Takaaki Yoshimura
| Faculty of Health Sciences | Lecturer |
Last Updated :2025/11/08
■Researcher basic information
Researchmap personal page
J-Global ID
Educational Organization
- Master's degree program, Graduate School of Health Sciences
- Doctoral (PhD) degree program, Graduate School of Health Sciences
■Research activity information
Awards
- Oct. 2025, Hokkaido University, 第11回北海道大学部局横断シンポジウムベストポスター賞
糖代謝が高い脳領域への放射線照射は膠芽腫患者の予後を悪化させるか？
Hiraki S;Nishioka K;Takahashi S;Yoshimura T;Kobashi K;Hirata K;Hashimoto T, Japan society, Japan - Oct. 2025, Hokkaido University, Hokkaido University Research Encouragement Award for Young Researchers (Silver)
超低磁場MRIによるMR画像誘導即時適応陽子線治療システムの開発
Yoshimura T;Matsuura T;Hashimoto T;Sugimori H;Togo R;Nakamoto T;Shimbo G, Japan society, Japan - Jul. 2025, Japanese Association fro Medical Artificial Intelligence (JMAI), JMAI AWARD
2.5D-SRCNNを用いた低カウントPET画像の画質改善：TotalSegmentatorを活用した多臓器における定量性の検証
Endo H;Yoshimura T;Sugimori H;Hirata K;Kudo K, Japan society, Japan - Jul. 2025, Japanese Association fro Medical Artificial Intelligence (JMAI), JMAI AWARD
Development of 3D-SRCNN model for treatment planning MRI in urethra-sparing proton therapy
Yoshimura T;Nishioka K;Sugimori H, Japan society, Japan - Apr. 2025, Japanese Society of Radiological Technology, 第81回日本放射線技術学会総会学術大会 座長推薦優秀研究
Evaluation of 18F-FDG PET/CT Report generation system using prompting methods
Minami K;Yoshimura T;Hirata K;Katsuki A;Uetake N;Watanabe S;Kudo K, Japan - Apr. 2025, The 81st Annual Meeting of Japanese Society of Radiological Technology (JSRT), 学生賞
Evaluation of 18F-FDG PET/CT Report generation system using prompting methods
Minami K;Yoshimura T;Hirata K;Katsuki A;Uetake N;Watanabe S;Kudo K, Japan society, Japan - Apr. 2025, The 81st Annual Meeting of Japanese Society of Radiological Technology (JSRT), 学生賞
Evaluating the effects of internal organ motion on Expiratory Phase CBCT images for Pancreas SBRT
Nukushina A;Yamada R;Yoshimura T;Kanehira T;Katoh N;Hashimoto T;Aoyama H, Japan society, Japan - Mar. 2025, 令和6年度 北海道大学CLAP受講者による成果発表会, 北海道大学大学院医学研究院医療AI開発者養成プログラム(CLAP), 優秀賞（ポスター部門）
深層学習を用いた膵区域区分における周囲構造検出法の検討
Kakizaki W;Yoshimura T;Sugimori H, Japan society, Japan - Mar. 2025, 令和6年度 北海道大学CLAP受講者による成果発表会, 北海道大学大学院医学研究院医療AI開発者養成プログラム(CLAP), 優秀賞（ポスター部門）
物体検出とセグメンテーションを併用した深層学習を用いた冠動脈描出方法における基礎的検討
Sakamoto M;Yoshimura T;Sugimori H;Hirata K;Kudo K, Japan society, Japan - Mar. 2025, Hokkaido University, Graduation Research Excellence Presentation Award
【学生・指導教員】膵癌SBRTにおける体内の動きが呼気止め分割CBCT画像に与える影響の評価
温品あい莉;吉村高明 - Mar. 2025, 令和6年度 北海道大学CLAP受講者による成果発表会, 北海道大学大学院医学研究院医療AI開発者養成プログラム(CLAP), 優秀賞（口演部門）
全身PET画像における短時間収集画像の画質改善:多臓器における定量性の検証
Endo H;Yoshimura T;Tang M;Sugimori H;Hasegawa A;Kogame S;Magota K;Kimura R;Watanabe S;Hirata K;Kudo K, Japan society, Japan - Nov. 2024, 令和6年度 電気・情報関係学会北海道支部連合大会, 若手優秀論文発表賞
J-MIDデータベースを用いたドメイン知識を考慮した継続自己教師あり学習に基づく肺疾患の分類に関する検討
太齋蓮;李広;藤後廉;唐明輝;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, Japan society, Japan - Nov. 2024, 日本放射線腫瘍学会, 学生・研修医賞
尿道同定精度向上に向けた治療計画用MR画像に対する超解像深層学習モデルの構築
Sato K, Yoshimura T, Nishioka K, Sinbo G, Endo H, Fujisawa Y, Sugimori H, Hirata K, Kudo K, Hashimoto T, Japan society, Japan - Sep. 2024, Hokkaido University, Hokkaido University Research Encouragement Award for Young Researchers (Bronze)
超低線量PET画像診断支援システムと生成AIの融合
Yoshimura T;Sugimori H;Togo R;Hirata K, Japan society, Japan - Apr. 2024, Japanese Society of Medical Physics, 学生奨励賞
A feasible study for classification of acute radiation-induced xerostomia risk based on a dosiomics
Takagi S;Nakamoto T;Yasuda K;Yoshimura T;Tamura H;Aoyama H, Japan society, Japan - Apr. 2024, 日本放射線技術学会北海道支部, 優秀研究賞
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
Sakaida M;Yoshimura T;Tang M;Ichikawa S;Sugimori H, Japan society, Japan - Mar. 2024, Clinical AI Human Resources Development Program (CLAP), Graduate School of Medicine, Hokkaido University, 優秀賞（ポスター部門）
マンモグラフィ石灰化検出における半教師あり学習の適用と評価
Sakaida M;Yoshimura T;Tang M;Ichikawa S;Sugimori H;Hirata K;Kudo K, Japan society, Japan - Mar. 2024, Hokkaido University, 保健学科長賞
【学生・指導教員】
藤澤祐太;吉村高明 - Mar. 2024, Clinical AI Human Resources Development Program (CLAP), Graduate School of Medicine, Hokkaido University, 優秀賞
SRCNNを用いた短時間収集PET画像の画質改善: 臨床画像における定量性の検証
Endo H;Yoshimura T;Tang M;Sugimori H;Hasegawa A;Kogame S;Magota K;Kimura R;Watanabe S;Hirata K;Kudo K, Japan society, Japan, 42583404 - Nov. 2023, Japanese Society of Radiological Technology, 第51回日本放射線技術学会秋季学術大会 座長推薦優秀研究
Estimation of angles for oblique lumbar spine images using deep learning
Moriya R;Yoshimura T;Tang M;Sugimori H, Japan society, Japan - Nov. 2023, Japanese Society of Radiological Technology, 第51回日本放射線技術学会秋季学術大会 座長推薦優秀研究
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
Inomata S;Yoshimura T;Tang M;Ichikawa S;Sugimori H, Japan society, Japan - Oct. 2023, Hokkaido University, 第9回北海道大学部局横断シンポジウムベストポスター賞
超解像深層学習を用いた低投与線量PET検査:臨床画像における定量性の検証
Endo H;Yoshimura T;Sugiumori H;Magota K;Togo R;Hirata K;Kudo K, Japan society, Japan - Oct. 2023, Hokkaido University, Hokkaido University Research Encouragement Award for Young Researchers (Bronze)
超解像深層学習を用いた低投与線量PET検査の実現に向けたシステム開発
Yoshimura T;Sugimori H;Hirata K;Togo R, Japan society, Japan - Apr. 2023, 日本放射線技術学会北海道支部, 優秀研究賞
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, Japan society, Japan - Mar. 2023, Hokkaido University, Graduation Research Excellence Presentation Award
【学生・指導教員】人獣連携による高精度MR-CT画像変換技術開発～前立腺癌に対するMR画像誘導即時適応陽子線治療の実現に向けて～
佐藤圭祐;吉村高明 - Nov. 2022, 第１回北大医療AIシンポジウム, 優秀研究賞
AIを用いた頭蓋内バイパス術の手術スキルの評価
Takahari R;Sugimori H;Yoshimura T;Ogasawara K;Sugiyama T;Tang M, Japan society, Japan - Oct. 2022, Japanese Society of Radiological Technology, 第50回日本放射線技術学会秋季学術大会 座長推薦優秀研究
Cine-MRIを用いた3D-CNNによる左室駆出率と右室駆出率推定
Inomata S;Yoshimura T;Tang M;Sugimori H, Japan society, Japan - Oct. 2022, 北海道大学, Hokkaido University Research Encouragement Award for Young Researchers (Bronze)
人獣連携によりMR画像-CT画像変換を高精度化する技術の開発~前立腺癌に対するMR画像誘導即時適応尿道線量低減陽子線治療の実現に向けて~
Yoshimura T;Shinbo G;Matsuura T;Hashimoto T;Nishioka K;Mori T;Kanehira T;Sugimori H, Japan society, Japan - Sep. 2022, Japan Society of Medical Physics, 奨励賞
Construction of Super-Resolution Convolution Neural Network for Medical Radiation Exposure Reduction in whole-body PET examination.
