 12, 2015, [Peer-reviewed], [Domestic magazines]
English, Scientific journal

Simple prediction of right ventricular ejection fraction using tricuspid annular plane systolic excursion in pulmonary hypertension
Takahiro Sato, Ichizo Tsujino, Noriko Oyama-Manabe, Hiroshi Ohira, Yoichi M. Ito, Hiroyuki Sugimori, Asuka Yamada, Chisa Takashina, Taku Watanabe, Masaharu Nishimura
International Journal of Cardiovascular Imaging, 29, 8, 1799, 1805, Dec. 2013, [Peer-reviewed]
English, Scientific journal

[Non-gated vessel wall imaging of the internal carotid artery using radial scanning and fast spin echo sequence: evaluation of vessel signal Intensity by flow rate at 3.0 tesla].
Manami Nakamura, Takeshi Makabe, Masaki Ichikawa, Ryohei Hatakeyama, Hiroyuki Sugimori, Motomichi Sakata
Nihon Hoshasen Gijutsu Gakkai zasshi, 69, 11, 1261, 1265, Nov. 2013, [Peer-reviewed]
Scientific journal

SIMPLE PREDICTION OF RIGHT VENTRICULAR EJECTION FRACTION USING TRICUSPID ANNULAR PLANE SYSTOLIC EXCURSION IN PULMONARY HYPERTENSION
Sato Takahiro, Tsujino Ichizo, Oyama-Manabe Noriko, Ohira Hiroshi, Ito Yoichi. M, Sugimori Hiroyuki, Yamada Asuka, Takashina Chisa, Watanabe Taku, Nishimura Masaharu
RESPIROLOGY, 18, 20, Nov. 2013, [Peer-reviewed]

Comparison of SPAMM and SENC methods for evaluating peak circumferential strain at 3T
Hiroyuki Sugimori, Noriko Oyama-Manabe, Kinya Ishizaka, Hiroyuki Hamaguchi, Motomichi Sakata
Magnetic Resonance in Medical Sciences, 12, 1, 69, 75, 1, 25 Mar. 2013, [Peer-reviewed], [Lead author], [Domestic magazines]
English, Scientific journal

Evaluation of renal blood flow using multi-phase echo-planar magnetic resonance imaging and signal targeting with alternating radiofrequency (EPISTAR) in 3-T magnetic resonance imaging
Hiroyuki Sugimori, Mitsuhiro Nakanishi, Noriyuki Fujima, Kinya Ishizaka, Suzuko Mito, Hiroyuki Hamaguchi, Motomichi Sakata
Radiological Physics and Technology, 6, 1, 86, 91, 1, Jan. 2013, [Peer-reviewed], [Lead author, Corresponding author]
Scientific journal

Identification and further differentiation of subendocardial and transmural myocardial infarction by fast strain-encoded (SENC) magnetic resonance imaging at 3.0 Tesla
Noriko Oyama-Manabe, Naoki Ishimori, Hiroyuki Sugimori, Marc Van Cauteren, Kohsuke Kudo, Osamu Manabe, Tomoyuki Okuaki, Tamotsu Kamishima, Yoichi M. Ito, Hiroyuki Tsutsui, Khin Khin Tha, Satoshi Terae, Hiroki Shirato
European Radiology, 21, 11, 2362, 2368, 11, Nov. 2011, [Peer-reviewed], [International Magazine]
English, Scientific journal

Comparison of ¹H MR spectroscopy, 3-point DIXON, and multi-echo gradient echo for measuring hepatic fat fraction
Kinya Ishizaka, Noriko Oyama, Suzuko Mito, Hiroyuki Sugimori, Mitsuhiro Nakanishi, Tomoyuki Okuaki, Hiroki Shirato, Satoshi Terae
Magnetic Resonance in Medical Sciences, 10, 1, 41, 48, 2011, [Peer-reviewed]
English, Scientific journal

[Comparison of fat suppression techniques of bilateral breast dynamic sequence at 3.0 T: utility of three-point DIXON technique].
Suzuko Mito, Kinya Ishizaka, Mitsuhiro Nakanishi, Hiroyuki Sugimori, Hiroyuki Hamaguchi, Tomoyasu Tsuzuki
Nihon Hoshasen Gijutsu Gakkai zasshi, 67, 6, 654, 660, 6, 2011, [Peer-reviewed]
Scientific journal

Optimization of dark-blood T2-weighted sequence in myocardium magnetic resonance imaging
Hiroyuki Sugimori, Takahiro Uno, Akira Yanagisawa
Nippon Hoshasen Gijutsu Gakkai zasshi, 65, 5, 612, 619, 5, 20 May 2009, [Peer-reviewed]
Scientific journal

VIBE MRI for evaluating the normal and abnormal gastrointestinal tract in fetuses
Tsutomu Inaoka, Hiroyuki Sugimori, Yoshihito Sasaki, Koji Takahashi, Kazuo Sengoku, Nobuhisa Takada, Tamio Aburano
American Journal of Roentgenology, 189, 6, 1316, W308, Dec. 2007, [Peer-reviewed]
English, Scientific journal

Usefulness of phase-sensitive inversion recovery in delayed enhanced cardiac MRI
Hiroyuki Sugimori, Naka Sakamoto, Shunsuke Natori, Akira Yanagisawa, Takahiro Uno, Makoto Kubota
Nippon Hoshasen Gijutsu Gakkai zasshi, 63, 6, 661, 666, 6, 20 Jun. 2007, [Peer-reviewed], [Lead author, Corresponding author]
Scientific journal

Thymic hyperplasia and thymus gland tumors: Differentiation with chemical shift MR imaging
Tsutomu Inaoka, Koji Takahashi, Masayuki Mineta, Tomonori Yamada, Noriyuki Shuke, Atsutaka Okizaki, Kenichi Nagasawa, Hiroyuki Sugimori, Tamio Aburano
Radiology, 243, 3, 869, 876, Jun. 2007, [Peer-reviewed]
English, Scientific journal

Visualization of normal pulmonary fissures on sagittal multiplanar reconstruction MDCT
Koji Takahashi, Brad Thompson, William Stanford, Yutaka Sato, Kenichi Nagasawa, Hiroaki Sato, Makoto Kubota, Ayako Kashiba, Hiroyuki Sugimori
American Journal of Roentgenology, 187, 2, 389, 397, Aug. 2006, [Peer-reviewed]
Scientific journal

Three-dimensional magnetic resonance imaging after ultrasonography for assessment of fetal gastroschisis
Yoshihito Sasaki, Toshinobu Miyamoto, Yasuhiro Hidaka, Hisashi Satoh, Naoyuki Takuma, Kazuo Sengoku, Hiroyuki Sugimori, Tsutomu Inaoka, Tamio Aburano
Magnetic Resonance Imaging, 24, 2, 201, 203, Feb. 2006, [Peer-reviewed]
Scientific journal

第3回北海道大学医療AIシンポジウム開催報告—Event Report of 3rd Hokkaido University Medical AI Symposium
唐 明輝, 佐藤 夏季, 平田 健司, 杉森 博行, 吉村 高明, 尾藤 良孝, 小笠原 克彦, 工藤 與亮, 北海道放射線医学雑誌 = Hokkaido Journal of Radiology, 5, 31, 35, Mar. 2025
[札幌] : 北海道放射線医学雑誌, Japanese

A Fundamental Study on Coronary Artery Detection Methods Using Deep Learning with Combined Object Detection and Segmentation
坂本茉凛, 吉村高明, 杉森博行, 日本診療放射線技師会誌, 71, 10, 1206, 1207, Oct. 2024
(公社)日本診療放射線技師会, Japanese

An initial study of developing an automatic positioning judgement model for mammography in MLO considering the pectoralis major muscle using deep learning
境田みう, 吉村高明, 杉森博行, 日本診療放射線技師会誌, 71, 10, 1185, 1185, Oct. 2024
(公社)日本診療放射線技師会, Japanese

Deep Learning-based Detection of Surrounding Structures for Pancreatic Segmentation
蠣崎航, 吉村高明, 杉森博行, 日本診療放射線技師会誌, 71, 10, 1158, 1158, Oct. 2024
(公社)日本診療放射線技師会, Japanese

A Study on Estimating the Angle of Oblique Lumbar Spine Radiographs Using Virtual X-ray Images for Training
山本吏理亜, 堤香織, 吉村高明, 杉森博行, 日本診療放射線技師会誌, 71, 10, 1207, 1208, Oct. 2024
(公社)日本診療放射線技師会, Japanese

血行再建に残された課題 CEAにおける患者安全と外科教育のための手術映像分析研究 頸動脈剥離における組織加速度評価　　　　　　　　　　　　　　　
杉山 拓, 伊東 雅基, 杉森 博行, 唐 明輝, 中村 俊孝, 小笠原 克彦, 藤村 幹, The Mt. Fuji Workshop on CVD, 41, 76,82, 83, Jul. 2024
The Mt. Fuji Workshop on CVD事務局, Japanese

第2回北海道大学医療AIシンポジウム開催報告—Event Report of 2nd Hokkaido University Medical AI Symposium
唐 明輝, 平田 健司, 佐藤 夏季, 杉森 博行, 吉村 高明, 小笠原 克彦, 中谷 純, 工藤 與亮, 北海道放射線医学雑誌 = Hokkaido Journal of Radiology, 4, 30, 32, Mar. 2024
[札幌] : 北海道放射線医学雑誌, Japanese

深層学習による体幹部X線CTのスライス位置を限定しない体重推定単一モデルの開発　　　　　　　　　　　　　　　
市川 翔太, 杉森 博行, 日本放射線技術学会総会学術大会予稿集, 80回, 232, 232, Mar. 2024
(公社)日本放射線技術学会, Japanese

Deep Learning技術を用いた腰椎X線撮影の画像品質管理に関する検討
山本吏理亜, 堤香織, 吉村高明, 杉森博行, 平田健司, 工藤與亮, 日本メディカルAI学会学術集会プログラム・抄録集, 6th, 2024

二次元断層心エコー画像による右室収縮機能予測モデルの構築
村山迪史, 吉村高明, 杉森博行, 日本メディカルAI学会学術集会プログラム・抄録集, 6th, 2024

深層学習による膵臓造影CTの領域識別アルゴリズムの検討
蠣崎航, 吉村高明, 杉森博行, 平田健司, 工藤與亮, 日本メディカルAI学会学術集会プログラム・抄録集, 6th, 2024

MR画像誘導即時適応陽子線治療の実現に向けたCycleGANを用いたMR to CT synthesisの初期検討
佐藤圭祐, 吉村高明, 西岡健太郎, 遠藤大輝, 藤澤祐太, 杉森博行, 平田健司, 工藤與亮, 日本メディカルAI学会学術集会プログラム・抄録集, 6th, 2024

MR画像誘導即時適応陽子線治療の実現に向けた16bit MR画像超解像技術の開発
藤澤祐太, 吉村高明, 西岡健太郎, 遠藤大輝, 佐藤圭祐, 杉森博行, 平田健司, 工藤與亮, 日本メディカルAI学会学術集会プログラム・抄録集, 6th, 2024

半教師あり学習を用いたマンモグラフィ石灰化検出に関する基礎的研究
境田みう, 吉村高明, 杉森博行, 平田健司, 工藤與亮, 日本メディカルAI学会学術集会プログラム・抄録集, 6th, 2024

深層学習を用いた冠動脈描出方法における基礎的検討
坂本茉凜, 吉村高明, 杉森博行, 平田健司, 工藤與亮, 日本メディカルAI学会学術集会プログラム・抄録集, 6th, 2024

A Note on Lung Disease Classification Considering Domain Knowledge Via Continuous Self-supervised Learning Pre-trained on J-MID Database
太齊蓮, LI Guang, 藤後廉, TANG Minghui, 吉村高明, 杉森博行, 平田健司, 小川貴弘, 工藤與亮, 長谷山美紀, 電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2024, 2024

SurfaceMIP:FDG-PETで皮膚を観察するためのアルゴリズムの実装とパラメーター最適化　　　　　　　　　　　　　　　
平田 健司, 木村 理奈, 唐 明輝, 渡邊 史郎, 竹中 淳規, 石井 宙史, 杉森 博行, 吉村 高明, 工藤 與亮, 核医学, 61, Suppl., S162, S162, 2024
(一社)日本核医学会, Japanese

2.5次元DDSRCNNを用いた低カウントPET画像の画質改善モデルの開発と定量性評価　　　　　　　　　　　　　　　
遠藤 大輝, 吉村 高明, 唐 明輝, 杉森 博行, 孫田 惠一, 木村 理奈, 渡邊 史郎, 平田 健司, 工藤 與亮, 核医学, 61, Suppl., S188, S188, 2024
(一社)日本核医学会, Japanese

SurfaceMIP:FDG-PETで皮膚を観察するためのアルゴリズムの実装とパラメーター最適化　　　　　　　　　　　　　　　
平田 健司, 木村 理奈, 唐 明輝, 渡邊 史郎, 竹中 淳規, 石井 宙史, 杉森 博行, 吉村 高明, 工藤 與亮, 核医学, 61, Suppl., S162, S162, 2024
(一社)日本核医学会, Japanese

2.5次元DDSRCNNを用いた低カウントPET画像の画質改善モデルの開発と定量性評価　　　　　　　　　　　　　　　
遠藤 大輝, 吉村 高明, 唐 明輝, 杉森 博行, 孫田 惠一, 木村 理奈, 渡邊 史郎, 平田 健司, 工藤 與亮, 核医学, 61, Suppl., S188, S188, 2024
(一社)日本核医学会, Japanese

造影心臓CT画像を用いた深層学習による大動脈弁自動抽出法の検討
猪股 壮一郎, 吉村 高明, 唐 明輝, 市川 翔太, 杉森 博行, 北海道放射線技術雑誌, 95, 95, 46, 46, Nov. 2023
(公社)日本放射線技術学会-北海道支部, Japanese

マンモグラフィにおける石灰化識別のための半教師あり学習の適用と評価
境田 みう, 吉村 高明, 唐 明輝, 市川 翔太, 杉森 博行, 北海道放射線技術雑誌, 95, 95, 48, 48, Nov. 2023
(公社)日本放射線技術学会-北海道支部, Japanese

市立函館病院における診療放射線技師に係るインシデントレポートの分析
狩野 麻名美, 爰地 祐次, 中西 一彰, 川嶋 雄平, 川口 礼子, 佐々木 淳, 小野 雅子, 杉森 博行, 函館医学誌, 47, 1, 12, 15, Sep. 2023
市立函館病院, Japanese

深層学習を用いた局所脳血流SPECT検査における撮像時間短縮の検討　　　　　　　　　　　　　　　
及川 青亮, 阪井 純, 安斉 公雄, 藤原 雄介, 杉森 博行, 日本放射線技術学会雑誌, 79, 9, 1098, 1098, Sep. 2023
(公社)日本放射線技術学会, Japanese