Endo H;Yoshimura T;Tang M;Sugimori H;Hasegawa A;Kogame S;Magota K;Kimura R;Watanabe S;Hirata K;Kudo K - Mar. 2022, 日本放射線技術学会北海道支部, 優秀研究賞
SSIMを用いたPET画像に対する再構成条件の最適化
葛西悠平;名雲渓太;MANIAWSKI PJ;孫田惠一;平田健司;吉村高明;山品博子 - Mar. 2022, Northern Advancement Center for Science & Technology (NOASTEC), NOASTEC Presidential Prize
人工知能を用いた前立腺がんに対する 動体追跡陽子線治療計画技術の開発
Yoshimmura T, Publisher, Japan - Feb. 2022, 令和3年度 家畜診療等技術全国研究集会, 全国農業共済協会長賞
子牛の急性腹症における超音波検査の有用性
吉村直彬;吉村高明;音井威重 - Oct. 2021, Japanese Society of Radiological Technology, 第49回日本放射線技術学会秋季学術大会 座長推薦優秀研究
Preliminary study for the diagnosis-aid of the prostate MRI using deep learning
Manabe K;Yoshimura T;Yamada T;Asami Y;Sugimori H, Japan society, Japan - Mar. 2021, Hokkaido University, Graduation Research Excellence Presentation Award
【学生・指導教員】Deep Learningを用いた放射線治療計画用MRIの画質改善に関する研究
Shouki Kogame;Takaaki Yoshimura - Jun. 2019, Particle Therapy Co-Operative Group(PTCOG), PTCOG58 Travel Fellowship Award
Yoshimura Takaaki
Papers
- 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, 15, 19, 10534, 10534, MDPI AG, 29 Sep. 2025, [Peer-reviewed]
English, Scientific journal, Purpose: To develop and validate a two-stage system for automated quality assessment of shoulder true-AP radiographs by combining joint localization with quality classification. Materials and Methods: From the MURA “SHOULDER” subset, 2956 anteroposterior images were identified; 59 images with negative–positive inversion, excessive metallic implants, extreme exposure, or presumed fluoroscopy were excluded, yielding a class-balanced set of 2800 images (1400 OK/1400 NG). A YOLOX-based detector localized the glenohumeral joint, and classifiers operated on both whole images and detector-centered crops. To enhance interpretability, we integrated Grad-CAM into both whole-image and local classifiers and assessed attention patterns against radiographic criteria. Results: The detector achieved AP@0.5 = 1.00 and a mean Dice similarity coefficient of 0.967. The classifier attained AUC = 0.977 (F1 = 0.943) on a held-out test set. Heat map analyses indicated anatomically focused attention consistent with expert-defined regions, and coverage metrics favored local over whole-image models. Conclusions: The two-stage, XAI-integrated approach provides accurate and interpretable assessment of shoulder true-AP image quality, aligning model attention with radiographic criteria. - Evaluation of intra-fractional target displacement by patient motion during a single-isocenter multi-target stereotactic radiation therapy for brain metastases.
Ryota Yamada, Takaaki Yoshimura, Ryo Murata, Kentaro Nishioka, Takashi Mori, Fuki Koizumi, Yoshihiro Fujita, Shuhei Takahashi, Takahiro Hattori, Takahiro Kanehira, Kohei Yokokawa, Rie Yamazaki, Kenji Horita, Hiroshi Tamura, Yamato Wakabayashi, Yuta Ichiu, Takayuki Hashimoto, Hidefumi Aoyama
Journal of applied clinical medical physics, 26, 9, e70219, Sep. 2025, [Peer-reviewed], [Corresponding author], [International Magazine]
English, Scientific journal, BACKGROUND: Single-isocenter multi-target volumetric modulated arc therapy (SIMT-VMAT) has been implemented widely in fractionated stereotactic radiosurgery (fSRS) to treat brain metastases. The impact of rotational intra-fractional patient motion (IFPM) is influenced by the distance between the geometric target's center and the isocenter (DTI). PURPOSE: We hypothesized that IFPM's impact on each target would increase with greater DTI during fSRS. Therefore, we aimed to estimate the intra-fractional target displacement (IFTD), which represents each target's displacement caused by translational and rotational components of IFPM. METHODS: In this study, we involved 35 patients with 2-13 brain metastases, all of whom had previously undergone SIMT-VMAT fSRS. All patients were immobilized using a clamshell-style device, with 28 using a biteplate. Cone beam computed tomography (CBCT) images were obtained at the same imaging center before and after treatment. The IFPM was calculated using both CBCT datasets. The IFTD was determined by comparing the planned target coordinates with the actual coordinates while factoring in IFPM. RESULTS: We evaluated 136 targets. The mean IFTD was 0.38 mm (95% confidence interval [CI]: 0.37-0.40 mm) with the biteplate and 0.65 mm (95% CI: 0.59-0.71 mm) without it. A very weak positive correlation was observed between DTI and IFTD despite the immobilization method. This correlation indicates that the distance dependence of IFTD is nearly negligible. CONCLUSION: The findings showed that the impact of IFPM on each target demonstrated minimal dependence on the DTI. Displacement was relatively consistent regardless of the target location. In addition, the use of a biteplate was suggested to potentially reduce these effects. - 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, Frontiers Media SA, 29 Aug. 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. - Development and validation of 3D super-resolution convolutional neural network for 18F-FDG-PET images.
Hiroki Endo, Kenji Hirata, Keiichi Magota, Takaaki Yoshimura, Chietsugu Katoh, Kohsuke Kudo
EJNMMI physics, 12, 1, 77, 77, 19 Aug. 2025, [Peer-reviewed], [International Magazine]
English, Scientific journal, BACKGROUND: Positron emission tomography (PET) is a valuable tool for cancer diagnosis but generally has a lower spatial resolution compared to computed tomography (CT) or magnetic resonance imaging (MRI). High-resolution PET scanners that use silicon photomultipliers and time-of-flight measurements are expensive. Therefore, cost-effective software-based super-resolution methods are required. This study proposes a novel approach for enhancing whole-body PET image resolution applying a 2.5-dimensional Super-Resolution Convolutional Neural Network (2.5D-SRCNN) combined with logarithmic transformation preprocessing. This method aims to improve image quality and maintain quantitative accuracy, particularly for standardized uptake value measurements, while addressing the challenges of providing a memory-efficient alternative to full three-dimensional processing and managing the wide dynamic range of tracer uptake in PET images. We analyzed data from 90 patients who underwent whole-body FDG-PET/CT examinations and reconstructed low-resolution slices with a voxel size of 4 × 4 × 4 mm and corresponding high-resolution (HR) slices with a voxel size of 2 × 2 × 2 mm. The proposed 2.5D-SRCNN model, based on the conventional 2D-SRCNN structure, incorporates information from adjacent slices to generate a high-resolution output. Logarithmic transformation of the voxel values was applied to manage the large dynamic range caused by physiological tracer accumulation in the bladder. Performance was assessed using the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). The quantitative accuracy of standardized uptake values (SUV) was validated using a phantom study. RESULTS: The results demonstrated that the 2.5D-SRCNN with logarithmic transformation significantly outperformed the conventional 2D-SRCNN in terms of PSNR and SSIM (p < 0.0001). The proposed method also showed an improved depiction of small spheres in the phantom while maintaining the accuracy of the SUV. CONCLUSIONS: Our proposed method for whole-body PET images using a super-resolution model with the 2.5D approach and logarithmic transformation may be effective in generating super-resolution images with a lower spatial error and better quantitative accuracy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40658-025-00791-y. - 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], [International Magazine]
English, 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]
English, 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], [International Magazine]
English, 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. - Cost-effectiveness analysis for multi adverse events of proton beam therapy for pediatric medulloblastoma in Japan.
Takaaki Yoshimura, Yasuhiro Morii, Honoka Tamori, Ryuki Kita, Takayuki Hashimoto, Hidefumi Aoyama, Katsuhiko Ogasawara
Journal of radiation research, 66, 1, 31, 38, Oxford University Press (OUP), 22 Jan. 2025, [Peer-reviewed], [Lead author], [International Magazine]
English, Scientific journal, Abstract
Medulloblastomas are one of the most common malignant cancers of the central nervous system in children. Proton beam therapy (PBT) is expected to provide equivalent tumor control to photon therapy while reducing the various adverse events caused by irradiation. Few studies have considered the cost-effectiveness of PBT for pediatric medulloblastoma, considering the multiple adverse effects and reflecting on the latest treatment advancements. A cost-utility analysis of PBT for pediatric medulloblastoma was conducted in a Japanese setting and compared to conventional photon therapy. The analysis was conducted from the public healthcare payer’s perspective, and direct costs for the treatment of radiation therapy and radiation-induced adverse events were included. A Markov model was used, and the health states of secondary cancer, hypothyroidism and hearing loss were defined as adverse events. The time horizon was the lifetime. Incremental cost-effectiveness ratio (ICER) was used as a measurement of cost-effectiveness, with quality-adjusted life years (QALYs) used as an outcome. The costs were estimated from the national fee schedule, and the utility and transition probabilities were estimated from published literature. PBT incurred an additional 1387116 Japanese yen (JPY) and 1.56 QALYs to the comparator. The ICER was JPY 887053/QALY, indicating that PBT was cost-effective, based on the reference value of JPY 5 million/QALY used in the Japanese cost-effectiveness analysis. Deterministic sensitivity analysis showed that the ICER ranged from JPY 284782/QALY to JPY 1918603/QALY as a result of deterministic sensitivity analysis, and probabilistic sensitivity analysis showed that PBT was cost-effective, with a probability of 91.7%. - 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
08 Jan. 2025
We propose a novel continual self-supervised learning method (CSSL)
considering medical domain knowledge in chest CT images. Our approach addresses
the challenge of sequential learning by effectively capturing the relationship
between previously learned knowledge and new information at different stages.
By incorporating an enhanced DER into CSSL and maintaining both diversity and
representativeness within the rehearsal buffer of DER, the risk of data
interference during pretraining is reduced, enabling the model to learn more
richer and robust feature representations. In addition, we incorporate a mixup
strategy and feature distillation to further enhance the model's ability to
learn meaningful representations. We validate our method using chest CT images
obtained under two different imaging conditions, demonstrating superior
performance compared to state-of-the-art methods. - 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], [International Magazine]
English, 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. - 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
Li Yanjun, Takaaki Yoshimura, Yuto Horima, Hiroyuki Sugimori
Electronics, 13, 18, 3676, 3676, 16 Sep. 2024, [Peer-reviewed], [International Magazine]
English, Scientific journal - Geometric target margin strategy of proton craniospinal irradiation for pediatric medulloblastoma
Takaaki Yoshimura, Keigo Kondo, Takayuki Hashimoto, Kentaro Nishioka, Takashi Mori, Takahiro Kanehira, Taeko Matsuura, Seishin Takao, Hiroshi Tamura, Takuya Matsumoto, Kenneth Sutherland, Hidefumi Aoyama
Journal of Radiation Research, 65, 5, 676, 688, Oxford University Press (OUP), 15 Sep. 2024, [Peer-reviewed], [International Magazine]
English, Scientific journal, Abstract
In proton craniospinal irradiation (CSI) for skeletally immature pediatric patients, a treatment plan should be developed to ensure that the dose is uniformly delivered to all vertebrae, considering the effects on bone growth balance. The technical (t) clinical target volume (CTV) is conventionally set by manually expanding the CTV from the entire intracranial space and thecal sac, based on the physician’s experience. However, there are differences in contouring methods among physicians. Therefore, we aimed to propose a new geometric target margin strategy. Nine pediatric patients with medulloblastoma who underwent proton CSI were enrolled. We measured the following water equivalent lengths for each vertebra in each patient: body surface to the dorsal spinal canal, vertebral limbus, ventral spinal canal and spinous processes. A simulated tCTV (stCTV) was created by assigning geometric margins to the spinal canal using the measurement results such that the vertebral limb and dose distribution coincided with a margin assigned to account for the uncertainty of the proton beam range. The stCTV with a growth factor (correlation between body surface area and age) and tCTV were compared and evaluated. The median values of each index for cervical, thoracic and lumber spine were: the Hausdorff distance, 9.14, 9.84 and 9.77 mm; mean distance-to-agreement, 3.26, 2.65 and 2.64 mm; Dice coefficient, 0.84, 0.81 and 0.82 and Jaccard coefficient, 0.50, 0.60 and 0.62, respectively. The geometric target margin setting method used in this study was useful for creating an stCTV to ensure consistent and uniform planning., 38970405 - 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], [International Magazine]
English, 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. - 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], [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], [Corresponding author], [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. - Probability of normal tissue complications for hematologic and gastrointestinal toxicity in postoperative whole pelvic radiotherapy for gynecologic malignancies using intensity-modulated proton therapy with robust optimization.
Takaaki Yoshimura, Ryota Yamada, Rumiko Kinoshita, Taeko Matsuura, Takahiro Kanehira, Hiroshi Tamura, Kentaro Nishioka, Koichi Yasuda, Hiroshi Taguchi, Norio Katoh, Keiji Kobashi, Takayuki Hashimoto, Hidefumi Aoyama
Journal of radiation research, 17 Mar. 2024, [Peer-reviewed], [Lead author], [International Magazine]
English, Scientific journal, This retrospective treatment-planning study was conducted to determine whether intensity-modulated proton therapy with robust optimization (ro-IMPT) reduces the risk of acute hematologic toxicity (H-T) and acute and late gastrointestinal toxicity (GI-T) in postoperative whole pelvic radiotherapy for gynecologic malignancies when compared with three-dimensional conformal radiation therapy (3D-CRT), intensity-modulated X-ray (IMXT) and single-field optimization proton beam (SFO-PBT) therapies. All plans were created for 13 gynecologic-malignancy patients. The prescribed dose was 45 GyE in 25 fractions for 95% planning target volume in 3D-CRT, IMXT and SFO-PBT plans and for 99% clinical target volume (CTV) in ro-IMPT plans. The normal tissue complication probability (NTCP) of each toxicity was used as an in silico surrogate marker. Median estimated NTCP values for acute H-T and acute and late GI-T were 0.20, 0.94 and 0.58 × 10-1 in 3D-CRT; 0.19, 0.65 and 0.24 × 10-1 in IMXT; 0.04, 0.74 and 0.19 × 10-1 in SFO-PBT; and 0.06, 0.66 and 0.15 × 10-1 in ro-IMPT, respectively. Compared with 3D-CRT and IMXT plans, the ro-IMPT plan demonstrated significant reduction in acute H-T and late GI-T. The risk of acute GI-T in ro-IMPT plan is equivalent with IMXT plan. The ro-IMPT plan demonstrated potential clinical benefits for reducing the risk of acute H-T and late GI-T in the treatment of gynecologic malignances by reducing the dose to the bone marrow and bowel bag while maintaining adequate dose coverage to the CTV. Our results indicated that ro-IMPT may reduce acute H-T and late GI-T risk with potentially improving outcomes for postoperative gynecologic-malignancy patients with concurrent chemotherapy., 36844057 - 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]
English, 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. - 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], [International Magazine]
English, 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. - 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]
English, 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], [Lead author], [International Magazine]
English, 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., 36844057 - 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, 31 May 2023, [Peer-reviewed]
English, 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. - 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], [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. - 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, 30 Jan. 2023, [Peer-reviewed], [International Magazine]
English, 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, Jan. 2023, [Peer-reviewed], [Lead author], [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., 36844057 - Efficacy of Abdominal Ultrasonography for Differentiation of Gastrointestinal Diseases in Calves