Deep Learningを用いたマンモグラフィ石灰化検出方法の開発　　　　　　　　　　　　　　　
境田 みう, 吉村 高明, 唐 明輝, 杉森 博行, 日本放射線技術学会雑誌, 79, 9, 1027, 1027, Sep. 2023
(公社)日本放射線技術学会, Japanese

肋骨CR画像の撮影時情報の事後推定におけるVision TransformerとCNNの精度比較　　　　　　　　　　　　　　　
窪田 将也, 吉村 高明, 唐 明輝, 杉森 博行, 日本放射線技術学会雑誌, 79, 9, 1078, 1078, Sep. 2023
(公社)日本放射線技術学会, Japanese

Deep Learningを用いた腰椎斜位像の角度推定における基礎的検討　　　　　　　　　　　　　　　
森谷 竜馬, 吉村 高明, 唐 明輝, 杉森 博行, 日本放射線技術学会雑誌, 79, 9, 1078, 1078, Sep. 2023
(公社)日本放射線技術学会, Japanese

心臓CT画像からの深層学習によるセグメンテーションを用いた大動脈弁自動推定法の検討　　　　　　　　　　　　　　　
猪股 壮一郎, 吉村 高明, 唐 明輝, 市川 翔太, 杉森 博行, 日本放射線技術学会雑誌, 79, 9, 1028, 1029, Sep. 2023
(公社)日本放射線技術学会, Japanese

【医療AIの普及拡大とさらなる展開 医療からヘルスケアサービスまで発展に向けた現状と展望】医療AIのための人材育成の現状と展望 北海道大学における医療AI開発者育成プログラムの活動と展望　　　　　　　　　　　　　　　
唐 明輝, 平田 健司, 杉森 博行, 吉村 高明, 小笠原 克彦, 中谷 純, 工藤 與亮, INNERVISION, 38, 7, 19, 20, Jun. 2023
(株)インナービジョン, Japanese

Ammonia PETにおけるDeep Learningを用いた心外集積除去法の検討　　　　　　　　　　　　　　　
山田 佑介, 安藤 彰, 本間 仁, 吉村 高明, 唐 明輝, 杉森 博行, 日本心臓核医学会ニュースレター, 25, 2, 85, 85, May 2023
日本心臓核医学会, Japanese

第1回北海道大学医療AIシンポジウム開催報告—Event Report of 1st Hokkaido University Medical AI Symposium
唐 明輝, 平田 健司, 杉森 博行, 吉村 高明, 小笠原 克彦, 中谷 純, 工藤 與亮, 北海道放射線医学雑誌 = Hokkaido Journal of Radiology, 3, 41, 44, Mar. 2023
[札幌] : 北海道放射線医学雑誌, Japanese

Automatic Quantification of Enhancing Pannus of the Rheumatoid Hand using Machine Learning in DCE-MRI　　　　　　　　　　　　　　　
Fang Wanxuan, Mao Yijun, An Yujie, Sugimori Hiroyuki, Kiuch Shinji, Kamishima Tamotsu, 日本医学放射線学会学術集会抄録集, 82回, S194, S194, Mar. 2023
(公社)日本医学放射線学会, English

Rheumatoid Arthritis Synovitis Segmentation Based on Unsupervised Learning and Time-intensity Curve Signal Data on Dynamic Contrast Enhaced MRI　　　　　　　　　　　　　　　
Mao Yijun, Fang Wanxuan, An Yujie, Sugimori Hiroyuki, Kiuch Shinji, Kamishima Tamotsu, 日本放射線技術学会総会学術大会予稿集, 79回, 322, 322, Mar. 2023
(公社)日本放射線技術学会, English

MIP類似アルゴリズムによるFDG-PET体表画像の有用性　　　　　　　　　　　　　　　
平田 健司, 木村 理奈, 唐 明輝, 渡邊 史郎, 竹中 淳規, 若林 直人, 杉森 博行, 吉村 高明, 工藤 與亮, 核医学, 60, Suppl., S208, S208, 2023
(一社)日本核医学会, Japanese

SRCNNを用いた短時間収集PET画像の画質改善モデルの開発と定量性評価　　　　　　　　　　　　　　　
遠藤 大輝, 吉村 高明, 唐 明輝, 杉森 博行, 長谷川 淳, 小亀 翔揮, 孫田 惠一, 木村 理奈, 渡邊 史郎, 平田 健司, 工藤 與亮, 核医学, 60, Suppl., S209, S209, 2023
(一社)日本核医学会, Japanese

SRCNNを用いた短時間収集PET画像の画質改善モデルの開発と定量性評価
遠藤大輝, 吉村高明, 唐明輝, 杉森博行, 長谷川淳, 小亀翔揮, 孫田惠一, 木村理奈, 渡邊史郎, 平田健司, 工藤與亮, 核医学(Web), 60, Supplement, 2023

MIP類似アルゴリズムによるFDG-PET体表画像の有用性
平田健司, 木村理奈, 唐明輝, 渡邊史郎, 竹中淳規, 若林直人, 杉森博行, 吉村高明, 工藤與亮, 平田健司, 木村理奈, 唐明輝, 渡邊史郎, 竹中淳規, 若林直人, 杉森博行, 吉村高明, 工藤與亮, 平田健司, 唐明輝, 渡邊史郎, 杉森博行, 吉村高明, 工藤與亮, 平田健司, 杉森博行, 吉村高明, 工藤與亮, 平田健司, 工藤與亮, 核医学(Web), 60, Supplement, 2023

MIP類似アルゴリズムによるFDG-PET体表画像の有用性　　　　　　　　　　　　　　　
平田 健司, 木村 理奈, 唐 明輝, 渡邊 史郎, 竹中 淳規, 若林 直人, 杉森 博行, 吉村 高明, 工藤 與亮, 核医学, 60, Suppl., S208, S208, 2023
(一社)日本核医学会, Japanese

SRCNNを用いた短時間収集PET画像の画質改善モデルの開発と定量性評価　　　　　　　　　　　　　　　
遠藤 大輝, 吉村 高明, 唐 明輝, 杉森 博行, 長谷川 淳, 小亀 翔揮, 孫田 惠一, 木村 理奈, 渡邊 史郎, 平田 健司, 工藤 與亮, 核医学, 60, Suppl., S209, S209, 2023
(一社)日本核医学会, Japanese

Comparison of evaluation metrics by devising supervised images in cerebral infarction
森谷竜馬, 吉村高明, TANG Minghui, 杉森博行, 北海道放射線技術雑誌(Web), 94, 2023

Deep Learning技術を用いた脳MRI画像によるヒトの年齢推定手法の検討　　　　　　　　　　　　　　　
薄井 康輔, 吉村 高明, 唐 明輝, 杉森 博行, 日本放射線技術学会雑誌, 78, 9, 1086, 1086, Sep. 2022
(公社)日本放射線技術学会, Japanese

3D-Convolutional Neural Network(CNN)による回帰を用いた左室駆出率予測に関する検討　　　　　　　　　　　　　　　
猪股 壮一郎, 吉村 高明, 唐 明輝, 杉森 博行, 日本放射線技術学会雑誌, 78, 9, 1117, 1117, Sep. 2022
(公社)日本放射線技術学会, Japanese

Deep Learning技術を用いた脳MRI画像によるヒトの年齢推定手法の検討　　　　　　　　　　　　　　　
薄井 康輔, 吉村 高明, 唐 明輝, 杉森 博行, 日本放射線技術学会雑誌, 78, 9, 1086, 1086, Sep. 2022
(公社)日本放射線技術学会, Japanese

3D-Convolutional Neural Network(CNN)による回帰を用いた左室駆出率予測に関する検討　　　　　　　　　　　　　　　
猪股 壮一郎, 吉村 高明, 唐 明輝, 杉森 博行, 日本放射線技術学会雑誌, 78, 9, 1117, 1117, Sep. 2022
(公社)日本放射線技術学会, Japanese

核医学～近未来核医学の向かう道-診断・治療の精度をあげる最新手法～ 核医学におけるAIの活用と課題　　　　　　　　　　　　　　　
平田 健司, 杉森 博行, 藤間 憲幸, 豊永 拓哉, 工藤 與亮, 日本医学放射線学会秋季臨床大会抄録集, 58回, S344, S344, Aug. 2022
(公社)日本医学放射線学会, Japanese

核医学〜近未来核医学の向かう道-診断・治療の精度をあげる最新手法〜 核医学におけるAIの活用と課題　　　　　　　　　　　　　　　
平田 健司, 杉森 博行, 藤間 憲幸, 豊永 拓哉, 工藤 與亮, 日本医学放射線学会秋季臨床大会抄録集, 58回, S344, S344, Aug. 2022
(公社)日本医学放射線学会, Japanese

頭部単純CT撮影における物体検出技術を用いた多段面再構成画像の自動生成　　　　　　　　　　　　　　　
市川 翔太, 山本 浩之, 板谷 英樹, 杉森 博行, 日本放射線技術学会総会学術大会予稿集, 78回, 165, 166, Mar. 2022
(公社)日本放射線技術学会, Japanese

関節リウマチ患者の滑膜炎を自動で抽出するソフトウエアの開発 手のダイナミックMRIを用いた検討(Toward Development of Software Application that can Automatically Demonstrate the Distribution of Pannus in Rheumatoid Hand using Dynamic MRI Dataset)　　　　　　　　　　　　　　　
Fang Wanxuan, An Yujie, Sugimori Hiroyuki, Kiuchi Shinji, Kamishima Tamotsu, 日本医学放射線学会学術集会抄録集, 81回, S210, S210, Mar. 2022
(公社)日本医学放射線学会, English

演繹法と帰納法の視点から見た医療AI　　　　　　　　　　　　　　　
平田 健司, 杉森 博行, 唐 明輝, 中谷 純, 小笠原 克彦, 豊永 拓哉, 工藤 與亮, 北海道放射線医学雑誌, 2, 1, 6, Mar. 2022
(NPO)メディカルイメージラボ, Japanese

喘息・COPDにおける人工知能を用いた予後予測による疾患マネジメントの向上　　　　　　　　　　　　　　　
清水 薫子, 杉森 博行, 大和証券ヘルス財団研究業績集, 45, 3, 6, Mar. 2022
(公財)大和証券ヘルス財団, Japanese

頭部単純CT撮影における物体検出技術を用いた多段面再構成画像の自動生成　　　　　　　　　　　　　　　
市川 翔太, 山本 浩之, 板谷 英樹, 杉森 博行, 日本放射線技術学会総会学術大会予稿集, 78回, 165, 166, Mar. 2022
(公社)日本放射線技術学会, Japanese

Convolutional Neural Networkへの複数画像同時適用における有用性の検討　　　　　　　　　　　　　　　
大浦 大輔, 杉森 博行, 日本放射線技術学会総会学術大会予稿集, 78回, 205, 205, Mar. 2022
(公社)日本放射線技術学会, Japanese

演繹法と帰納法の視点から見た医療AI　　　　　　　　　　　　　　　
平田 健司, 杉森 博行, 唐 明輝, 中谷 純, 小笠原 克彦, 豊永 拓哉, 工藤 與亮, 北海道放射線医学雑誌, 2, 1, 6, Mar. 2022
(NPO)メディカルイメージラボ, Japanese

¹⁷O標識水を水トレーサーとして用いたMRIによる関節軟骨の質的評価法の確立
細川吉暁, 小野寺智洋, 亀田浩之, 宝満健太郎, 工藤與亮, 杉森博行, 岩崎倫政, JOSKAS-JOSSM (Web), 2022, 2022

深層学習を用いた脳梗塞領域抽出における教師画像の工夫による評価指標の比較
森谷竜馬, 吉村高明, 唐明輝, 杉森博行, 北海道放射線技術雑誌(Web), 93, 2022

cine-MRIを用いた3D-CNNによる左室駆出率と右室駆出率推定
猪股壮一郎, 吉村高明, 唐明輝, 杉森博行, 北海道放射線技術雑誌(Web), 93, 2022

¹⁷O標識水を水トレーサーとして用いたMRIによる関節軟骨病変評価法の確立
細川吉暁, 小野寺智洋, 宝満健太郎, 工藤與亮, 亀田浩之, 杉森博行, 北海道整形災害外科学会, 141st, 2022

Semantic segmentation(SS)技術におけるData augmentation(DA)の手法と効果の検証
浅見 祐輔, 山田 宝生, 真鍋 圭佑, 杉森 博行, 北海道放射線技術雑誌, 91, 91, 51, 51, Oct. 2021
(公社)日本放射線技術学会-北海道支部, Japanese

MRIに特化したCNN(convolutional neural network)の開発
真鍋 圭佑, 山田 宝生, 浅見 祐輔, 杉森 博行, 北海道放射線技術雑誌, 91, 91, 51, 51, Oct. 2021
(公社)日本放射線技術学会-北海道支部, Japanese

Deep Learningを用いた心撮像断面自動推定手法の検討
薄井 康輔, 杉森 博行, 北海道放射線技術雑誌, 91, 91, 57, 57, Oct. 2021
(公社)日本放射線技術学会-北海道支部, Japanese

【Nuclear Medicine Today 2021 キーワードから展望する核医学の技術開発と臨床応用】人工知能(AI)の研究開発の現状と将来展望 1)腫瘍核医学におけるAI利用の動向
平田 健司, 藤間 憲幸, 杉森 博行, 工藤 與亮, INNERVISION, 36, 10, 17, 20, Sep. 2021
(株)インナービジョン, Japanese

Deep Learning技術を用いた胸部X線画像評価ソフトウェアの開発　　　　　　　　　　　　　　　
薄井 康輔, 泉 勇希, 杉森 博行, 日本放射線技術学会雑誌, 77, 9, 1024, 1024, Sep. 2021
(公社)日本放射線技術学会, Japanese

主幹動脈閉塞における短時間Phase Contrast Angiographyを用いたMRAと塞栓物質の同時描出　　　　　　　　　　　　　　　
大浦 大輔, 伊原 陸, 蛯名 翼, 月花 正幸, 杉森 博行, 日本放射線技術学会雑誌, 77, 9, 1037, 1037, Sep. 2021
(公社)日本放射線技術学会, Japanese

Deep Learningを用いた前立腺がん検出の精度向上に関する基礎的検討　　　　　　　　　　　　　　　
真鍋 圭佑, 吉村 高明, 山田 宝生, 浅見 祐輔, 杉森 博行, 日本放射線技術学会雑誌, 77, 9, 1089, 1089, Sep. 2021
(公社)日本放射線技術学会, Japanese

背景情報の少ない医用画像における物体検出の学習最適化の基礎的検討　　　　　　　　　　　　　　　
山田 宝生, 真鍋 圭祐, 浅見 祐輔, 杉森 博行, 日本放射線技術学会雑誌, 77, 9, 1106, 1106, Sep. 2021
(公社)日本放射線技術学会, Japanese