Naoaki Yoshimura, Takeshi Tsuka, Takaaki Yoshimura, Takeshige Otoi
Animals, 12, 19, 2489, 2489, MDPI AG, 20 Sep. 2022, [Peer-reviewed]
English, Scientific journal, This study investigated the clinical efficacy of abdominal ultrasonography for abomasal dilation in three calves, intestinal volvulus in five calves, intussusception in one calf, and internal hernia in one calf. In the abdominal ultrasonograms of the abomasal dilation cases, this disease was commonly characterized by severely extended lumens, including heterogeneously hyperechoic ingesta without intraluminal accumulations of gas. In the animals with intestinal volvulus and intussusception, a to-and-fro flow was observed to be a common ultrasonographic characteristic that led to suspicion of an intestinal obstruction. The use of abdominal ultrasonography for five cases with intestinal volvulus gave no reason to suspect this disease, despite its efficacy in one case, based on an acutely angled narrowing. Although three of five animals with intestinal volvulus had intestinal ruptures, no ultrasonographic evidence could be obtained. When abdominal ultrasonography was used for one case with intussusception, this pathological condition could be strongly suspected, as a “target” sign was observed. This finding supported surgical intervention for this case, followed by treatment with manual reduction, resulting in a favorable outcome. In terms of the differential and definitive diagnosis for various intestinal diseases, abdominal ultrasonography may be poor at providing indicative evidence, but very helpful for confirming intestinal obstruction. - Dosimetric advantages of daily adaptive strategy in IMPT for high‐risk prostate cancer
Hiroshi Tamura, Keiji Kobashi, Kentaro Nishioka, Takaaki Yoshimura, Takayuki Hashimoto, Shinichi Shimizu, Yoichi M. Ito, Yoshikazu Maeda, Makoto Sasaki, Kazutaka Yamamoto, Hiroyasu Tamamura, Hidefumi Aoyama, Hiroki Shirato
Journal of Applied Clinical Medical Physics, 23, 4, e13531, Wiley, Apr. 2022, [Peer-reviewed], [International Magazine]
English, Scientific journal, PURPOSE: To evaluate the dosimetric advantages of daily adaptive radiotherapy (DART) in intensity-modulated proton therapy (IMPT) for high-risk prostate cancer by comparing estimated doses of the conventional non-adaptive radiotherapy (NART) that irradiates according to an original treatment plan through the entire treatment and the DART that uses an adaptive treatment plan generated by using daily CT images acquired before each treatment. METHODS: Twenty-three patients with prostate cancer were included. A treatment plan with 63 Gy (relative biological effectiveness (RBE)) in 21 fractions was generated using treatment planning computed tomography (CT) images assuming that all patients had high-risk prostate cancer for which the clinical target volume (CTV) needs to include prostate and the seminal vesicle (SV) in our treatment protocol. Twenty-one adaptive treatment plans for each patient (total 483 data sets) were generated using daily CT images, and dose distributions were calculated. Using a 3 mm set-up uncertainty in the robust optimization, the doses to the CTV, prostate, SV, rectum, and bladder were compared. RESULTS: Estimated accumulated doses of NART and DART in the 23 patients were 60.81 ± 3.47 Gy (RBE) and 63.24 ± 1.04 Gy (RBE) for CTV D99 (p < 0.01), 62.99 ± 1.28 Gy (RBE) and 63.43 ± 1.33 Gy (RBE) for the prostate D99 (p = 0.2529), and 59.07 ± 5.19 Gy (RBE) and 63.17 ± 1.04 Gy (RBE) for SV D99 (p < 0.001). No significant differences were observed between NART and DART in the estimated accumulated dose for the rectum and bladder. CONCLUSION: Compared with the NART, DART was shown to be a useful approach that can maintain the dose coverage to the target without increasing the dose to the organs at risk (OAR) using the 3 mm set-up uncertainty in the robust optimization in patients with high-risk prostate cancer. - 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, 12, 4, 872, 872, MDPI AG, 31 Mar. 2022, [Peer-reviewed], [Lead 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., 33179641 - 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, 16 Dec. 2021, [Peer-reviewed]
English, 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. - Are simple verbal instructions sufficient to ensure that bladder volume does not deteriorate prostate position reproducibility during spot scanning proton therapy?
Kentaro Nishioka, Kento Gotoh, Takayuki Hashimoto, Takashige Abe, Takahiro Osawa, Ryuji Matsumoto, Isao Yokota, Norio Katoh, Rumiko Kinoshita, Koichi Yasuda, Toshiaki Yakabe, Takaaki Yoshimura, Seishin Takao, Nobuo Shinohara, Hidefumi Aoyama, Shinichi Shimizu, Hiroki Shirato
BJR|Open, 3, 1, 20210064, 20210064, British Institute of Radiology, Nov. 2021, [Peer-reviewed], [International Magazine]
English, Scientific journal,Objectives: The purpose of this study is to investigate whether verbal instructions are sufficient for bladder volume (BV) control not to deteriorate prostate position reproducibility in image-guided spot scanning proton therapy (SSPT) for localized prostate cancer.Methods: A total of 268 treatment sessions in 12 consecutive prostate cancer patients who were treated with image-guided SSPT with fiducial markers were retrospectively analyzed. In addition to strict rectal volume control procedures, simple verbal instructions to void urine one hour before the treatment were used here. The BV was measured by a Bladder Scan just before the treatment, and the prostate motion was measured by intraprostatic fiducial markers and two sets of X-ray fluoroscopy images. The correlation between the BV change and prostate motion was assessed by linear mixed-effects models and systematic and random errors according to the reproducibility of the BV.Results: The mean absolute BV change during treatment was from −98.7 to 86.3 ml (median 7.1 ml). The mean absolute prostate motion of the patients in the left-right direction was −1.46 to 1.85 mm; in the cranial-caudal direction it was −6.10 to 3.65 mm, and in the anteroposterior direction −1.90 to 5.23 mm. There was no significant relationship between the BV change and prostate motion during SSPT. The early and late genitourinary and gastrointestinal toxicity was minimal with a minimum follow up of 4.57 years.Conclusions: Simple verbal instructions about urination was suggested to be sufficient to control the BV not to impact on the prostate motion and clinical outcomes in image-guided SSPT. Careful attention to BV change is still needed when the seminal vesicle is to be treated.Advances in knowledge: Our data demonstrated that there was no apparent relationship between BV changes and prostate position reproducibility and simple verbal instruction about urination could be sufficient for image-guided SSPT. - A treatment planning study of urethra-sparing intensity-modulated proton therapy for localized prostate cancer
Takaaki Yoshimura, Kentaro Nishioka, Takayuki Hashimoto, Kazuya Seki, Shouki Kogame, Sodai Tanaka, Takahiro Kanehira, Masaya Tamura, Seishin Takao, Taeko Matsuura, Keiji Kobashi, Fumi Kato, Hidefumi Aoyama, Shinichi Shimizu
Physics and Imaging in Radiation Oncology, 20, 23, 29, Elsevier BV, Oct. 2021, [Peer-reviewed], [Lead author], [International Magazine]
English, Scientific journal, BACKGROUND AND PURPOSE: Urethra-sparing radiation therapy for localized prostate cancer can reduce the risk of radiation-induced genitourinary toxicity by intentionally underdosing the periurethral transitional zone. We aimed to compare the clinical impact of a urethra-sparing intensity-modulated proton therapy (US-IMPT) plan with that of conventional clinical plans without urethral dose reduction. MATERIALS AND METHODS: This study included 13 patients who had undergone proton beam therapy. The prescribed dose was 63 GyE in 21 fractions for 99% of the clinical target volume. To compare the clinical impact of the US-IMPT plan with that of the conventional clinical plan, tumor control probability (TCP) and normal tissue complication probability (NTCP) were calculated with a generalized equivalent uniform dose-based Lyman-Kutcher model using dose volume histograms. The endpoints of these model parameters for the rectum, bladder, and urethra were fistula, contraction, and urethral stricture, respectively. RESULTS: The mean NTCP value for the urethra in US-IMPT was significantly lower than that in the conventional clinical plan (0.6% vs. 1.2%, p < 0.05). There were no statistically significant differences between the conventional and US-IMPT plans regarding the mean minimum dose for the urethra with a 3-mm margin, TCP value, and NTCP value for the rectum and bladder. Additionally, the target dose coverage of all plans in the robustness analysis was within the clinically acceptable range. CONCLUSIONS: Compared with the conventional clinically applied plans, US-IMPT plans have potential clinical advantages and may reduce the risk of genitourinary toxicities, while maintaining the same TCP and NTCP in the rectum and bladder., 13099649 - Cost-effectiveness analysis using lifetime attributable risk of proton beam therapy for pediatric medulloblastoma in Japan.
Takaaki Yoshimura, Honoka Tamori, Yasuhiro Morii, Takayuki Hashimoto, Shinichi Shimizu, Katsuhiko Ogasawara
Journal of radiation research, 29 Sep. 2021, [Peer-reviewed], [Lead author], [International Magazine]
English, Scientific journal, Compared to conventional X-ray therapy, proton beam therapy (PBT) has more clinical and physical advantages such as irradiation dose reduction to normal tissues for pediatric medulloblastoma. However, PBT is expensive. We aimed to compare the cost-effectiveness of PBT for pediatric medulloblastoma with that of conventional X-ray therapy, while focusing on radiation-induced secondary cancers, which are rare, serious and negatively affect a patient's quality of life (QOL). Based on a systematic review, a decision tree model was used for the cost-effectiveness analysis. This analysis was performed from the perspective of health care payers; the cost was estimated from medical fees. The target population was pediatric patients with medulloblastoma below 14 years old. The time horizon was set at 7.7 years after medulloblastoma treatment. The primary outcome was the incremental cost-effectiveness ratio (ICER), which was defined as the ratio of the difference in cost and lifetime attributable risk (LAR) between conventional X-ray therapy and PBT. The discount rate was set at 2% annually. Sensitivity analyses were performed to model uncertainty. Cost and LAR in conventional X-ray therapy and PBT were Japanese yen (JPY) 1 067 608 and JPY 2436061 and 42% and 7%, respectively. The ICER was JPY 3856398/LAR. In conclusion, PBT is more cost-effective than conventional X-ray therapy in reducing the risk of radiation-induced secondary cancers in pediatric medulloblastoma. Thus, our constructed ICER using LAR is one of the valid indicators for cost-effectiveness analysis in radiation-induced secondary cancer. - Ophthalmic findings in a septic calf with the concurrent exhibition of meningitis and endophthalmitis.
Naoaki Yoshimura, Takeshi Tsuka, Yuji Sunden, Takehito Morita, Md Shafiqul Islam, Osamu Yamato, Takaaki Yoshimura
The Journal of veterinary medical science, 14 Sep. 2021, [Peer-reviewed], [Domestic magazines]
English, Scientific journal, The aim of this study was to evaluate the impacts of ophthalmic findings obtained from both macroscopic examination and ocular ultrasonography when diagnosing bovine endophthalmitis. A newborn crossbreed (Japanese black and Holstein breeds) calf was suspected of visual impairment and central nervous system (CNS) symptoms, such as decreased activity and weak drinking performance. This calf was found to display macroscopic signs, such as clouded lens, convergent strabismus, and horizontal nystagmus, in both eyes. On ocular ultrasonography of both eyes, a V-shaped, thickened, hyperechoic structure was present in the anechoic vitreous humors, indicating retinal detachment. The animal died 4 days after the examination. Sepsis was evident in this case, as Escherichia coli was isolated from multiple organs. The autopsy and histological examination revealed meningitis, encephalitis, and secondary hydrocephalus in the CNS, and endophthalmitis and retinal detachment in both eyes. In this case, the ophthalmic findings did not provide definitive evidence for a diagnosis of endophthalmitis. However, this study indicated that retinal detachment might be an ultrasonographic finding that is suggestive of bovine endophthalmitis. - First experimental results of gated proton imaging using x-ray fluoroscopy to detect a fiducial marker.
Sodai Tanaka, Naoki Miyamoto, Yuto Matsuo, Takaaki Yoshimura, Seishin Takao, Taeko Matsuura
Physics in medicine and biology, 66, 18, 09 Sep. 2021, [Peer-reviewed], [International Magazine]
English, Scientific journal, Increasing numbers of proton imaging research studies are being conducted for accurate proton range determination in proton therapy treatment planning. However, there is no proton imaging system that deals with motion artifacts. In this study, a gated proton imaging system was developed and the first experimental results of proton radiography were obtained for a moving object without motion artifacts. A motion management system using dual x-ray fluoroscopy for detecting a spherical gold fiducial marker was introduced and the proton beam was gated in accordance with the motion of the object. To demonstrate the performance of the gated proton imaging system, gated proton radiography images of a moving phantom were acquired experimentally, and the motion artifacts clearly were diminished. Also, the factors causing image deteriorations were evaluated focusing on the new gating system developed here, and the main factor was identified as the latency (with a maximum value of 93 ms) between the ideal gating signal according to the actual marker position and the actual gating signal. The possible deterioration due to the latency of the proton imaging system and proton beam irradiation was small owing to appropriate setting of the time structure. - 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], [Lead author]
Scientific journal, 13099649 - Construction of a detachable artificial trachea model for three age groups for use in an endotracheal suctioning training environment simulator.
Takaaki Yoshimura, Noriyo Colley, Shunsuke Komizunai, Shinji Ninomiya, Satoshi Kanai, Atsushi Konno, Koichi Yasuda, Hiroshi Taguchi, Takayuki Hashimoto, Shinichi Shimizu
PloS one, 16, 3, e0249010, 2021, [Peer-reviewed], [Lead author], [International Magazine]
English, Scientific journal, Tracheal suctioning is an important procedure to maintain airway patency by removing secretions. Today, suctioning operators include not only medical staff, but also family caregivers. The use of a simulation system has been noted to be the most effective way to learn the tracheal suctioning technique for operators. While the size of the trachea varies across different age groups, the artificial trachea model in the simulation system has only one fixed model. Thus, this study aimed to construct multiple removable trachea models according to different age groups. We enrolled 20 patients who had previously received proton beam therapy in our institution and acquired the treatment planning computed tomography (CT) image data. To construct the artificial trachea model for three age groups (children, adolescents and young adults, and adults), we analyzed the three-dimensional coordinates of the entire trachea, tracheal carina, and the end of the main bronchus. We also analyzed the diameter of the trachea and main bronchus. Finally, we evaluated the accuracy of the model by analyzing the difference between the constructed model and actual measurements. The trachea model was 8 cm long for children and 12 cm for adolescents and young adults, and for adults. The angle between the trachea and bed was about 20 degrees, regardless of age. The mean model accuracy was less than 0.4 cm. We constructed detachable artificial trachea models for three age groups for implementation in the endotracheal suctioning training environment simulator (ESTE-SIM) based on the treatment planning CT image. Our constructed artificial trachea models will be able to provide a simulation environment for various age groups in the ESTE-SIM. - Potential benefits of adaptive intensity-modulated proton therapy in nasopharyngeal carcinomas.
Hideki Minatogawa, Koichi Yasuda, Yasuhiro Dekura, Seishin Takao, Taeko Matsuura, Takaaki Yoshimura, Ryusuke Suzuki, Isao Yokota, Noriyuki Fujima, Rikiya Onimaru, Shinichi Shimizu, Hidefumi Aoyama, Hiroki Shirato
Journal of applied clinical medical physics, 22, 1, 174, 183, Jan. 2021, [Peer-reviewed], [International Magazine]
English, Scientific journal, PURPOSE: To investigate potential advantages of adaptive intensity-modulated proton beam therapy (A-IMPT) by comparing it to adaptive intensity-modulated X-ray therapy (A-IMXT) for nasopharyngeal carcinomas (NPC). METHODS: Ten patients with NPC treated with A-IMXT (step and shoot approach) and concomitant chemotherapy between 2014 and 2016 were selected. In the actual treatment, 46 Gy in 23 fractions (46Gy/23Fx.) was prescribed using the initial plan and 24Gy/12Fx was prescribed using an adapted plan thereafter. New treatment planning of A-IMPT was made for the same patients using equivalent dose fractionation schedule and dose constraints. The dose volume statistics based on deformable images and dose accumulation was used in the comparison of A-IMXT with A-IMPT. RESULTS: The means of the Dmean of the right parotid gland (P < 0.001), right TM joint (P < 0.001), left TM joint (P < 0.001), oral cavity (P < 0.001), supraglottic larynx (P = 0.001), glottic larynx (P < 0.001), , middle PCM (P = 0.0371), interior PCM (P < 0.001), cricopharyngeal muscle (P = 0.03643), and thyroid gland (P = 0.00216), in A-IMPT are lower than those of A-IMXT, with statistical significance. The means of, D0.03cc , and Dmean of each sub portion of auditory apparatus and D30% for Eustachian tube and D0.5cc for mastoid volume in A-IMPT are significantly lower than those of A-IMXT. The mean doses to the oral cavity, supraglottic larynx, and glottic larynx were all reduced by more than 20 Gy (RBE = 1.1). CONCLUSIONS: An adaptive approach is suggested to enhance the potential benefit of IMPT compared to IMXT to reduce adverse effects for patients with NPC. - Proton dose calculation based on converting dual-energy CT data to stopping power ratio (DEEDZ-SPR): a beam-hardening assessment.