Deep Learningを用いた肝臓の自動抽出に関する基礎的研究　　　　　　　　　　　　　　　
泉 勇希, 薄井 康輔, 杉森 博行, 日本放射線技術学会雑誌, 77, 9, 1106, 1106, Sep. 2021
(公社)日本放射線技術学会, Japanese

Semantic Segmentation技術を用いた単純CT画像におけるの血管検出の基礎的検討　　　　　　　　　　　　　　　
浅見 祐輔, 裴 梓言, 山田 宝生, 真鍋 圭佑, 杉森 博行, 日本放射線技術学会雑誌, 77, 9, 1107, 1107, Sep. 2021
(公社)日本放射線技術学会, Japanese

【Nuclear Medicine Today 2021 キーワードから展望する核医学の技術開発と臨床応用】人工知能(AI)の研究開発の現状と将来展望 1)腫瘍核医学におけるAI利用の動向　　　　　　　　　　　　　　　
平田 健司, 藤間 憲幸, 杉森 博行, 工藤 與亮, INNERVISION, 36, 10, 17, 20, Sep. 2021
(株)インナービジョン, Japanese

胸部CTでの定量的手法による1群又は3群強皮症性肺高血圧症の評価　　　　　　　　　　　　　　　
蜷川 慶太, 加藤 将, 河野 通仁, 藤枝 雄一郎, 大平 洋, 奥 健志, 杉森 博行, 辻野 一三, 渥美 達也, 日本肺高血圧・肺循環学会学術集会・日本小児肺循環研究会プログラム・抄録集, 6回・27回, 41, 41, May 2021
日本肺高血圧・肺循環学会・日本小児肺循環研究会, Japanese

肝臓のMR弾性造影に関する半自動化定量ソフトウエアの結果の妥当性と再現性に関する研究(Validation and Reproducibility Study on Semi-automatic Quantification Software for MR Elastography of the Liver)　　　　　　　　　　　　　　　
Katsuumi Yuri, Kamishima Tamotsu, Sugimori Hiroyuki, Shimamura Tsuyoshi, Kawamura Norio, Takeda Hiroshi, 日本放射線技術学会総会学術大会予稿集, 77回, 248, 249, Mar. 2021
(公社)日本放射線技術学会, English

畳み込みニューラルネットワークを用いた胸部CTスカウト画像からの性別・体格推定
市川翔太, 市川翔太, 山本浩之, 杉森博行, 中四国放射線医療技術フォーラムプログラム抄録集, 17th, 2021

PET画像におけるDeep learningとその複合技術を用いた検出精度の検討
河上 壮志, 杉森 博行, 平田 健司, 孫田 惠一, 加藤 千恵次, 核医学技術, 40, 予稿集, 345, 345, Oct. 2020
(NPO)日本核医学技術学会, Japanese

乳腺dynamic MRIにおける造影中期片側乳房矢状断高分解能撮像の検討
狩野 麻名美, 畠山 遼兵, 柴崎 光咲, 小川 肇, 杉森 博行, 函館医学誌, 44, 1, 28, 31, Sep. 2020
市立函館病院, Japanese

敵対的生成ネットワークを用いた脳MR画像生成に関する検討　　　　　　　　　　　　　　　
小川 敬由樹, 真鍋 圭佑, 杉森 博行, 北海道放射線技術雑誌, 88, 2, 3, Apr. 2020
(公社)日本放射線技術学会-北海道支部, Japanese

Deep learningによる物体検出技術を用いた脳動脈瘤検出の基礎的検討　　　　　　　　　　　　　　　
山田 宝生, 浅見 祐輔, 杉森 博行, 北海道放射線技術雑誌, 88, 4, 5, Apr. 2020
(公社)日本放射線技術学会-北海道支部, Japanese

Deep learningによる超解像技術を用いたMR画像の高解像度化に関する研究　　　　　　　　　　　　　　　
真鍋 圭佑, 小川 敬由樹, 杉森 博行, 北海道放射線技術雑誌, 88, 6, 7, Apr. 2020
(公社)日本放射線技術学会-北海道支部, Japanese

Deep Learningによる物体検出技術を用いた椎体位置自動同定の検討　　　　　　　　　　　　　　　
浅見 祐輔, 山田 宝生, 杉森 博行, 北海道放射線技術雑誌, 88, 8, 9, Apr. 2020
(公社)日本放射線技術学会-北海道支部, Japanese

内挿法を用いた心臓cine-MR画像における時相間平滑化の検討　　　　　　　　　　　　　　　
山内 桃花, 杉森 博行, 石坂 欣也, 北海道放射線技術雑誌, 88, 16, 17, Apr. 2020
(公社)日本放射線技術学会-北海道支部, Japanese

Compressed SensingがLook Locker法のT1値測定に及ぼす影響の検討　　　　　　　　　　　　　　　
平野 裕也, 石坂 欣也, 青池 寿々子, 杉森 博行, 北海道放射線技術雑誌, 88, 18, 19, Apr. 2020
(公社)日本放射線技術学会-北海道支部, Japanese

肝臓のMRエラストグラフィにおける閾値によるSemi-automatic Quantification Softwareの検証研究(Validation Study on Semi-automatic Quantification Software by Threshold Value for MR Elastography of the Liver)　　　　　　　　　　　　　　　
Katsuumi Yuri, Kamishima Tamotsu, Sugimori Hiroyuki, Shimamura Tsuyoshi, Kawamura Norio, Takeda Hiroshi, 日本放射線技術学会総会学術大会予稿集, 76回, 318, 319, Mar. 2020
(公社)日本放射線技術学会, English

マンモグラフィ装置におけるIntelligent AECの性能評価と位置依存性の検討　　　　　　　　　　　　　　　
狩野 麻名美, 高見 光咲, 藤田 佐智恵, 市川 昌樹, 畠山 遼兵, 竹田 亜由美, 伊藤 真理, 今野 祐治, 杉森 博行, 日本放射線技術学会東北部会雑誌, 29, 158, 159, Jan. 2020
(公社)日本放射線技術学会-東北支部, Japanese

A study of middle phase contrast high-resolution single breast sagittal imaging during bilateral breast dynamic scans in MR mammography
狩野麻名美, 畠山遼兵, 柴崎光咲, 小川肇, 杉森博行, 函館医学誌, 44, 1, 28, 31, 2020
市立函館病院, Japanese

携帯端末を通して得られたCT画像を用いた画像分類の精度評価の検討　　　　　　　　　　　　　　　
曹 瀛丹, 杉森 博行, 小笠原 克彦, 医療情報学連合大会論文集, 39回, 788, 789, Nov. 2019
(一社)日本医療情報学会, Japanese

携帯端末を通して得られたCT画像を用いた画像分類の精度評価の検討　　　　　　　　　　　　　　　
曹 瀛丹, 杉森 博行, 小笠原 克彦, 医療情報学連合大会論文集, 39回, 788, 789, Nov. 2019
(一社)日本医療情報学会, Japanese

携帯端末を通して得られたCT画像を用いた画像分類の精度評価の検討　　　　　　　　　　　　　　　
曹 瀛丹, 杉森 博行, 小笠原 克彦, 医療情報学連合大会論文集, 39回, 406, 406, Nov. 2019
(一社)日本医療情報学会, Japanese

Compressed SensingがLook-Locker法のT1値測定に及ぼす影響の検討　　　　　　　　　　　　　　　
平野 裕也, 石坂 欣也, 青池 寿々子, 杉森 博行, 北海道放射線技術雑誌, 87, 56, 56, Oct. 2019
(公社)日本放射線技術学会-北海道支部, Japanese

内挿法を用いた心臓cine-MR画像における時相間平滑化の検討　　　　　　　　　　　　　　　
山内 桃花, 石坂 欣也, 杉森 博行, 北海道放射線技術雑誌, 87, 67, 67, Oct. 2019
(公社)日本放射線技術学会-北海道支部, Japanese

Deep learningによる物体検出技術を用いた脳動脈瘤検出の基礎的検討　　　　　　　　　　　　　　　
山田 宝生, 杉森 博行, 浅見 祐輔, 北海道放射線技術雑誌, 87, 75, 75, Oct. 2019
(公社)日本放射線技術学会-北海道支部, Japanese

Deep learningによる超解像技術を用いたMR画像の高解像度化に関する研究　　　　　　　　　　　　　　　
真鍋 圭佑, 杉森 博行, 小川 敬由樹, 北海道放射線技術雑誌, 87, 76, 76, Oct. 2019
(公社)日本放射線技術学会-北海道支部, Japanese

Deep learningによる物体検出技術を用いた椎体位置自動同定の検討　　　　　　　　　　　　　　　
浅見 祐輔, 杉森 博行, 山田 宝生, 北海道放射線技術雑誌, 87, 76, 76, Oct. 2019
(公社)日本放射線技術学会-北海道支部, Japanese

敵対的生成ネットワークを用いた脳MR画像生成に関する検討　　　　　　　　　　　　　　　
小川 敬由樹, 真鍋 圭佑, 杉森 博行, 北海道放射線技術雑誌, 87, 77, 77, Oct. 2019
(公社)日本放射線技術学会-北海道支部, Japanese

Deep learningを用いたPET画像における病変や生理的集積の自動検出精度の検討
河上 壮志, 平田 健司, 杉森 博行, 加藤 千恵次, 核医学技術, 39, 予稿集, 346, 346, Oct. 2019
(NPO)日本核医学技術学会, Japanese

肺高血圧患者における2D-PC法を用いた主肺動脈Wall Shear Stress解析による検討　　　　　　　　　　　　　　　
山内 桃花, 杉森 博行, 石坂 欣也, 真鍋 徳子, 日本放射線技術学会雑誌, 75, 9, 1035, 1035, Sep. 2019
(公社)日本放射線技術学会, Japanese

ステントグラフト内挿術におけるエンドリーク解析の検討　　　　　　　　　　　　　　　
三ツ井 貴博, 杉森 博行, 西田 純, 花輪 真, 日本放射線技術学会雑誌, 75, 9, 1053, 1053, Sep. 2019
(公社)日本放射線技術学会, Japanese

肝臓のMR Elastography検査におけるスライス位置の検討　　　　　　　　　　　　　　　
勝海 友里, 神島 保, 杉森 博行, 嶋村 剛, 川村 典生, 武田 宏司, 日本放射線技術学会雑誌, 75, 9, 1056, 1056, Sep. 2019
(公社)日本放射線技術学会, Japanese

施設倫理委員会への書類提出への手引き　　　　　　　　　　　　　　　
杉森 博行, 北海道放射線技術雑誌, 86, 80, 80, Apr. 2019
(公社)日本放射線技術学会-北海道支部, Japanese

Phase contrast法による位相画像を用いた主肺動脈血管壁剪断応力の評価　　　　　　　　　　　　　　　
山内 桃花, 杉森 博行, 川崎 智博, 石坂 欣也, 真鍋 徳子, 北海道放射線技術雑誌, 86, 32, 33, Apr. 2019
(公社)日本放射線技術学会-北海道支部, Japanese

3T両側乳腺ダイナミックにおけるCAIPIRINHA法による高分解能撮像の検討　　　　　　　　　　　　　　　
狩野 麻名美, 畠山 遼兵, 三浦 喬弘, 宇野 弘幸, 高見 光咲, 永田 健悟, 杉森 博行, 北海道放射線技術雑誌, 86, 46, 47, Apr. 2019
(公社)日本放射線技術学会-北海道支部, Japanese

肝MRエラストグラフィーの半自動定量化ソフトウェアに関する検証研究(Validation Study on Semi-Automatic Quantification Software for MR Elastography of the Liver)　　　　　　　　　　　　　　　
Katsuumi Yuri, Kamishima Tamotsu, Sugimori Hiroyuki, Shimamura Tsuyoshi, Kawamura Norio, Takeda Hiroshi, 日本放射線技術学会総会学術大会予稿集, 75回, 211, 212, Mar. 2019
(公社)日本放射線技術学会, English

慢性眼痛を有する症例の安静時機能的MRIによる検討
田川義晃, 杉森博行, THA KhinKhin, 石田晋, Pain Research, 34, 2, 2019

慢性眼痛を有する症例の安静的機能的MRIによる検討
田川義晃, 大口剛司, 杉森博行, KHIN Tha Khin, 木嶋理紀, 岩田大樹, 田川義継, 石田晋, 日本眼科学会雑誌, 123, 2019

乳腺ダイナミックMRIにおける造影中期片側乳房矢状断高分解能撮像の検討
狩野麻名美, 畠山遼兵, 永田健悟, 宇野弘幸, 高見光咲, 杉森博行, 日本乳癌画像研究会プログラム・抄録集, 28th, 2019

Study on Accuracy Evaluation of Image Classification Using CT Images Obtained by Mobile Phone Terminals
CAO Yingdan, 杉森博行, 小笠原克彦, 医療情報学連合大会論文集(CD-ROM), 39th, 2019

仕事から研究へ繋げる一歩とは? 大学の教育者として思うところ　　　　　　　　　　　　　　　
杉森 博行, 北海道放射線技術雑誌, 85, 23, 24, Nov. 2018
(公社)日本放射線技術学会-北海道支部, Japanese

眼痛を有する患者の安静時機能的MRIによる検討　　　　　　　　　　　　　　　
田川 義晃, 杉森 博行, Tha Khin Khin, 石田 晋, PAIN RESEARCH, 33, 2, 146, 146, Jun. 2018
日本疼痛学会, Japanese

Convolution neural networkを用いたMRIの分類(Classification of Magnetic Resonance Images by Using Convolutional Neural Networks)　　　　　　　　　　　　　　　
Sugimori Hiroyuki, Hamaguchi Hiroyuki, Fujiwara Taro, Ishizaka Kinya, 日本放射線技術学会総会学術大会予稿集, 74回, 211, 211, Mar. 2018
(公社)日本放射線技術学会, English

超音波とMRIの複合的評価は生物学的疾患修飾性抗リウマチ薬(bDMARDs)を投与中の関節リウマチにおけるX線上の関節破壊の予測能を改善する(Composite Assessment of Ultrasonography and MRI Improves the Prognostic Power of Joint Destruction on Radiograph in Rheumatoid Arthritis on Biological Disease-modifying Antirheumatic Drugs(bDMARDs))　　　　　　　　　　　　　　　
Fujimori Motoshi, Kamishima Tamotsu, Seno Yumika, Sugimori Hiroyuki, Nishida Mutsumi, Atsumi Tatsuya, 日本放射線技術学会総会学術大会予稿集, 74回, 231, 232, Mar. 2018
(公社)日本放射線技術学会, English

MOLLI法を用いたT1値測定に関する検討　　　　　　　　　　　　　　　
平野 裕也, 藤原 太郎, 杉森 博行, 平山 博之, 堀江 達則, 石坂 欣也, 北海道放射線技術雑誌, 84, 8, 9, Mar. 2018
(公社)日本放射線技術学会-北海道部会, Japanese

ドライアイ症状を有する患者の安静時機能的MRIによる検討　　　　　　　　　　　　　　　
田川 義晃, 大口 剛司, 杉森 博行, タ・キンキン, 木嶋 理紀, 岩田 大樹, 田川 義継, 石田 晋, 日本眼科学会雑誌, 122, 臨増, 156, 156, Mar. 2018
(公財)日本眼科学会, Japanese

眼瞼けいれん患者の安静時機能的MRIによる検討
田川義晃, 新明康弘, 杉森博行, KHIN Tha Khin, 陳進輝, 石田晋, 神経眼科, 35, 2018

関節リウマチにおける造影ダイナミックMRIを用いた滑膜炎定量評価
小林 勇渡, 市川 翔太, 神島 保, 杉森 博行, 野口 淳史, 河野 通仁, 渥美 達也, 北海道医学雑誌, 92, 2, 108, 109, Nov. 2017
北海道医学会, Japanese

下肢静脈造影CT検査における造影方法の現状　　　　　　　　　　　　　　　
狩野 麻名美, 宇野 弘幸, 小林 匡, 守山 亮, 本庄 俊一, 安井 太一, 小川 肇, 杉森 博行, 函館医学誌, 41, 1, 67, 69, Oct. 2017
深部静脈血栓症(DVT)63例(男23例、女41例、平均年齢71.9歳)を対象に、下肢静脈CT検査と下肢静脈エコーによるDVTの検出率を比較検討した。その結果、下肢静脈エコーを実施していたのは46例(73.0%)と比較的多かった。一方、下肢静脈造影CTを実施していたのは12例(19.0%)、その他CT検査を実施していたのは11例(17.5%)であった。DVTの検出率は、下肢静脈エコーが41症例(89.1%)と最も高かった。一方、下肢静脈CT検査は12例(33.3%)、その他のCT検査は1症例(9.0%)であった。今回の検討では、下肢静脈CTにおける診断能を向上させるためには、CT装置の性能や撮像条件、CTを撮影する診療放射線技師教育などの検討が必要であると考えられた。, 市立函館病院, Japanese

Ultra-short Echo Timeシーケンスにおけるスポーク数が画質に与える影響の検討　　　　　　　　　　　　　　　
濱口 裕行, 杉森 博行, 藤原 太郎, 沼田 直人, 石坂 欣也, 日本放射線技術学会雑誌, 73, 9, 808, 808, Sep. 2017
(公社)日本放射線技術学会, Japanese

Intravoxel Incoherent Motion解析手法が算出パラメータに与える影響の検討　　　　　　　　　　　　　　　
沼田 直人, 杉森 博行, 石坂 欣也, 日本放射線技術学会雑誌, 73, 9, 857, 857, Sep. 2017
(公社)日本放射線技術学会, Japanese

Zonally Magnified Oblique Multislice(ZOOM)EPI-DTI法を用いた腕神経叢の描出における至適撮像条件の検討　　　　　　　　　　　　　　　
野畑 圭亮, Khin Khin Tha, 藤原 太郎, 杉森 博行, 石坂 欣也, 奥秋 知幸, 工藤 興亮, 日本放射線技術学会雑誌, 73, 9, 918, 918, Sep. 2017
(公社)日本放射線技術学会, Japanese

関節リウマチにおける手の滑膜炎定量のためのダイナミック造影強調MRIを用いた簡易的なアプローチ 完全自動化した全画素の分析(Simplified Approach to Quantification for Hand Synovitis in Rheumatoid Arthritis Using Dynamic Contrast Enhanced MRI: Full-Automatic Pixel-By-Pixel Analysis)　　　　　　　　　　　　　　　
Kobayashi Yuto, Kamishima Tamotsu, Ichikawa Shota, Sugimori Hiroyuki, Noguchi Atsushi, Kono Michihito, Atsumi Tatsuya, 日本放射線技術学会総会学術大会予稿集, 73回, 178, 179, Mar. 2017
(公社)日本放射線技術学会, English

Phase-contrast法を用いた腎動脈血流解析における基礎的検討　　　　　　　　　　　　　　　
沼田 直人, 杉森 博行, 石坂 欣也, 北海道放射線技術雑誌, 82, 11, 12, Mar. 2017
(公社)日本放射線技術学会-北海道部会, Japanese

2D-Phase contrast法におけるVENC(velocity encoding)の違いによる頸部血管のWSS(Wall Shear Stress)評価　　　　　　　　　　　　　　　
川崎 智博, 杉森 博行, 石坂 欣也, 北海道放射線技術雑誌, 82, 41, 42, Mar. 2017
(公社)日本放射線技術学会-北海道部会, Japanese

小児股関節撮影におけるExposure Indexの検討　　　　　　　　　　　　　　　
宮本 佳史子, 森 静香, 坂野 稜典, 杉森 博行, 小田 まこと, 北海道放射線技術雑誌, 82, 63, 64, Mar. 2017
(公社)日本放射線技術学会-北海道部会, Japanese

頭部の1H-MR spectroscopyにおける解析ソフトウェアの違いによる結果への影響の検討　　　　　　　　　　　　　　　
濱口 裕行, 杉森 博行, 高森 清華, 藤原 太郎, 葛西 克彦, 北海道放射線技術雑誌, 82, 73, 74, Mar. 2017
(公社)日本放射線技術学会-北海道部会, Japanese

DW-ASLを用いた脳虚血領域におけるwater permeabilityの評価　　　　　　　　　　　　　　　
藤間 憲幸, 奥秋 知幸, 青池 拓哉, 青池 寿々子, 杉森 博行, 工藤 與亮, 日本磁気共鳴医学会雑誌, 37, 1, 15, 17, Feb. 2017
脳虚血性病変を有する8例(男性7名、女性1名、53〜73歳)を対象とした。5例はdiffusion-weighted arterial spin labelingの撮像日の3年前に全脳のT2強調像、FLAIR像での評価を行った。それぞれのROIごとにT2強調像、FLAIR像で虚血の程度に応じて、3段階のグレード評価を行い、殆ど虚血を認めないNI群、虚血性変化が軽度のMI群、虚血性変化が中等度ないし高度のSI群に分けた。それぞれのROIごとに虚血の変化を比較して虚血の変化に応じて二つのグレードに分割した(NP群;3年間で虚血が殆ど変化なし、P群;3年間で虚血が拡大)。DW-ASLの撮像は問題なく施行可能であった。104個のROIによる3段階の虚血の程度の評価に関してKw値を算出し、SI群はMI群、NI群と比較して有意に高かった。3年間の虚血の進行の程度に関しては、5例における65個のROIの評価の結果、P群はNP群と比較して、有意にKwの値が高かった。視覚的にはT2強調像やFLAIR像で認める高信号域よりやや広い領域でwater permeabilityの変化がみられる領域が観察される傾向があった。, (一社)日本磁気共鳴医学会, Japanese

骨髄MRI T1 map値は海綿骨強度予測のための間接的骨質指標となる　　　　　　　　　　　　　　　
遠藤香織, 高畑雅彦, 杉森博行, Wang Jeffrey, 山田悟史, 伊藤陽一, 高橋大介, 清水智弘, 東藤正浩, 但野茂, 工藤與亮, 岩崎倫政, 日本整形外科学会雑誌, 90, 8, S1732, S1732, Aug. 2016
(公社)日本整形外科学会, Japanese

骨髄MRI T1 map値は海綿骨強度予測のための間接的骨質指標となる　　　　　　　　　　　　　　　
遠藤 香織, 高畑 雅彦, 杉森 博行, Wang Jeffrey, 山田 悟史, 伊藤 陽一, 高橋 大介, 清水 智弘, 東藤 正浩, 但野 茂, 工藤 與亮, 岩崎 倫政, 日本整形外科学会雑誌, 90, 8, S1732, S1732, Aug. 2016
(公社)日本整形外科学会, Japanese

3T乳腺MRIにおける拡散尖度画像を用いた浸潤性乳癌の評価 バイオマーカーおよび腋窩リンパ節転移との比較　　　　　　　　　　　　　　　
加藤 扶美, 工藤 與亮, 藤原 太郎, Wang Jeff, 杉森 博行, 山下 啓子, 細田 充主, 真鍋 徳子, 三村 理恵, 白土 博樹, 日本乳癌学会総会プログラム抄録集, 24回, 250, 250, Jun. 2016
(一社)日本乳癌学会, Japanese

Arterial spin labeling法を用いた腎血流量算出におけるInversion timeの検討　　　　　　　　　　　　　　　
沼田 直人, 藤原 太郎, 石坂 欣也, 水戸 寿々子, 高森 清華, 杉森 博行, 北海道放射線技術雑誌, 80, 56, 57, Apr. 2016
(公社)日本放射線技術学会-北海道部会, Japanese

拡散強調画像におけるreadout方法の違いがADC値に与える影響　　　　　　　　　　　　　　　
藤原 太郎, 石坂 欣也, 平山 博之, 吉富 敬祐, 坂本 悠輔, 野畑 圭亮, 杉森 博行, 北海道放射線技術雑誌, 80, 4, 5, Apr. 2016
(公社)日本放射線技術学会-北海道部会, Japanese

3T-MRIにおける胸腰椎撮像時の両腕固定位置の違いがCNRおよびB1不均一に与える影響　　　　　　　　　　　　　　　
石坂 欣也, 原田 邦明, 白猪 亨, 藤原 太郎, 杉森 博行, 北海道放射線技術雑誌, 80, 8, 9, Apr. 2016
(公社)日本放射線技術学会-北海道部会, Japanese

2D-Phase contrast法による頸部血管のWSS(Wall Shear Stress)評価　　　　　　　　　　　　　　　
川崎 智博, 杉森 博行, 北海道放射線技術雑誌, 80, 10, 11, Apr. 2016
(公社)日本放射線技術学会-北海道部会, Japanese

乳腺ダイナミックMRIにおけるParametric Mappingによる血流解析ソフトウェアPMViewの有用性　　　　　　　　　　　　　　　
長谷川 佳菜, 水戸 寿々子, 加藤 扶美, 石坂 欣也, 杉森 博行, 森 祐生, 海谷 佳孝, 北海道放射線技術雑誌, 80, 12, 13, Apr. 2016
(公社)日本放射線技術学会-北海道部会, Japanese

肩腱板の拡散テンソル解析 撮像シーケンスの比較　　　　　　　　　　　　　　　
青野 聡, 石坂 欣也, 高森 清華, 藤原 太郎, 水戸 寿々子, 杉森 博行, 北海道放射線技術雑誌, 80, 14, 15, Apr. 2016
(公社)日本放射線技術学会-北海道部会, Japanese

DSIにおけるパラメータ変化による神経束角度への影響についての検討　　　　　　　　　　　　　　　
吉富 敬祐, 石坂 欣也, 押野見 一哉, 河口 蒼, Khin Khin Tha, 山本 徹, 杉森 博行, 北海道放射線技術雑誌, 80, 16, 17, Apr. 2016
(公社)日本放射線技術学会-北海道部会, Japanese

頭部非造影4D-MRAにおける早期描出能が改善された時相間可変flip angle法と固定flip angle法の比較と検討　　　　　　　　　　　　　　　
川角 恵里奈, 杉森 博行, 石坂 欣也, 藤間 憲幸, 小原 真, 北海道放射線技術雑誌, 80, 18, 19, Apr. 2016
(公社)日本放射線技術学会-北海道部会, Japanese

3D GraSE pCASLにおける至適条件の基礎的検討　　　　　　　　　　　　　　　
青池 拓哉, 水戸 寿々子, 吉富 敬祐, 高森 清華, 藤原 太郎, 石坂 欣也, 杉森 博行, 北海道放射線技術雑誌, 80, 24, 25, Apr. 2016
(公社)日本放射線技術学会-北海道部会, Japanese

Pencil beam型飽和パルスに関する基礎的検討　　　　　　　　　　　　　　　
一宇 佑太, 杉森 博行, 藤原 太郎, 石坂 欣也, 北海道放射線技術雑誌, 80, 30, 31, Apr. 2016
(公社)日本放射線技術学会-北海道部会, Japanese

Spin labeling法を用いた脳脊髄液動態イメージング法の検討 背景信号抑制の試み　　　　　　　　　　　　　　　
平山 博之, 杉森 博行, 藤間 憲幸, 奥秋 知幸, 北海道放射線技術雑誌, 80, 32, 33, Apr. 2016
(公社)日本放射線技術学会-北海道部会, Japanese

頭部領域におけるdouble inversion recovery法とphase sensitive inversion recovery法の白質描出能の比較検討　　　　　　　　　　　　　　　
野畑 圭亮, 藤原 太郎, 杉森 博行, 平山 博之, 青池 拓哉, 石坂 欣也, 北海道放射線技術雑誌, 80, 36, 37, Apr. 2016
(公社)日本放射線技術学会-北海道部会, Japanese

呼吸同期用ナビゲータパルスが1H-MRSpectroscopyの解析値に及ぼす影響の検討　　　　　　　　　　　　　　　
高森 清華, 石坂 欣也, 藪崎 哲史, 川角 恵里奈, 杉森 博行, 北海道放射線技術雑誌, 80, 40, 41, Apr. 2016
(公社)日本放射線技術学会-北海道部会, Japanese

頭部領域におけるisoFSE T1WIの再収束フリップアングルが画質に与える影響　　　　　　　　　　　　　　　
坂本 悠輔, 石坂 欣也, 青池 拓哉, 浅野 有加里, 佐藤 泰彦, 藤原 太郎, 杉森 博行, 北海道放射線技術雑誌, 80, 42, 43, Apr. 2016
(公社)日本放射線技術学会-北海道部会, Japanese

心筋におけるIntravoxel incoherent motion(IVIM)-DWIの基礎的検討　　　　　　　　　　　　　　　
杉森 博行, Wang Jeffery, 真鍋 徳子, 水戸 寿々子, 高森 清華, 藤原 太郎, 石坂 欣也, 坂田 元道, 北海道放射線技術雑誌, 80, 54, 55, Apr. 2016
(公社)日本放射線技術学会-北海道部会, Japanese

3T乳腺MRIにおける拡散尖度画像の検討　　　　　　　　　　　　　　　
加藤 扶美, 工藤 與亮, 三村 理恵, 藪崎 哲史, 坂本 圭太, 宮本 憲幸, 真鍋 徳子, 藤原 太郎, 杉森 博行, 山下 啓子, 細田 充主, Wang Jeff, 白土 博樹, Japanese Journal of Radiology, 34, Suppl., 13, 13, Feb. 2016
(公社)日本医学放射線学会, Japanese

3Tesla胸腰椎撮像 腕位置の変更による画質改善　　　　　　　　　　　　　　　
石坂 欣也, 原田 邦明, 白猪 亨, 藤原 太郎, 杉森 博行, 工藤 興亮, 日本放射線技術学会総会学術大会予稿集, 72回, 295, 295, Feb. 2016
(公社)日本放射線技術学会, Japanese