Sodai Tanaka, Yoshiyuki Noto, Satoru Utsunomiya, Takaaki Yoshimura, Taeko Matsuura, Masatoshi Saito
Physics in medicine and biology, 65, 23, 235046, 235046, IOP Publishing, 18 Dec. 2020, [Peer-reviewed], [International Magazine]
English, Scientific journal, To achieve an accurate stopping power ratio (SPR) prediction in particle therapy treatment planning, we previously proposed a simple conversion to the SPR from dual-energy (DE) computed tomography (CT) data via electron density and effective atomic number (Z eff) calibration (DEEDZ-SPR). This study was conducted to carry out an initial implementation of the DEEDZ-SPR conversion method with a clinical treatment planning system (TPS; VQA, Hitachi Ltd., Tokyo) for proton beam therapy. Consequently, this paper presents a proton therapy plan for an anthropomorphic phantom to evaluate the stability of the dose calculations obtained by the DEEDZ-SPR conversion against the variation of the calibration phantom size. Dual-energy x-ray CT images were acquired using a dual-source CT (DSCT) scanner. A single-energy CT (SECT) scan using the same DSCT scanner was also performed to compare the DEEDZ-SPR conversion with the SECT-based SPR (SECT-SPR) conversion. The scanner-specific parameters necessary for the SPR calibration were obtained from the CT images of tissue substitutes in a calibration phantom. Two calibration phantoms with different sizes (a 33 cm diameter phantom and an 18 cm diameter phantom) were used for the SPR calibrations to investigate the beam-hardening effect on dosimetric uncertainties. Each set of calibrated SPR data was applied to the proton therapy plan designed using the VQA TPS with a pencil beam algorithm for the anthropomorphic phantom. The treatment plans with the SECT-SPR conversion exhibited discrepancies between the dose distributions and the dose-volume histograms (DVHs) of the 33 cm and 18 cm phantom calibrations. In contrast, the corresponding dose distributions and the DVHs obtained using the DEEDZ-SPR conversion method coincided almost perfectly with each other. The DEEDZ-SPR conversion appears to be a promising method for providing proton dose plans that are stable against the size variations of the calibration phantom and the patient. - Quantitative analysis of treatments using real-time image gated spot-scanning with synchrotron-based proton beam therapy system log data.
Takaaki Yoshimura, Shinichi Shimizu, Takayuki Hashimoto, Kentaro Nishioka, Norio Katoh, Hiroshi Taguchi, Koichi Yasuda, Taeko Matsuura, Seishin Takao, Masaya Tamura, Sodai Tanaka, Yoichi M Ito, Yuto Matsuo, Hiroshi Tamura, Kenji Horita, Kikuo Umegaki, Hiroki Shirato
Journal of applied clinical medical physics, 21, 12, 10, 19, Dec. 2020, [Peer-reviewed], [Lead author], [International Magazine]
English, Scientific journal, A synchrotron-based real-time image gated spot-scanning proton beam therapy (RGPT) system with inserted fiducial markers can irradiate a moving tumor with high accuracy. As gated treatments increase the beam delivery time, this study aimed to investigate the frequency of intra-field adjustments corresponding to the baseline shift or drift and the beam delivery efficiency of a synchrotron-based RGPT system. Data from 118 patients corresponding to 127 treatment plans and 2810 sessions between October 2016 and March 2019 were collected. We quantitatively analyzed the proton beam delivery time, the difference between the ideal beam delivery time based on a simulated synchrotron magnetic excitation pattern and the actual treatment beam delivery time, frequency corresponding to the baseline shift or drift, and the gating efficiency of the synchrotron-based RGPT system according to the proton beam delivery machine log data. The mean actual beam delivery time was 7.1 min, and the simulated beam delivery time in an ideal environment with the same treatment plan was 2.9 min. The average difference between the actual and simulated beam delivery time per session was 4.3 min. The average frequency of intra-field adjustments corresponding to baseline shift or drift and beam delivery efficiency were 21.7% and 61.8%, respectively. Based on our clinical experience with a synchrotron-based RGPT system, we determined the frequency corresponding to baseline shift or drift and the beam delivery efficiency using the beam delivery machine log data. To maintain treatment accuracy within ± 2.0 mm, intra-field adjustments corresponding to baseline shift or drift were required in approximately 20% of cases. Further improvements in beam delivery efficiency may be realized by shortening the beam delivery time., 13099649 - Analysis of treatment process time for real-time-image gated-spot-scanning proton-beam therapy (RGPT) system.
Takaaki Yoshimura, Shinichi Shimizu, Takayuki Hashimoto, Kentaro Nishioka, Norio Katoh, Tetsuya Inoue, Hiroshi Taguchi, Koichi Yasuda, Taeko Matsuura, Seishin Takao, Masaya Tamura, Yoichi M Ito, Yuto Matsuo, Hiroshi Tamura, Kenji Horita, Kikuo Umegaki, Hiroki Shirato
Journal of applied clinical medical physics, 21, 2, 38, 49, Feb. 2020, [Peer-reviewed], [Lead author], [International Magazine]
English, Scientific journal, We developed a synchrotron-based real-time-image gated-spot-scanning proton-beam therapy (RGPT) system and utilized it to clinically operate on moving tumors in the liver, pancreas, lung, and prostate. When the spot-scanning technique is linked to gating, the beam delivery time with gating can increase, compared to that without gating. We aim to clarify whether the total treatment process can be performed within approximately 30 min (the general time per session in several proton therapy facilities), even for gated-spot-scanning proton-beam delivery with implanted fiducial markers. Data from 152 patients, corresponding to 201 treatment plans and 3577 sessions executed from October 2016 to June 2018, were included in this study. To estimate the treatment process time, we utilized data from proton beam delivery logs during the treatment for each patient. We retrieved data, such as the disease site, total target volume, field size at the isocenter, and the number of layers and spots for each field, from the treatment plans. We quantitatively analyzed the treatment process, which includes the patient load (or setup), bone matching, marker matching, beam delivery, patient unload, and equipment setup, using the data obtained from the log data. Among all the cases, 90 patients used the RGPT system (liver: n = 34; pancreas: n = 5; lung: n = 4; and prostate: n = 47). The mean and standard deviation (SD) of the total treatment process time for the RGPT system was 30.3 ± 7.4 min, while it was 25.9 ± 7.5 min for those without gating treatment, excluding craniospinal irradiation (CSI; head and neck: n = 16, pediatric: n = 31, others: n = 15); for CSI (n = 11) with two or three isocenters, the process time was 59.9 ± 13.9 min. Our results demonstrate that spot-scanning proton therapy with a gating function can be achieved in approximately 30-min time slots., 13099649 - The urethral position may shift due to urethral catheter placement in the treatment planning for prostate radiation therapy.
Yasuhiro Dekura, Kentaro Nishioka, Takayuki Hashimoto, Naoki Miyamoto, Ryusuke Suzuki, Takaaki Yoshimura, Ryuji Matsumoto, Takahiro Osawa, Takashige Abe, Yoichi M Ito, Nobuo Shinohara, Hiroki Shirato, Shinichi Shimizu
Radiation oncology (London, England), 14, 1, 226, 226, 12 Dec. 2019, [Peer-reviewed], [International Magazine]
English, Scientific journal, PURPOSE: To determine the best method to contour the planning organ at risk volume (PRV) for the urethra, this study aimed to investigate the displacement of a Foley catheter in the urethra with a soft and thin guide-wire. METHODS: For each patient, the study used two sets of computed tomography (CT) images for radiation treatment planning (RT-CT): (1) set with a Foley urethral catheter (4.0 mm diameter) plus a guide-wire (0.46 mm diameter) in the first RT-CT and (2) set with a guide-wire alone in the second CT recorded 2 min after the first RT-CT. Using three fiducial markers in the prostate for image fusion, the displacement between the catheter and the guide-wire in the prostatic urethra was calculated. In 155 consecutive patients treated between 2011 and 2017, 5531 slices of RT-CT were evaluated. RESULTS: Assuming that ≥3.0 mm of difference between the catheter and the guide-wire position was a significant displacement, the urethra with the catheter was displaced significantly from the urethra with the guide-wire alone in > 20% of the RT-CT slices in 23.2% (36/155) of the patients. The number of patients who showed ≥3.0 mm anterior displacement with the catheter in ≥20% RT-CT slices was significantly larger at the superior segment (38/155) than at the middle (14/155) and inferior segments (18/155) of the prostatic urethra (p < 0.0167). CONCLUSIONS: The urethral position with a Foley catheter is different from the urethral position with a thin and soft guide-wire in a significant proportion of the patients. This should be taken into account for the PRV of the urethra to ensure precise radiotherapy such as in urethra-sparing radiotherapy., 13099649 - Clinical experience of craniospinal intensity-modulated spot-scanning proton therapy using large fields for central nervous system medulloblastomas and germ cell tumors in children, adolescents, and young adults.
Takayuki Hashimoto, Shinichi Shimizu, Seishin Takao, Shunsuke Terasaka, Akihiro Iguchi, Hiroyuki Kobayashi, Takashi Mori, Takaaki Yoshimura, Yuto Matsuo, Masaya Tamura, Taeko Matsuura, Yoichi M Ito, Rikiya Onimaru, Hiroki Shirato
Journal of radiation research, 60, 4, 527, 537, 01 Jul. 2019, [Peer-reviewed], [International Magazine]
English, Scientific journal, The outcomes of intensity-modulated proton craniospinal irradiation (ipCSI) are unclear. We evaluated the clinical benefit of our newly developed ipCSI system that incorporates two gantry-mounted orthogonal online X-ray imagers with a robotic six-degrees-of-freedom patient table. Nine patients (7-19 years old) were treated with ipCSI. The prescribed dose for CSI ranged from 23.4 to 36.0 Gy (relative biological effectiveness) in 13-20 fractions. Four adolescent and young adult (AYA) patients (15 years or older) were treated with vertebral-body-sparing ipCSI (VBSipCSI). Myelosuppression following VBSipCSI was compared with that of eight AYA patients treated with photon CSI at the same institution previously. The mean homogeneity index (HI) in the nine patients was 0.056 (95% confidence interval: 0.044-0.068). The mean time from the start to the end of all beam delivery was 37 min 39 s ± 2 min 24 s (minimum to maximum: 22 min 49 s - 42 min 51 s). The nadir white blood cell, hemoglobin, and platelet levels during the 4 weeks following the end of the CSI were significantly higher in the VBSipCSI group than in the photon CSI group (P = 0.0071, 0.0453, 0.0024, respectively). The levels at 4 weeks after the end of CSI were significantly higher in the VBSipCSI group than in the photon CSI group (P = 0.0023, 0.0414, 0.0061). Image-guided ipCSI was deliverable in a reasonable time with sufficient HI. Using VBSipCSI, AYA patients experienced a lower incidence of serious acute hematological toxicity than AYA patients treated with photon CSI. - Physical and biological impacts of collimator-scattered protons in spot-scanning proton therapy.
Ueno K, Matsuura T, Hirayama S, Takao S, Ueda H, Matsuo Y, Yoshimura T, Umegaki K
Journal of applied clinical medical physics, 20, 7, 48, 57, Wiley, Jun. 2019, [Peer-reviewed]
Scientific journal - NTCP modeling analysis of acute hematologic toxicity in whole pelvic radiation therapy for gynecologic malignancies - A dosimetric comparison of IMRT and spot-scanning proton therapy (SSPT)
Takaaki Yoshimura, Rumiko Kinoshita, Shunsuke Onodera, Chie Toramatsu, Ryusuke Suzuki, Yoichi M. Ito, Seishin Takao, Taeko Matsuura, Yuka Matsuzaki, Kikuo Umegaki, Hiroki Shirato, Shinichi Shimizu
PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS, 32, 9, 1095, 1102, Sep. 2016, [Peer-reviewed], [Lead author]
English, Scientific journal - Aldehyde oxidase 1 gene is regulated by Nrf2 pathway
Kenichiro Maeda, Takashi Ohno, Shizuka Igarashi, Takaaki Yoshimura, Koji Yamashiro, Masaharu Sakai
GENE, 505, 2, 374, 378, Sep. 2012, [Peer-reviewed]
English, Scientific journal
Other Activities and Achievements
- Event Report of 3rd Hokkaido University Medical AI Symposium
Minghui Tang, Natsuki Sato, Kenji Hirata, Hiroyuki Sugimori, Takaaki Yoshimura, Yoshitaka Bito, Katsuhiko Ogasawara, Kohsuke Kudo, 北海道放射線医学雑誌, 5, 1, 31, 35, 06 Mar. 2025, [Domestic magazines]
Japanese, Meeting report - 適応放射線治療におけるDIRによる線量合算の不確かさ低減のための線量分布変形経路最適化アルゴリズムの開発
藤澤祐太, 田村昌也, 小橋啓司, 橋本孝之, 西岡健太郎, 吉村高明, 前田嘉一, 佐藤義高, 玉村裕保, 青山英史, 日本放射線腫瘍学会高精度放射線外部照射部会学術大会プログラム・抄録集, 38th, 2025 - 多発性脳転移腫瘍に対するSingle-Isocenter Multi Target VMATを用いた定位放射線治療における治療中の患者の動きによる各標的の変位に関する検討
山田亮太, 吉村高明, 吉村高明, 吉村高明, 村田凌, 西岡健太郎, 西岡健太郎, 森崇, 小泉富基, 高橋周平, 山崎理衣, 橋本孝之, 橋本孝之, 青山英史, 日本放射線腫瘍学会高精度放射線外部照射部会学術大会プログラム・抄録集, 38th, 2025 - 体内の動きが膵癌SBRTにおける呼気止め分割CBCT画像に与える影響の評価
温品あい莉, 山田亮太, 吉村高明, 吉村高明, 吉村高明, 金平孝博, 加藤徳雄, 橋本孝之, 青山英史, 日本放射線腫瘍学会高精度放射線外部照射部会学術大会プログラム・抄録集, 38th, 2025 - ポストコロナにおける初期多職種連携教育の取り組み
吉村 高明, 矢野 理香, JART: 日本診療放射線技師会誌, 71, 10, 1256, 1257, Oct. 2024
(公社)日本診療放射線技師会, Japanese - EVALUATION OF THE ACTUAL DOSE DISTRIBUTION USING CBCT FOR ULTRA-HYPOFRACTIONATED INTENSITY MODULATED PROTON THERAPY IN PROSTATE CANCER
Hiroshi Tamura, Keiji Nakazato, Takaaki Yoshimura, Hiroto Yoshimoto, Yuto Matsuo, Keiichi Magota, Seishin Takao, Taeko Matsuura, Takashi Mori, Kentaro Nishioka, Takayuki Hashimoto, Hidefumi Aoyama, International Journal of Particle Therapy, 12, 100456, 100456, Jun. 2024
Elsevier BV - Event Report of 2nd Hokkaido University Medical AI Symposium
Tang M, Hirata K, Sato N, Sugimori H, Yoshimura T, Ogasawara K, Nakaya J, Kudo K, 北海道放射線医学雑誌, 4, 1, 30, 32, 18 Mar. 2024, [Domestic magazines]
Japanese, Meeting report - 非剛体レジストレーションを用いた膀胱腫瘍床描出の試み
西岡健太郎, 長谷川淳, 小橋啓司, 吉村高明, 中里慧二, 森崇, 高橋周平, 宮崎智彦, 橋本孝之, 青山英史, 日本放射線腫瘍学会高精度放射線外部照射部会学術大会プログラム・抄録集, 37th (CD-ROM), 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 - 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, 村山迪史, 加賀早苗, 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, 日本超音波検査学会学術集会講演抄録集(Web), 49th, 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 - 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, 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, 2024
(公社)日本診療放射線技師会, Japanese - Effect of patient factors on irradiation time during respiratory-gated VMAT for body
山田亮太, 吉村高明, 吉村高明, 吉村高明, 田村弘詞, 一宇佑太, 横川航平, 加藤徳雄, 日本診療放射線技師会誌, 71, 10, 1220, 1220, 2024
(公社)日本診療放射線技師会, Japanese - Preliminary Exploration for Constructing an 18F-FDG PET/CT Report Generation System
南和孝, 吉村高明, 平田健司, 吉村高明, 平田健司, 吉村高明, 平田健司, 吉村高明, 平田健司, 甲木晶枝, 植竹望, 唐明輝, 渡邊史郎, 工藤與亮, 唐明輝, 渡邊史郎, 工藤與亮, 渡邊史郎, 工藤與亮, 日本診療放射線技師会誌, 71, 10, 1152, 1152, 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, 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, 2024
(公社)日本診療放射線技師会, 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, 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, 48, 48, Nov. 2023