頭部非造影4D-MRAにおける早期描出能が改善された時相間可変flip angle法と固定flip angle法の比較と検討　　　　　　　　　　　　　　　
川角 恵里奈, 杉森 博行, 石坂 欣也, 水戸 寿々子, 高森 清華, 藤間 憲幸, 小原 真, 北海道放射線技術雑誌, 79, 88, 88, Oct. 2015
(公社)日本放射線技術学会-北海道支部, Japanese

Spin labeling法を用いた脳脊髄液動態イメージングにおける背景脳脊髄液信号抑制の試み　　　　　　　　　　　　　　　
平山 博之, 杉森 博行, 藤間 憲幸, 石坂 欣也, 水戸 寿々子, 高森 清華, 奥秋 知幸, 北海道放射線技術雑誌, 79, 103, 103, Oct. 2015
(公社)日本放射線技術学会-北海道支部, Japanese

Phase-sensitive inversion recovery(PSIR)法の撮像パラメータが白質・灰白質の信号強度に与える影響の検討　　　　　　　　　　　　　　　
野畑 圭亮, 藤原 太郎, 杉森 博行, 石坂 欣也, 青池 拓哉, Wang Jeff, 藤間 憲幸, 日本放射線技術学会雑誌, 71, 9, 855, 856, Sep. 2015
(公社)日本放射線技術学会, Japanese

肩腱板の拡散テンソル解析 固定肢位の違いが与える影響　　　　　　　　　　　　　　　
青野 聡, 石坂 欣也, 高森 清華, 藤原 太郎, 水戸 寿々子, 杉森 博行, 日本放射線技術学会雑誌, 71, 9, 861, 861, Sep. 2015
(公社)日本放射線技術学会, Japanese

MRI T2 mappingによる移植骨の骨強度評価の可能性　　　　　　　　　　　　　　　
遠藤 香織, 高畑 雅彦, 高橋 大介, 岩崎 倫政, 杉森 博行, Wang Jeffery Kuo-Chen, 山田 悟史, 東藤 正浩, 但野 茂, 北海道外科雑誌, 60, 1, 98, 98, Jun. 2015
北海道外科学会, Japanese, Summary national conference

解剖学的2束ACL再建術における遺残組織の温存が移植腱の成熟過程に与える効果 MRIを用いた臨床研究　　　　　　　　　　　　　　　
小野寺 純, 近藤 英司, 北村 信人, 坂本 圭太, 杉森 博行, 岩崎 倫政, 安田 和則, 日本整形外科学会雑誌, 89, 3, S1041, S1041, Mar. 2015
(公社)日本整形外科学会, Japanese

心筋perfusion撮像におけるガドリニウム造影剤が心筋信号強度に与える影響の検討
KAWASAKI TOMOHIRO, SUGIMORI HIROYUKI, MANABE TOKUKO, FUJIWARA TARO, ISHIZAKA KIN'YA, 日本放射線技術学会総会学術大会予稿集, 71st, 227, 28 Feb. 2015
Japanese

腰椎周囲組織におけるUltra short TEを用いた定量的評価の検討　　　　　　　　　　　　　　　
佐藤 泰彦, 濱口 裕行, 杉森 博行, 奥秋 知幸, 藤原 太郎, 石坂 欣也, 日本放射線技術学会総会学術大会予稿集, 71回, 145, 145, Feb. 2015
(公社)日本放射線技術学会, Japanese

リウマチ性滑膜炎の描出 従来型のコントラスト造影MRIに対する二重標識後待ち時間(PLD)測定を利用したASL画像分析法の利点(Depiction of Rheumatoid Synovitis: Advantage of ASL Imaging Analysis Using Dual Post Labeling Delay(PLD) Settings Over Conventional Contrast Enhanced MRI)　　　　　　　　　　　　　　　
Sakashita Taro, Kamishima Tamotsu, Sugimori Hiroyuki, Tou Meiki, Noguchi Atsushi, Kono Michihito, Atsumi Tatsuya, 日本放射線技術学会総会学術大会予稿集, 71回, 185, 185, Feb. 2015
(公社)日本放射線技術学会, English

画素間演算と時間強度曲線の形状分析を用いた関節リウマチにおける滑膜炎の正確な定量評価(Accurate Quantitative Assessment of Synovitis in Rheumatoid Arthritis Using Pixel by Pixel, Time-intensity Curve Shape Analysis)　　　　　　　　　　　　　　　
Sugimori Hiroyuki, Tou Meiki, Noguchi Atsushi, Kono Michihito, Atsumi Tatsuya, 日本放射線技術学会総会学術大会予稿集, 71回, 185, 185, Feb. 2015
(公社)日本放射線技術学会, English

3T MRIを用いた浸潤性乳癌の拡散尖度画像の検討 腋窩リンパ節転移予測における有用性(Diffusion kurtosis imaging at 3T MRI for invasive breast cancer: prediction of axillary lymph node metastasis)　　　　　　　　　　　　　　　
Kato Fumi, Kudo Kohsuke, Fujiwara Taro, Wang Jeff, Sugimori Hiroyuki, Yamashita Hiroko, Hosoda Mitsuchika, Mimura Rie, Miyamoto Noriyuki, Manabe Noriko, 日本医学放射線学会学術集会抄録集, 74回, S190, S191, Feb. 2015
(公社)日本医学放射線学会, English

3T心臓パーフュージョンMRIを用いた心筋血流定量法の確立(How to quantify cardiac perfusion MRI at 3 tesla in comparison with water PET)　　　　　　　　　　　　　　　
Manabe Noriko, Tomiyama Yuuki, Manabe Osamu, Katoh Chietsugu, Sugimori Hiroyuki, Kikuchi Yasuka, Kato Fumi, Kudo Kohsuke, Miyamoto Noriyuki, Nagara Tamaki, 日本医学放射線学会学術集会抄録集, 74回, S289, S289, Feb. 2015
(公社)日本医学放射線学会, English

Spin labeling法を用いた脳脊髄液動態イメージングの検討　　　　　　　　　　　　　　　
平山 博之, 杉森 博行, 藤間 憲幸, 奥秋 知幸, 日本放射線技術学会総会学術大会予稿集, 71回, 174, 174, Feb. 2015
(公社)日本放射線技術学会, Japanese

頭頸部領域におけるflip angleの至適化及び、撮像角度による脂肪抑制効果の検討　　　　　　　　　　　　　　　
川角 恵里奈, 水戸 寿々子, 藤間 憲幸, 石坂 欣也, 藤原 太郎, 高森 清華, 杉森 博行, 日本放射線技術学会総会学術大会予稿集, 71回, 225, 225, Feb. 2015
(公社)日本放射線技術学会, Japanese

局所心筋ストレイン評価におけるfast strain‐encoded法の有用性の検討
AOIKE TAKUYA, SUGIMORI HIROYUKI, MANABE TOKUKO, OKUAKI TOMOYUKI, ISHIZAKA KIN'YA, FUJIWARA TARO, MITO SUZUKO, TAKAMORI SEIKA, 日本放射線技術学会雑誌, 70, 9, 957, 20 Sep. 2014
Japanese

前立腺diffusion kurtosis imagingにおける至適b値組み合わせの検討　　　　　　　　　　　　　　　
一宇 佑太, 杉森 博行, Wang Jeff K.C, 奥秋 知幸, 加藤 扶美, 石坂 欣也, 藤原 太郎, 日本放射線技術学会雑誌, 70, 9, 1012, 1012, Sep. 2014
(公社)日本放射線技術学会, Japanese

MRI申込書記述内容と撮影プロトコルの関係の調査　　　　　　　　　　　　　　　
谷川原 綾子, 辻 真太朗, 潟端 純也, 濱口 裕行, 杉森 博行, 仲 知保, 北海道放射線技術雑誌, 77, 26, 27, Sep. 2014
(公社)日本放射線技術学会-北海道支部, Japanese

頭部double inversion recovery法における撮像パラメータが白質・脳脊髄液の信号強度に与える影響の検討　　　　　　　　　　　　　　　
野畑 圭亮, 藤原 太郎, 藤間 憲幸, 杉森 博行, 高森 清華, 石坂 欣也, 青池 拓哉, 日本放射線技術学会雑誌, 70, 9, 1046, 1046, Sep. 2014
(公社)日本放射線技術学会, Japanese

頭部拡散強調画像における生理的変動が各種拡散指標に与える影響の検討　　　　　　　　　　　　　　　
濱口 裕行, Tha Khin Khin, 杉森 博行, 中西 光広, 中川 伸, 吉田 博一, 大野 誠一郎, 田原 誠司, 日本放射線技術学会雑誌, 70, 9, 1044, 1044, Sep. 2014
(公社)日本放射線技術学会, Japanese

Diffusion weighted imaging with slice selection gradient reversal method using 3-tesla breast MRI
F. Kato, R. Mimura, K. Kudo, N. Manabe, T. Fujiwara, H. Sugimori, M. Hosoda, H. Yamashita, H. Shirato, Japanese Journal of Clinical Radiology, 59, 4, 558, 562, Apr. 2014, [Peer-reviewed]

多列検出器CTによるcanal of posterior ampullary nerve(singular nerve canal)の検出 1症例報告と文献考察(The Canal of Posterior Ampullary Nerve(Singular nerve canal) with Multi-Detector Row CT: A case report and review of literature)　　　　　　　　　　　　　　　
福屋 香菜子, 長谷川 佳菜, 天羽 浩太, 青野 聡, 杉森 博行, 中村 麻名美, 舛田 玲香, 坂田 元道, 北海道放射線技術雑誌, 76, 34, 35, Mar. 2014
(公社)日本放射線技術学会-北海道部会, Japanese

3.0T Radial scan法による頸動脈プラーク評価　　　　　　　　　　　　　　　
中村 麻名美, 真壁 武司, 本庄 俊一, 畠山 遼兵, 爰地 祐次, 杉森 博行, 坂田 元道, 北海道放射線技術雑誌, 76, 36, 37, Mar. 2014
(公社)日本放射線技術学会-北海道部会, Japanese

頸動脈black blood imageにおけるMotion Sensitized Driven Equilibrium(MSDE)を用いた時の血液抑制効果の検討　　　　　　　　　　　　　　　
田村 弘詞, 杉森 博行, 吉田 博一, 高森 清華, 濱口 裕行, 藤原 太郎, 野畑 圭亮, 北海道放射線技術雑誌, 76, 40, 41, Mar. 2014
(公社)日本放射線技術学会-北海道部会, Japanese

頭部3D-DIR法における適正delay timeの検討　　　　　　　　　　　　　　　
野畑 圭亮, 藤原 太郎, 藤間 憲幸, 杉森 博行, 濱口 裕行, 吉田 博一, 平山 博之, 北海道放射線技術雑誌, 76, 46, 47, Mar. 2014
(公社)日本放射線技術学会-北海道部会, Japanese

Ultra short echo time法を用いた脊髄靱帯評価の基礎的検討　　　　　　　　　　　　　　　
濱口 裕行, 杉森 博行, Tha Khin Khin, 吉田 博一, 藤原 太郎, 高森 清華, 野畑 圭亮, 北海道放射線技術雑誌, 76, 56, 57, Mar. 2014
(公社)日本放射線技術学会-北海道部会, Japanese

飽和パルスを用いた4-dimensional magnetic resonance angiography(4D-MRA)最適化の検討　　　　　　　　　　　　　　　
杉森 博行, 藤間 憲幸, 濱口 裕行, 藤原 太郎, 吉田 博一, 中村 麻名美, 坂田 元道, 北海道放射線技術雑誌, 76, 64, 65, Mar. 2014
(公社)日本放射線技術学会-北海道部会, Japanese

FID充填型Radial scanにおけるTrajectory Delay Timeが画質に及ぼす影響　　　　　　　　　　　　　　　
吉田 博一, 杉森 博行, 濱口 裕行, 高森 清華, 藤原 太郎, 田村 弘詞, 野畑 圭祐, 北海道放射線技術雑誌, 76, 68, 69, Mar. 2014
(公社)日本放射線技術学会-北海道部会, Japanese

膝関節軟骨のT1ρ値に関する基礎的検討　　　　　　　　　　　　　　　
平山 博之, 杉森 博行, 北海道放射線技術雑誌, 76, 74, 75, Mar. 2014
(公社)日本放射線技術学会-北海道部会, Japanese

3テスラ乳腺MRIにおけるSSGR(slice selection gradient reversal)法を用いた拡散強調像
KATO FUMI, MANABE TOKUKO, MIMURA RIE, HARADA TAISUKE, TERAE SATOSHI, FUJIWARA TARO, SUGIMORI HIROYUKI, HOSODA MICHITSUKA, TAGUCHI KAZUNORI, YAMASHITA HIROKO, SHIRATO HIROKI, Jpn J Radiol, 32, Supplement, 10, 25 Feb. 2014
Japanese

15O-水PETを用いて検証したダイナミック灌流3テスラMRIによる心筋血流量の定量 領域分析への応用(Quantification of myocardial blood flow with dynamic perfusion 3.0 Tesla MRI using validation with 15O-water PET: Application to regional analysis)　　　　　　　　　　　　　　　
Tomiyama Yuuki, Manabe Osamu, Oyama-Manabe Noriko, Kikuchi Yasuka, Sugimori Hiroyuki, Katoh Chietsugu, Tamaki Nagara, 日本放射線技術学会総会学術大会予稿集, 70回, 134, 135, Feb. 2014
(公社)日本放射線技術学会, English

手関節のリウマチ滑膜炎におけるPixel-by-Pixel TIC解析
坂下太郎, 神島保, 杉森博行, 唐明輝, 河野通仁, 渥美達也, 日本放射線技術学会雑誌, 70, 9, 2014

多時相造影MRIのTIC解析を用いた滑膜炎抽出
小野雅人, 神島保, 杉森博行, 唐明輝, 河野通仁, 渥美達也, 北海道医学雑誌, 89, 1, 2014

乳癌の画像診断update 3T‐MR mammographyの意義と展望
KATO FUMI, SUGIMORI HIROYUKI, MANABE NORIKO, KUDO KOSUKE, 臨床画像, 29, 11, 1312, 1322, 26 Nov. 2013
Japanese

頸動脈プラークイメージにおけるMotion Sensitized Driven Equilibrium(MSDE)を用いた時の血液信号抑制効果の検討　　　　　　　　　　　　　　　
田村 弘詞, 杉森 博行, 吉田 博一, 高森 清華, 濱口 裕行, 藤原 太郎, 野畑 圭亮, 北海道放射線技術雑誌, 75, 98, 98, Oct. 2013
(公社)日本放射線技術学会-北海道部会, Japanese

テキストマイニングによるMRI申込書記述内容と撮影プロトコルの関係の調査　　　　　　　　　　　　　　　
谷川原 綾子, 辻 真太朗, 潟端 純也, 濱口 裕行, 杉森 博行, 仲 知保, 北海道放射線技術雑誌, 75, 109, 109, Oct. 2013
(公社)日本放射線技術学会-北海道支部, Japanese

飽和パルスを用いた4-dimensional magnetic resonance angiography(4D-MRA)最適化の検討　　　　　　　　　　　　　　　
杉森 博行, 濱口 裕行, 藤原 太郎, 吉田 博一, 藤間 憲幸, 中村 麻名美, 坂田 元道, 北海道放射線技術雑誌, 75, 99, 99, Oct. 2013
(公社)日本放射線技術学会-北海道支部, Japanese

頭部3D-DIR法における適正delay timeの検討　　　　　　　　　　　　　　　
野畑 圭亮, 藤原 太郎, 杉森 博行, 濱口 裕行, 吉田 博一, 平山 博之, 藤間 憲幸, 北海道放射線技術雑誌, 75, 101, 101, Oct. 2013
(公社)日本放射線技術学会-北海道支部, Japanese

MRIにおいて果実・野菜が人体脳組織ファントムになり得るか?
寺本 大翼, 潮田 悠一, 佐々木 絢加, 櫻井 佑樹, 長濱 宏史, 中村 麻名美, 杉森 博行, 坂田 元道, 日本放射線技術学会雑誌, 69, 10, 1146, 1152, Oct. 2013, [Peer-reviewed]
MRIにおいて果実・野菜が人体脳組織ファントムになり得るか検討した。ボランティアの頭部を測定し、T1WIにおいて灰白質のSIは305±12、白質のSIは363±11であった。頭部側ファントムで灰白質と白質のSIを含むことができたのは11th(541±22)と12th(195±20)の間であった。T2WIでは、灰白質のSIは356±16、白質のSIは256±15となった。頭部側ファントムで灰白質と白質のSIを含むことができたのは5th(357±13)と6th(226±14)の間となった。8種類の果実・野菜のSIを測定し、T1WI、T2WIの両方にてすべてのROI位置でSIを含むことが可能であった果実・野菜はバナナであった。, (公社)日本放射線技術学会, Japanese

[Can fruits and vegetables be used as substitute phantoms for normal human brain tissues in magnetic resonance imaging?].
Daisuke Teramoto, Yuichi Ushioda, Ayaka Sasaki, Yuki Sakurai, Hiroshi Nagahama, Manami Nakamura, Hiroyuki Sugimori, Motomichi Sakata, Nihon Hoshasen Gijutsu Gakkai zasshi, 69, 10, 1146, 1152, Oct. 2013, [Peer-reviewed]
10

多列検出器CTを用いたcanal of posterior ampullary nerve(singular nerve canal)の検出(The Canal of Posterior Ampullary Nerve(Singular nerve canal) with Multi-Detector Row CT: A case report and review of literature)　　　　　　　　　　　　　　　
福屋 香菜子, 長谷川 佳菜, 青野 聡, 天羽 浩太, 中村 麻名美, 杉森 博行, 坂田 元道, 北海道放射線技術雑誌, 75, 111, 111, Oct. 2013
(公社)日本放射線技術学会-北海道部会, Japanese

3.0T乳腺MRIにおけるB1均一性に関する検討　　　　　　　　　　　　　　　
藤原 太郎, 吉田 博一, 高森 清華, 濱口 裕行, 杉森 博行, 水戸 寿々子, 加藤 扶美, 北海道放射線技術雑誌, 75, 125, 125, Oct. 2013
(公社)日本放射線技術学会-北海道部会, Japanese

FID充填型Radial scanにおけるTrajectory Delay Timeが画質に及ぼす影響　　　　　　　　　　　　　　　
吉田 博一, 杉森 博行, 濱口 裕行, 高森 清華, 藤原 太郎, 田村 弘詞, 野畑 圭亮, 北海道放射線技術雑誌, 75, 130, 130, Oct. 2013
(公社)日本放射線技術学会-北海道部会, Japanese

ASL法を用いた腎血流量算出におけるパラメータが定量値に与える影響の検討
SUGIMORI HIROYUKI, WANG JEFF, OKUAKI TOMOYUKI, HAMAGUCHI HIROYUKI, YOSHIDA HIROKAZU, FUJIWARA TARO, SAKATA MOTOMICHI, MANABE NORIKO, 日本放射線技術学会雑誌, 69, 9, 1105, 20 Sep. 2013
Japanese

膝関節靱帯におけるUltra short TEを用いた定量的評価の検討　　　　　　　　　　　　　　　
吉田 博一, 杉森 博行, Wang Jeff, 奥秋 知幸, 谷川原 綾子, 濱口 裕行, 藤原 太郎, 坂本 圭太, 日本放射線技術学会雑誌, 69, 9, 1040, 1040, Sep. 2013
(公社)日本放射線技術学会, Japanese

[Optimal scan parameters for a method of k-space trajectory (radial scan method) in evaluation of carotid plaque characteristics].
Manami Nakamura, Takeshi Makabe, Hideomi Tezuka, Takahiro Miura, Takuma Umemura, Hiroyuki Sugimori, Motomichi Sakata, Nihon Hoshasen Gijutsu Gakkai zasshi, 69, 4, 407, 412, Apr. 2013, [Peer-reviewed]
4

負荷心筋perfusion時の造影剤が心筋T₁値に及ぼす影響の検討
SUGIMORI HIROYUKI, YOSHIDA HIROKAZU, YAGAHARA AYAKO, HAMAGUCHI HIROYUKI, FUJIWARA TARO, MANABE TOKUKO, SAKATA MOTOMICHI, 北海道放射線技術雑誌, 74, 74, 22, 23, 29 Mar. 2013
(公社)日本放射線技術学会-北海道支部, Japanese

3.0T装置を用いた乳腺DWIにおけるSSGR法の基礎的検討　　　　　　　　　　　　　　　
藤原 太郎, 杉森 博行, 加藤 扶美, 北海道放射線技術雑誌, 74, 24, 25, Mar. 2013
(公社)日本放射線技術学会-北海道部会, Japanese

DTI撮像における撮像体位の影響　　　　　　　　　　　　　　　
濱口 裕行, 高森 清華, 谷川原 綾子, 水戸 寿々子, 杉森 博行, 石坂 欣也, Tha Khin Khin, 北海道放射線技術雑誌, 74, 26, 27, Mar. 2013
(公社)日本放射線技術学会-北海道支部, Japanese

Direct-coronal拡散強調画像における画像歪みについての基礎的検討　　　　　　　　　　　　　　　
田村 弘詞, 高森 清華, 石坂 香織, 濱口 裕行, 藤原 太郎, 谷川原 綾子, 杉森 博行, 北海道放射線技術雑誌, 74, 30, 31, Mar. 2013
(公社)日本放射線技術学会-北海道支部, Japanese

Slice selection gradient reversal法を用いた3T乳腺MRIにおける拡散強調像の画像評価
KATO FUMI, MIMURA RIE, FUJIWARA TARO, MANABE NORIKO, SUGIMORI HIROYUKI, HOSODA MICHITSUKA, TAGUCHI KAZUNORI, YAMASHITA HIROKO, TERAE SATOSHI, SHIRATO HIROKI, 日本医学放射線学会総会抄録集, 72nd, S356, 28 Feb. 2013
Japanese

Laterality of the Corticospinal Tract and the Influence of Handedness : Findings of a DTI Study
THA Khin Khin, TERAE Satoshi, HAMAGUCHI Hiroyuki, ISHIZAKA Kinya, POPY Kawser Akter, HIROTANI Makoto, SUGIMORI Hiroyuki, FUJIMA Noriyuki, YOSHIDA Atsushi, MINOWA Kazuyuki, SUZUKI Yuriko, SHIRATO Hiroki, 日本磁気共鳴医学会雑誌, 33, 1, 33, 34, 15 Feb. 2013
(一社)日本磁気共鳴医学会, English

頸動脈血管壁画像における並行画像を利用したradial scan法のためのflow void現象の評価(Evaluation of the flow void effect for radial scan method with parallel imaging in carotid vessel wall imaging)　　　　　　　　　　　　　　　
Nakamura Manami, Makabe Takeshi, Kobayashi Masami, Tezuka Hideomi, Sugimori Hiroyuki, Sakata Motomichi, 日本放射線技術学会総会学術大会予稿集, 69回, 165, 165, Feb. 2013
(公社)日本放射線技術学会, English

Gd-EOB-DTPA造影剤を用いた肝臓のT1短縮効果と肝機能指標との比較　　　　　　　　　　　　　　　
高森 清華, 杉森 博行, 濱口 裕行, 谷川原 綾子, 石坂 香織, 藤原 太郎, 田村 弘詞, 日本放射線技術学会総会学術大会予稿集, 69回, 141, 141, Feb. 2013
(公社)日本放射線技術学会, Japanese

Small FOV Imagingにおける画質の基礎的検討　　　　　　　　　　　　　　　
濱口 裕行, 杉森 博行, キンキンタ, 高森 清華, 谷川原 綾子, 藤原 太郎, 吉田 博一, 日本放射線技術学会総会学術大会予稿集, 69回, 300, 300, Feb. 2013
(公社)日本放射線技術学会, Japanese

Gd造影剤および塩化マンギン四水和物を用いたファントム作成における基礎的検討　　　　　　　　　　　　　　　
吉田 博一, 田村 弘詞, 石坂 香織, 杉森 博行, 北海道放射線技術雑誌, 73, 123, 123, Nov. 2012
(公社)日本放射線技術学会-北海道部会, Japanese

DTI撮像における撮像本位の影響　　　　　　　　　　　　　　　
濱口 裕行, 高森 清華, 谷川原 綾子, 杉森 博行, 石坂 欣也, 水戸 寿々子, Tha Khin Khin, 北海道放射線技術雑誌, 73, 105, 105, Nov. 2012
(公社)日本放射線技術学会-北海道支部, Japanese

Direct-coronal収集での拡散強調画像における画像歪みについての基礎的検討　　　　　　　　　　　　　　　
田村 弘詞, 高森 清華, 石坂 香織, 濱口 裕行, 藤原 太郎, 谷川原 綾子, 杉森 博行, 北海道放射線技術雑誌, 73, 124, 124, Nov. 2012
(公社)日本放射線技術学会-北海道支部, Japanese

3.0T装置を用いた乳腺DWIにおけるSSGR法の基礎的検討　　　　　　　　　　　　　　　
藤原 太郎, 田村 弘詞, 谷川原 綾子, 濱口 裕行, 杉森 博行, 加藤 扶美, 北海道放射線技術雑誌, 73, 129, 129, Nov. 2012
(公社)日本放射線技術学会-北海道支部, Japanese

負荷心筋perfusion時の造影剤が心筋T1値に及ぼす影響の検討　　　　　　　　　　　　　　　
杉森 博行, 吉田 博一, 谷川原 綾子, 濱口 裕行, 藤原 太郎, 真鍋 徳子, 坂田 元道, 北海道放射線技術雑誌, 73, 130, 130, Nov. 2012
(公社)日本放射線技術学会-北海道支部, Japanese

¹⁵O‐H₂O心筋PETを用いたMRI perfusion imageによる心筋血流定量法の評価
TOMIYAMA YUKI, KATO CHIETSUGU, MANABE OSAMU, MANABE NORIKO, SUGIMORI HIROYUKI, YOSHINAGA KEIICHIRO, 日本放射線技術学会総会学術大会予稿集, 68th, 181, 29 Feb. 2012
Japanese

3T MRIによる冠血流評価—O‐15 labeled water PETとの比較—
真鍋治, 大山徳子, 杉森博行, 吉永恵一郎, 寺江聡, 玉木長良, 日独医報, 56, 2, 261, 261, 20 Dec. 2011
バイエル薬品(株), Japanese

肺高血圧患者におけるMRI右心機能評価
大山徳子, 杉森博行, 佐藤隆博, 大平洋, 辻野一三, 後藤大祐, 玉木長良, 寺江聡, 日独医報, 56, 2, 260, 260, 20 Dec. 2011
バイエル薬品(株), Japanese

3T MRI心臓perfusion撮像におけるmulti‐transmit RFの有用性に関する検討
杉森博行, 大山徳子, 真鍋治, 小原真, 玉木長良, 坂田元道, 寺江聡, 日独医報, 56, 2, 261, 20 Dec. 2011
Japanese

3T‐MRIにおける送信RFが画像に及ぼす影響の検討
杉森博行, 中西光広, 石坂欣也, 濱口裕行, 水戸寿々子, 藤原太郎, 坂田元道, 北海道放射線技術雑誌, 71, 119, 25 Oct. 2011
Japanese

3 Teslaにおける頭部用coilの比較検討
濱口裕行, 杉森博行, 中西光広, 石坂欣也, 水戸寿々子, 藤原太郎, 仲知保, 北海道放射線技術雑誌, 71, 119, 25 Oct. 2011
Japanese

ALCAPA Syndrome (Bland-White-Garland Syndrome)
SASAKI KOJI, KIKUCHI MASANORI, TAKAHASHI TOSHIMITSU, ONO NAOMI, SUGIMORI HIROYUKI, SAKATA MOTOMICHI, 北海道放射線技術雑誌, 71, 23, 25, 25 Oct. 2011
Japanese

A Coarctation of the Aorta: Three-dimensional Imaging
SAKATA MOTOMICHI, SUGIMORI HIROYUKI, KAWASUMI ERINA, NAGAHAMA HIROSHI, SAKURAI YUKI, 北海道放射線技術雑誌, 71, 19, 22, 25 Oct. 2011
Japanese

3T乳腺MRIにおいてMulti Transmitシステムが乳腺の信号強度及び造影効果に与える影響
水戸寿々子, 石坂欣也, 中西光広, 杉森博行, 濱口裕行, 藤原太郎, 北海道放射線技術雑誌, 71, 120, 25 Oct. 2011
Japanese

The Role of MDCT in the Evaluation of Aortic Mechanical Valve: case report
EHARA KENSUKE, IKUTA YASUTAKA, SHIRAI NOBUMASA, ONO HAJIME, SUGIMORI HIROYUKI, SAKATA MOTOMICHI, 北海道放射線技術雑誌, 71, 11, 14, 25 Oct. 2011
Japanese

Ossification of the stapedial muscle Demonstration with three dimensional computed tomography
SAKATA MOTOMICHI, SUGIMORI HIROYUKI, KAWASUMI ERINA, 北海道放射線技術雑誌, 71, 15, 17, 25 Oct. 2011
Japanese

MOVIE‐STAR法による頭部非造影4D‐MRDSAの試み
中西光広, 杉森博行, 石坂欣也, 濱口裕行, 水戸寿々子, 藤原太郎, 北海道放射線技術雑誌, 71, 114, 25 Oct. 2011
Japanese

Look‐lockerシーケンスを用いた心筋T₁‐mapにおける基礎的検討
杉森博行, 真鍋徳子, 中西光広, 石坂欣也, 水戸寿々子, 濱口裕行, 坂田元道, 日本放射線技術学会雑誌, 67, 9, 1086, 20 Sep. 2011
Japanese

3T‐心筋perfusionにおけるMulti‐transmit RFが信号強度に与える影響の検討
杉森博行, 大山徳子, 中西光広, 石坂欣也, 水戸寿々子, 濱口裕行, 坂田元道, 日本放射線技術学会総会学術大会予稿集, 67th, 166, 167, 25 Feb. 2011
Japanese

Multi Transmitを用いた3T乳腺MRIにおける造影効果の検討
水戸寿々子, 石坂欣也, 加藤扶美, 杉森博行, 中西光宏, 白土博樹, 寺江聡, 日本放射線技術学会総会学術大会予稿集, 67th, 244, 25 Feb. 2011
Japanese

Comparison of Fat Suppression Techniques of Bilateral Breast Dynamic Sequence at 3.0 T: Utility of Three-point DIXON Technique
MITO SUZUKO, ISHIZAKA KIN'YA, NAKANISHI MITSUHIRO, SUGIMORI HIROYUKI, HAMAGUCHI HIROYUKI, TSUZUKI TOMOYASU, 日本放射線技術学会雑誌, 67, 6, 654, 660, 2011
Japanese

Arterial spin labeling(ASL)法を用いたMRIでの腎血流量評価の試み
杉森博行, 中西光広, 濱口裕行, 石坂欣也, 水戸寿々子, 坂田元道, 日本放射線技術学会雑誌, 66, 9, 1057, 1058, 20 Sep. 2010
Japanese

3テスラにおけるMulti Transmitを用いた4D‐TRAKの検討
濱口裕行, 中西光広, 杉森博行, 石坂欣也, 水戸寿々子, 田村弘詞, 横山英辰, 仲知保, 日本放射線技術学会雑誌, 66, 9, 1123, 20 Sep. 2010
Japanese

頚動脈plaque ImageにおけるTRが画像におよぼす影響について
中西光広, 大山徳子, 杉森博行, 石坂欣也, 濱口裕行, 水戸寿々子, 日本放射線技術学会総会学術大会予稿集, 66th, 108, 26 Feb. 2010
Japanese

MRIを用いた心筋Strain解析におけるStrain‐Encoded(SENC)法の有用性についての検討
杉森博行, 大山徳子, 石坂欣也, 中西光広, 水戸寿々子, 濱口裕行, 坂田元道, 日本放射線技術学会総会学術大会予稿集, 66th, 125, 26 Feb. 2010
Japanese

e‐THRIVE法におけるhalf scanが画質に与える影響について
石坂欣也, 中西光広, 杉森博行, 濱口裕行, 水戸寿々子, 佐賀和高, 仲知保, 日本放射線技術学会雑誌, 65, 9, 1222, 1223, 20 Sep. 2009
Japanese

SSFPシーケンスを用いた心筋taggingにおける基礎的検討
杉森博行, 石坂欣也, 中西光広, 濱口裕行, 坂田元道, 日本放射線技術学会雑誌, 65, 9, 1271, 20 Sep. 2009
Japanese

Optimization of Dark-blood T2-weighted Sequence in Myocardium Magnetic Resonance Imaging
SUGIMORI Hiroyuki, UNO Takahiro, YANAGISAWA Akira, Japanese Journal of Radiological Technology, 65, 5, 612, 619, 20 May 2009
The evaluation of myocardium characterization is an important role in cardiac MRI. The dark-blood (DB) sequence is used for non-enhanced T2-weighted myocardial images. The purpose of this study was to evaluate and optimize the DB T2WI sequence in myocardium images. We changed the parameters TE, echo train length (ETL), inversion time (TI), and fat suppression method, respectively. Data acquisition with end-diastolic phase was effective for avoiding the blur that was caused by cardiac motion. Consequently, the optimal setting of ETL and TI was important for myocardial images. STIR was suitab..., Japanese Society of Radiological Technology, Japanese

Ｉ−123甲状腺ＳＰＥＣＴを用いたｖｏｌｕｍｅｔｒｙの検討
宇野 貴寛, 佐藤 順一, 杉森 博行, 高橋 敬一, 旭川放射線技師会会誌, 30, 41, 44, 2008
雑誌掲載版, Japanese

365 I-123甲状腺SPECTを用いたvolumetryの検討(核医学検査撮像技術・他, 第35回日本放射線技術学会秋季学術大会プログラム)
宇野 貴寛, 佐藤 順一, 杉森 博行, 高橋 敬一, 日本放射線技術學會雜誌, 63, 9, 1074, 1074, 20 Sep. 2007
公益社団法人日本放射線技術学会, Japanese

240 OS-EM再構成におけるSPECT収集中の放射能変化による影響(核医学検査SPECT・画像再構成, 第35回日本放射線技術学会秋季学術大会プログラム)
佐藤 順一, 宇野 貴寛, 杉森 博行, 高橋 敬一, 秀毛 範至, 沖崎 琢貴, 佐々木 智章, 油野 民雄, 日本放射線技術學會雜誌, 63, 9, 1045, 1045, 20 Sep. 2007
公益社団法人日本放射線技術学会, Japanese

256 Tc-99mGSAを用いた肝術前予備能評価におけるCT-anatomical MAPの有用性(核医学検査Fusion, 第35回日本放射線技術学会秋季学術大会プログラム)
杉森 博行, 佐藤 順一, 宇野 貴寛, 日本放射線技術學會雜誌, 63, 9, 1049, 1050, 20 Sep. 2007
公益社団法人日本放射線技術学会, Japanese

Usefulness of Phase-sensitive Inversion Recovery in Delayed Enhanced Cardiac MRI
Sugimori Hiroyuki, Sakamoto Naka, Natori Shunsuke, Yanagisawa Akira, Uno Takahiro, Kubota Makoto, Japanese Journal of Radiological Technology, 63, 6, 661, 666, 20 Jun. 2007
[Background] Delayed-enhancement MRI is a technique that has significant clinical usefulness, particularly for myocardial viability determination in ischemic heart disease. Delayed enhanced images have been acquired by using the inversion recovery (IR) method. It is necessary for the IR method to select optimal inversion time (TI). Recently, the phase-sensitive inversion recovery (PSIR) method has been developed to detect Gd-DTPA enhanced myocardium. [Purpose] To compare the IR method with the PSIR method by acquiring Gd-DTPA solution phantoms A (0.05 mmol/l) and B (0.04 mmol/l) in various ..., Japanese Society of Radiological Technology (JSRT), Japanese

Assessment of the Normal Gastrointestinal Tract of Fetal MRI Using 3D-Gradient Echo Sequence : A Preliminary Study
SATO Hiroaki, INAOKA Tsutomu, TAKAHASHI Koji, YAMADA Tomonori, NAGASAWA Kenichi, HIRANUMA Hatsune, YAMAKI Toshihiro, SUGIMORI Hiroyuki, SASAKI Yoshihito, NAKAMURA Eiki, ABURANO Tamio, 日本小児放射線学会雑誌 = Journal of Japanese Society of Pediatric Radiology, 23, 1, 19, 24, 15 Feb. 2007
Japanese

ＨＡＳＴＥを用いた非造影ＭＲＡにおける下肢血管描出の検討
宇野 貴寛, 杉森 博行, 柳澤 亨, 増田 憲昭, 旭川放射線技師会会誌, 29, 38, 40, 2007
雑誌掲載版, Japanese

24 SPECTにおける分解能補正(DRC)法の検討(核医学検査 分解能・減弱補正,一般研究発表,日本放射線技術学会 第34回秋季学術大会)
佐藤 順一, 杉森 博行, 高橋 敬一, 秀毛 範至, 沖崎 貴琢, 油野 民雄, 吉岡 克則, 横井 孝司, 日本放射線技術學會雜誌, 62, 9, 1198, 1198, 20 Sep. 2006
公益社団法人日本放射線技術学会, Japanese

105 胎児腸管描出における脂肪抑制併用3D-flashの有用性の検討(MR検査 腹部(2),一般研究発表,日本放射線技術学会 第34回秋季学術大会)
杉森 博行, 宇野 貴寛, 柳澤 亨, 窪田 誠, 佐藤 宏朗, 稲岡 努, 日本放射線技術學會雜誌, 62, 9, 1217, 1218, 20 Sep. 2006
公益社団法人日本放射線技術学会, Japanese

心筋遅延造影MRIにおけるシーケンスの比較〜IR-TurboFlashとIR-TrueFISPについて〜
杉森 博行, 柳澤 亨, 山田 裕樹, 窪田 誠, 北海道放射線技術雑誌, 66, 7, 12, Jul. 2006
出版社版心筋遅延造影MRI用シーケンスとしてInversion Recovery付加Turbo Flash(IR-Turbo Flash)とInversion Recovery付加True FISP(IR-True FISP)の2種類を用い撮像を行っている.今回,自作ファントムを作成し,2種類のシーケンスを用いて撮像を行い,それぞれのシーケンスにおける有用性について検討した.心筋遅延造影MRIにおいて,IR-Turbo Flashを用いて撮像することによりCNRの高い画像を得ることができた.IR-Turbo Flashの方がIR-TrueFISPよりCNRが高くなったが,性質が異なるシーケンスを用いているため,パラメータの検討および使い分けの検討を行った.IR-True FISPはSegment数の変化によって正常心筋のNullポイントおよびコントラストに変化がないため,息止め困難な患者において有用であることが示唆された, Japanese

ガドリニウム造影MRIが有用であった心サルコイドーシスの一例(第93回日本循環器学会北海道地方会)
坂本 央, 菅野 貴康, 田代 直彦, 八巻 多, 藤野 貴行, 竹原 有史, 田邊 康子, 竹内 利治, 佐藤 伸之, 川村 祐一郎, 長谷部 直幸, 菊池 健次郎, 名取 俊介, 長沢 研一, 油野 民雄, 杉森 博行, Circulation journal : official journal of the Japanese Circulation Society, 69, 947, 947, 20 Oct. 2005
社団法人日本循環器学会, Japanese

肘関節
稲岡 努, 高橋 康二, 杉森 博行, 油野 民雄, 後山 恒範, 松野 丈夫, 臨床スポーツ医学 = The journal of clinical sports medicine, 21, 6, 666, 675, 01 Jun. 2004
文光堂, Japanese

フルカラーCGで学ぶ X線撮影のポジショニングとテクニック−改訂第2版
著, 杉森 博行, 監修, 神島 保
メジカルビュー社, 03 Feb. 2026, 4758322759, 256, [Single work]

新・医用放射線科学講座 X線撮影機器学
齋藤, 茂芳, 林, 則夫, 石田, 隆行, (担当:共著, 範囲:第4章 X線機械装置), (担当:共著, 範囲:第4章 X線機械装置)
医歯薬出版, 03 Mar. 2025, 4263206541, 234, Japanese

MR撮像技術学(改訂4版) (放射線技術学シリーズ)
日本放射線技術学会, 齋藤 茂芳, 第3章 傾斜磁場・k空間・画像再構成
オーム社, 19 Oct. 2024, 427423262X, 552, Japanese, [Joint work]

基礎からわかる！実践できる！ 造影検査学
著, 杉森 博行, 監修, 神島 保
メジカルビュー社, 29 Dec. 2022, 4758320748, 252, Japanese, [Single work]

Dr.のほしい画像が撮れる! オーダーの解釈と撮影・撮像技術 脳・頭頸部・脊椎・脊髄
(監修)工藤 與亮, (編集)杉森 博行
メジカルビュー社, 03 Sep. 2022, 4758320357, 304, Japanese, [Editor]

Dr.のほしい画像が撮れる! オーダーの解釈と撮影・撮像技術 四肢・体幹
(監修)工藤 與亮, (編集)杉森 博行
メジカルビュー社, 02 Aug. 2021, 4758320349, 472, Japanese, [Editor]

図解診療放射線技術実践ガイド 第4版
遠藤, 啓吾, 杜下, 淳次, 小倉, 明夫, 片渕, 哲朗, 赤澤, 博之, 西谷, 源展, 第1章 P335-338
文光堂, 04 Feb. 2020, 4830642327, 1044, Japanese, [Contributor]

フルカラーCGで学ぶ MR撮像のポジショニングとテクニック
著, 杉森 博行, 監修, 神島 保
メジカルビュー社, 03 Aug. 2019, 4758319340, 234, Japanese, [Single work]

Radiographic Positioning and Techniques with Full-color Computer Graphics
Hiroyuki Sugimori, 256pages
MEDICAL VIEW CO., LTD., 30 Dec. 2017, 4758319073, 256, Japanese, [Single work]

診療放射線技師 ポケット・レビュー帳
福士, 政広, 第6章 P186-240
メジカルビュー社, 27 Mar. 2017, 4758317267, 425, Japanese, [Contributor]

Practical medical imaging　　　　　　　　　　　　　　　
Hokkaido University
Apr. 2024 - Present

Magnetic Resonance in Medicine Ⅰ　　　　　　　　　　　　　　　
Hokkaido University
Apr. 2022 - Present

臨床画像技術学特論演習　　　　　　　　　　　　　　　
北海道大学大学院保健科学院
Apr. 2019 - Present

臨床画像技術学特論　　　　　　　　　　　　　　　
北海道大学大学院保健科学院
Apr. 2019 - Present

医用画像科学特講演習　　　　　　　　　　　　　　　
北海道大学大学院保健科学院
Apr. 2019 - Present

医用画像科学特講　　　　　　　　　　　　　　　
北海道大学大学院保健科学院
Apr. 2019 - Present

基礎撮影技術学実習　　　　　　　　　　　　　　　
北海道大学
Apr. 2016 - Present

臨床画像解剖学Ⅰ　　　　　　　　　　　　　　　
北海道大学
Apr. 2016 - Present

臨床撮影技術学Ⅱ　　　　　　　　　　　　　　　
北海道大学
Apr. 2016 - Present

臨床撮影技術学Ⅰ　　　　　　　　　　　　　　　
北海道大学
Apr. 2016 - Present

臨床画像技術学　　　　　　　　　　　　　　　
北海道大学
Apr. 2016 - Present

遠隔医療のためのAIを用いたリンパ浮腫診断支援ツールの開発
科学研究費助成事業
01 Apr. 2025 - 31 Mar. 2028
小林 範子, 杉森 博行
日本学術振興会, 基盤研究(C), 北海道大学, 25K13857

Elucidation of Water Dynamics in the Glymphatic System by Stable Isotope Water Molecule Imaging
Grants-in-Aid for Scientific Research
01 Apr. 2024 - 31 Mar. 2027
工藤 與亮, 小畠 隆行, 小牧 裕司, 杉森 博行, 亀田 浩之, 安井 正人
Japan Society for the Promotion of Science, Grant-in-Aid for Scientific Research (B), Hokkaido University, 24K02388

AIによる進化的知識更新を有したクラウドベースの診断補助システムの開発
科学研究費助成事業
01 Apr. 2024 - 31 Mar. 2027
杉森 博行
日本学術振興会, 基盤研究(C), 北海道大学, 24K13333

糖尿病熟練看護師の臨床判断に基づいたAIによる療養指導サポートシステムの検討
科学研究費助成事業
01 Apr. 2023 - 31 Mar. 2027
吉田 祐子, 杉森 博行, 中村 典雄, 冨澤 登志子
日本学術振興会, 基盤研究(C), 札幌保健医療大学, 23K09843

術中ヒヤリハットを未然に防止する脳神経外科手術支援AI
科学研究費助成事業
Apr. 2024 - Mar. 2027
伊藤 康裕, 杉山 拓, 杉森 博行, 唐 明輝, 伊東 雅基, 小笠原 克彦
日本学術振興会, 基盤研究(C), 北海道大学, 24K15785

Deep phenotyping toward the precision medicine for chronic airway diseases
Grants-in-Aid for Scientific Research
01 Apr. 2023 - 31 Mar. 2026
今野 哲, 村上 正晃, 杉森 博行, 清水 薫子, 木村 孔一, 鈴木 雅
本研究は、慢性閉塞性肺疾患と気管支喘息はともに慢性気道炎症と気流閉塞を特徴とする慢性気道疾患であるが、歴史的には別個の疾患として扱われ、両疾患における病態解析ならびに治療法のエビデンスは互いの疾患を除外した形で積み重ねられてきた。一方で、特に高齢化社会を迎えた本邦における実臨床では両疾患を明確に区別できない症例が多く存在し、このような症例に対する診療エビデンスは明らかに不足している。本研究では、臨床情報・呼吸生理学的所見・CT画像所見・血中バイオマーカー・末梢血T細胞表面マーカー等の高次元データを用いて慢性気道疾患患者の既存の疾患名にとらわれない精細な表現型解析(deep phenotyping)を行い、その基盤となる分子・免疫病態ならびに至適な治療戦略の同定を目指すことを目的とする。


閉塞性肺疾患患者の日常モニタリングの一つとして、PRF（ピークフロー）モニタリングがあるが、令和5年度においては、日々のPEFモニタリングのデータを用い、PEFの変化が数日後の増悪を予測することを明らかにし、英語論文発表をおこなった（Yang Y, et al.)。これは、閉塞性疾患のphenotyoeの一つとして、増悪をきたしやすい対象の選定に役立つものと思われる。


令和5年度じゅうには、閉塞性疾患のEndtype同定の為、血清、喀痰上清中のバイオマーカーの一部の測定を終了した。本年度は、経過中の検体も含め、末梢血T細胞表面マーカー、microRNAを含む網羅的測定を予定している。更には、ベースラインで得られた胸部CT画像に対して3次元評価を行い、肺気腫病変（低吸収域体積割合）、気道（気道内腔面積・気道壁面積割合）、肺内血管（断面積が5mm2あるいは10mm2未満の肺血管容量および総肺血管体積に対する割合）の定量を深層学習を含めて行う。
Japan Society for the Promotion of Science, Grant-in-Aid for Scientific Research (B), Hokkaido University, 23K27606

Multiple advanced video analysis to elucidate the 'complexity' of microsurgery
Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (C)
01 Apr. 2021 - 31 Mar. 2026
杉山 拓, 杉森 博行, 松澤 等, 小笠原 克彦, 藤村 幹, 伊東 雅基
本研究の目的は、外科手術の機能や安全性、術者スキルに関わる重要な要素を探索することであり、この先にアウトカム予測、有害イベント予測、術者スキル評価AIなどを目指すものである。
令和3年度は、頚動脈狭窄症に対する動脈内膜剥離術に焦点を当て、手術映像の解析に着手した。頚動脈を剥離する際の、頚動脈の動き（加速度）に着目し、これを手術映像から測定することで、“組織に対する愛護的な手術操作”の新たな指標と仮定した。１１７例の頚動脈内膜剥離術中映像の網羅的解析により、この新たな指標が、手術スキルおよび手術合併症に相関することが証明された。また、この指標を用いることにより、どの様な手術剥離法が客観的に有用であるか（組織に対して愛護的であるか）を示すことが可能になった。さらには、このような手術パフォーマンスの指標が、従来の研究で多く用いられてきた患者側の指標と同等以上に、治療成績にも相関することが多変量解析の結果からも証明し得た。本研究結果を、現在英語論文として投稿準備中である。
また、微小脳血管吻合のトレーニング映像を用いて、術具の先端を自動追跡する深層学習アルゴリズム、手術操作の対象となる微小血管をセグメンテーションするアルゴリズムの作成を開始し、おおむね精度の高いアルゴリズムが形成されてきている。これらを用いて、術具の軌道分析や、患者組織の変形分析などを行い、術者レベルや血管吻合成否に関与する因子の網羅的探索を継続している。
Japan Society for the Promotion of Science, Grant-in-Aid for Scientific Research (C), Hokkaido University, 21K09091

Elucidating the pathogenesis of childhood-onset pulmonary hypertension through multifaceted pathological investigation based on deep learning
Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (C)
01 Apr. 2022 - 31 Mar. 2025
永井 礼子, 武田 充人, 正木 直樹, 高桑 恵美, 杉森 博行, 平田 健司, 齋木 佳克
Japan Society for the Promotion of Science, Grant-in-Aid for Scientific Research (C), Hokkaido University, 22K08225

Multi-scale Imaging of Water Molecules using MRI and Isotope Microscope
Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B)
01 Apr. 2021 - 31 Mar. 2024
工藤 與亮, 亀田 浩之, 杉森 博行, 村上 正晃, 坂本 直哉, 小牧 裕司, 小畠 隆行, 安井 正人
①MRI撮像法開発：O-17標識水の存在によるT2値の短縮を定量的に計測してO-17濃度を定量解析するため、プリパルスを利用した高速T2 mapping法を開発して最適化を行った。異なる濃度のO-17標識水を含有した濃度ファントムを作成し、高速T2 mapping法と従来のFSE法によるT2 mapping法の精度を比較した。従来法と比較して高精度のT2値測定が可能となった。
②正常動物・疾患モデル動物でのMRI撮像：正常マウスやラットにてO-17標識水の静脈内投与法や髄腔内投与法、頸動脈内投与法、腹腔内投与法などを確立した。静脈内投与や頸動脈内投与によって脳内の有意なMRI信号変化を確認した。水中毒モデルラットにO-17標識水を腹腔内投与してMRI撮像を行い、AQP4欠損ラットとの比較を行った。AQP4欠損によって脳内の水貯留が増加することが明らかになった。ALSモデルマウス・ラットにてO-17標識水を静脈内投与してMRI撮像を行った。野生型と比較して錐体路での水漏出が増加していることが明らかとなった。
③同位体顕微鏡による水分子イメージング：新たに導入した多機能コーティング装置を用いて凍結下での標本作成から同位体顕微鏡によるイメージングまでの解析手順を確立した。ラット脳にO-18標識水を直接注入し、注入部位でのO-18濃度の上昇を確認した。摘出したラット肝の門脈内にO-18標識水を注入し、血管内や類洞内のO-18濃度の上昇を確認した。
④ヒトでのMRI撮像：認知症患者を対象にしたO-17標識水の髄腔内投与研究にて、特発性正常圧水頭症患者とアルツハイマー型認知症患者で、髄腔内の水吸収速度に差があることを見出した。
Japan Society for the Promotion of Science, Grant-in-Aid for Scientific Research (B), Hokkaido University, 21H02857

AI技術によるインスリン自己注射管理指導のためのエコーシステムの開発
科学研究費助成事業 基盤研究(C)
01 Apr. 2021 - 31 Mar. 2024
菊地 実, 永瀬 晃正, 吉田 祐子, 杉森 博行
当該年度の研究成果は、コロナ禍の影響で計画どおりには進まなかったが、AIによるエコー画像の判読システムに使用するハードウェアの選定とその性能評価、研究テーマの関連疾患についての学会報告を行った。本研究に必要な主なハードウェアは、超音波診断装置とAIを起動させ画像判読を行うためのパソコンが対象である。超音波診断装置については、携帯性、拡張性に優れ、皮膚領域における画像表示能力も高いものを基準に選定した。表在組織が観察可能なタブレット画像表示が可能なG社とP者の２機種を候補としカタログによる性能と拡張面を比較評価し、画像表示性能は超音波精度評価用ファントムにて評価した。総合的に評価した結果、G社の機器が本研究目的を果たすに適すると判断した。G社機器の納入見積を取り寄せ研究計画の経費内であることも確認した。
AIを起動させ超音波画像を取り込みするパソコンは、携帯性に優れるものを基準に選定したが、ノート型やタブレット型など該当すると思われる機種が多数あるためカタログ収集とかカタログ記載事項による性能評価を行った。AIシステムの起動条件としてOSがWINDOWSに限られること、画像を扱うためメモリーが比較的容量の大きいもの基準に選定を進めているところである。
学会報告については、日本超音波医学会北海道地方会において研究テーマと関連するインスリン由来アミロイドーシスのエラストグラフィーによる評価を発表し、この病態における超音波画像診断の有用性についてを認識してもらうための活動を行った。
日本学術振興会, 基盤研究(C), 北海道大学, 21K10568

Development of a Prognostic Method Integrating AI and Radiomics Analysis in Cerebral Ischemic Lesions
Grants-in-Aid for Scientific Research
Apr. 2021 - Mar. 2024
杉森 博行
虚血性脳血管障害における画像データに対し人工知能技術を用いて検出しRadiomics解析による高次元特徴量を複合した予後予測を行うという目的において、当該年度は「脳虚血病変検出処理方法の確立」と「機械学習エンジン構築手法の確立」に取り組んだ。画像診断補助の基礎となる脳虚血病変の検出に関しては拡散強調画像とFLAIR画像を用い、病変断面の検出と病変領域の特定方法について検討を行った。
複数断面ある脳画像からまず脳虚血病変の有無を判別させるため、拡散強調画像とFLAIR画像のそれぞれにおいて、画像分類器を作成し病変有無を判別した。また、セマンティックセグメンテーション技術を用いて脳虚血病変のみを領域分割し抽出する検出モデルを作成した。次に検出された病変の信号強度を評価するためにセマンティックセグメンテーションで検出された領域に対して対側の脳領域に同サイズの関心を自動設定する仕組みを実装し、拡散強調画像・FLAIR画像および拡散強調画像収集過程で生成されたADC－mapにおいて病変と対側の脳領域の信号値ならびにADC値を比較することで超急性期・急性期・慢性期を判断することのできる統合ソフトウェアを開発した。画像分類器での病変有無判断をセマンティックセグメンテーションにおける病変領域抽出と組み合わせることで、誤検出された病変領域を除外する機能を実装した。
人工知能技術によって得られた脳虚血領域のRadiomics解析に必要なデータは得られたものの経時的データ解析における変化量の抽出まで至らなかったため、次年度前半でこの点の解析と評価および次年度計画にある脳虚血性病変の予後予測に必要な解析を進める。
Japan Society for the Promotion of Science, Grant-in-Aid for Scientific Research (C), Hokkaido University, Principal investigator, 21K07586

易傷害性肝グラフトの至適体外灌流法と非侵襲的グラフト機能評価法の開発
科学研究費助成事業 基盤研究(B)
Apr. 2020 - Mar. 2023
嶋村 剛, 深井 原, 藤好 真人, 暮地本 宙己, 杉森 博行, 木村 太一
本研究の目的はラットExpanded Criteria Donor (脂肪肝、心停止肝) を用いた肝移植の術後成績を向上させるグラフト灌流法、コンディショニング法を開発し、術後成績を予測できる移植前グラフト機能評価法を確立することである。その実現のために、メタボローム解析とタンパク質機能解析が不可欠であり、前処理の条件検討を行った。トリクロロ酢酸 (TCA) によりタンパク質解析(沈殿)とメタボローム解析用(上清)の試料を同時に取得した。メタボローム解析用の試料は、LC-MS/MS、NMR、ラマン分光解析に供する目的でそれぞれに至適な条件を検討した。特にNMR、ラマン分光解析は試料のpHが測定結果に影響するため、ジエチルエーテルとの混和によるTCAの除去を繰り返し、安定した測定条件を見出した。これにより、NMR解析は600円/試料、LC-MS/MS解析は6000円/試料程度の費用で測定できる目処が立った (外注では約12万円/試料)。本課題におけるもっとも重要な評価系の一部が確立された。脂肪肝モデルも検討し、肝移植のグラフトとしての検討に資する30-60%の大滴性脂肪肝を安定して作成する方法を確立した。灌流液中のFMN濃度がグラフトのミトコンドリアComplex1の傷害マーカーになることが報告されているが、多くは灌流液の蛍光強度のみの測定であり、同じ蛍光を発するリボフラビン (RF)、フラビンアデニンジモノヌクレオチド (FMN)、フラビンアデニンジヌクレオチド (FAD)由来の蛍光の合算を"FMN"と称している。われわれはこれらの分子をHPLCで分離し、蛍光検出することにより、肝冷保存や体外灌流中のRF, FMN, FAD の漏出を5分以内で定量できるシステムを確立した。
日本学術振興会, 基盤研究(B), 北海道大学, Coinvestigator, 20H03737

Development of A Noninvasive Electrical Conductivity Measurement System for Lung Tumors
Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (C)
Apr. 2017 - Mar. 2020
Tha Khin Khin
Electrical conductivity is a property of materials to conduct electric current. Different materials have different electrical conductivity values, ranging from almost zero (insulators) to several million siemens per meter (S/m) (conductors). Living tissues are also reported to have variable electrical conductivity values - fat and bone have lower values, whereas the cerebrospinal fluid (CSF) and blood have higher values. Tumors, especially malignant ones, are reported to have higher values than the normal tissues. This study aimed at the establishment of an MRI technique to noninvasively measure electrical conductivity of lung lesions and to evaluate the accuracy of this technique in distinguishing lung tumors.
We established an MRI technique for noninvasive electrical conductivity measurement for lung lesions. Tumor contrast, which is a texture characteristic of electrical conductivity distribution, can be useful in distinguishing between benign and malignant lung tumors.
Japan Society for the Promotion of Science, Grant-in-Aid for Scientific Research (C), Hokkaido University, Coinvestigator, 17K10390

人工知能が自分で学習を進める画像診断システムの開発　　　　　　　　　　　　　　　
札幌ライフサイエンス産業活性化事業 研究シーズ発掘補助金（札幌タレント補助金）
Aug. 2018 - Mar. 2019
杉森 博行
北海道科学技術総合振興センター（略称：ノーステック財団）, Principal investigator, Competitive research funding

Arterial spin labeling法を用いたMRIでの腎血流量評価　　　　　　　　　　　　　　　
北海道放射線技術研究助成金
Apr. 2010 - Mar. 2012
杉森 博行
日本放射線技術学会 北海道部会, Principal investigator, Competitive research funding

判定装置およびプログラム
Patent right, 吉村 高明, 杉森 博行, 遠藤 大輝, 平田 健司, 工藤 與亮, 国立大学法人北海道大学
特願2022-187189, 24 Nov. 2022
特開2024-075922, 05 Jun. 2024
202403004526241990