(公社)日本放射線技術学会-北海道支部, 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 - 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 - 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 - 【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 - PO-2122 Effect of early fractional lymphocyte loss on lymphopenia probability models for NSCLC
T. Kanehira, H. Taguchi, N. Katoh, Y. Uchinami, T. Yoshimura, M. Tamura, R. Suzuki, T. Hashimoto, H. Aoyama, Radiotherapy and Oncology, 182, S1909, S1911, May 2023, [Peer-reviewed], [International Magazine]
Elsevier BV, English, Summary international conference - 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 - 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, 94, 54, 55, Apr. 2023
(公社)日本放射線技術学会-北海道支部, Japanese - Cine-MRIを用いた3D-Convolutional Neural Network(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, 94, 56, 57, Apr. 2023
(公社)日本放射線技術学会-北海道支部, Japanese - 体幹部定位放射線治療時の呼気止め分割CBCTにおける呼吸再現性と輪郭の評価
山田 亮太, 吉村 高明, 一宇 佑太, 若林 倭, 松尾 勇斗, 孫田 惠一, 加藤 徳雄, 日本放射線技術学会総会学術大会予稿集, 79回, 190, 190, Mar. 2023
(公社)日本放射線技術学会, Japanese - Event Report of 1st Hokkaido University Medical AI Symposium
Tang M, Hirata K, Sugimori H, Yoshimura T, Ogasawara K, Nakaya J, Kudo K, 北海道放射線医学雑誌, 3, 1, 41, 44, Mar. 2023
Japanese, Meeting report - 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, 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 - 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 - 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 - Estimation of Left and Right Ventricular Ejection Fraction from cine-MRI Using 3D-CNN
猪股壮一郎, 吉村高明, TANG Minghui, 杉森博行, 北海道放射線技術雑誌(Web), 94, 2023 - 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 - 小児髄芽腫陽子線治療における複数の晩期有害事象を考慮した費用対効果分析モデルの検討
北 龍樹, 森井 康博, 吉村 高明, 小笠原 克彦, 医療情報学連合大会論文集, 42回, 775, 776, Nov. 2022
(一社)日本医療情報学会, Japanese - 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, 93, 27, 27, Oct. 2022
(公社)日本放射線技術学会-北海道支部, Japanese - 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, 93, 27, 27, Oct. 2022
(公社)日本放射線技術学会-北海道支部, 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 - HyperArcを用いた多発性脳転移腫瘍に対する脳定位放射線治療中の各標的の位置ずれに関する検討
山田 亮太, 吉村 高明, 若林 倭, 金平 孝博, 森 崇, 西岡 健太郎, 青山 英史, 日本放射線技術学会総会学術大会予稿集, 78回, 161, 162, Mar. 2022
(公社)日本放射線技術学会, Japanese - 超解像深層学習を用いたPET検査における被ばく線量低減に向けた検討
長谷川 淳, 吉村 高明, 孫田 恵一, 木村 理奈, 渡邊 史郎, 平田 健司, 日本放射線技術学会総会学術大会予稿集, 78回, 160, 160, Mar. 2022
(公社)日本放射線技術学会, Japanese - 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 - 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, Nov. 2021, [Peer-reviewed], [Lead author]
Elsevier BV, English - SSIMを用いたPET画像に対する再構成条件の最適化
葛西 悠平, 名雲 渓太, Maniawski PJ, 孫田 惠一, 平田 健司, 吉村 高明, 山品 博子, 北海道放射線技術雑誌, 91, 52, 52, Oct. 2021
(公社)日本放射線技術学会-北海道支部, Japanese - 尿量の経日変化および前立腺体積が放射線治療時の前立腺の位置に与える影響(The Impact of Daily Urinary Volume Variations and Prostate Volume on Prostate Position During Radiotherapy)
Nishioka Kentaro, Hashimoto Takayuki, Yokota Isao, Katoh Norio, Kinoshita Rumiko, Yasuda Koichi, Takao Seishin, Yoshimura Takaaki, Aoyama Hidefumi, Shimizu Shinichi, 日本医学放射線学会学術集会抄録集, 80回, S193, S194, Mar. 2021
(公社)日本医学放射線学会, English - The Impact of Daily Urinary Volume Variations and Prostate Volume on Prostate Position During Radiotherapy(和訳中)
Nishioka Kentaro, Hashimoto Takayuki, Yokota Isao, Katoh Norio, Kinoshita Rumiko, Yasuda Koichi, Takao Seishin, Yoshimura Takaaki, Aoyama Hidefumi, Shimizu Shinichi, 日本医学放射線学会学術集会抄録集, 80回, S193, S194, Mar. 2021
(公社)日本医学放射線学会, English - SSIMを用いたPET画像に対する再構成条件の最適化
葛西悠平, 名雲渓太, MANIAWSKI PJ, 孫田惠一, 平田健司, 吉村高明, 山品博子, 北海道放射線技術雑誌(Web), 91, 91, 52, 52, 2021
(公社)日本放射線技術学会-北海道支部, 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, 2021 - 小児髄芽腫陽子線治療における放射線誘発性二次がんに伴う費用分析のための決定木モデルの構築
田森 帆乃夏, 吉村 高明, 森井 康博, 小笠原 克彦, 医療情報学連合大会論文集, 40回, 642, 643, Nov. 2020
(一社)日本医療情報学会, Japanese - Normal Tissue Complication Probability for Hematologic and Gastrointestinal Toxicity in Postoperative Whole Pelvic Radiotherapy for Gynecologic Malignancies using Intensity Modulated Proton Therapy with Robust Optimization
T. Yoshimura, R. Yamada, R. Kinoshita, H. Tamura, T. Matsuura, S. Takao, M. Tamura, S. Tanaka, N. Nagae, K. Kobashi, H. Aoyama, S. Shimizu, International Journal of Radiation Oncology*Biology*Physics, 108, 3, e494, e494, Nov. 2020, [Peer-reviewed]
Elsevier BV, Summary international conference - Automatic Bladder Delineation on MR Images Using a Convolution Neural Network for Online Image-Guided Radiotherapy
K. Nishioka, Y. Nomura, T. Hashimoto, R. Kinoshita, N. Katoh, H. Taguchi, K. Yasuda, T. Mori, Y. Uchinami, M. Otsuka, T. Matsuura, S. Takao, R. Suzuki, S. Tanaka, T. Yoshimura, H. Aoyama, S. Shimizu, International Journal of Radiation Oncology*Biology*Physics, 108, 3, e288, e289, Nov. 2020, [Peer-reviewed]
Elsevier BV, Summary international conference - Construction of a decision tree model for cost analysis associated with radiation-induced cancer in proton therapy for pediatric medulloblastoma
田森帆乃夏, 吉村高明, 森井康博, 小笠原克彦, 医療情報学連合大会論文集(CD-ROM), 40th, 2020 - OC-078: Development of Gated Proton Imaging System for Moving Target
S. Tanaka, N. Miyamoto, T. Nishio, T. Yoshimura, S. Takao, Y. Matsuo, S. Shimizu, H. Shirato, T. Matsuura, Radiotherapy and Oncology, 141, S32, S33, Dec. 2019, [Peer-reviewed]
Elsevier BV, Summary international conference - The Potential Benefit of Adaptive Intensity Modulated Proton Therapy in Nasopharyngeal Carcinoma: Planning Comparison Study
H. Minatogawa, K. Yasuda, T. Matsuura, R. Onimaru, T. Yoshimura, S. Takao, Y. Matsuo, Y. Dekura, R. Suzuki, M. Tamura, N. Miyamoto, S. Shimizu, H. Shirato, International Journal of Radiation Oncology*Biology*Physics, 105, 1, E394, E394, Sep. 2019, [Peer-reviewed]
Elsevier BV, Summary international conference - Analysis of Beam Delivery Times and Dose Rates for the Treatment of Mobile Tumors Using Real Time Image Gated Spot-Scanning Proton Beam Therapy
S. Shimizu, T. Yoshimura, N. Katoh, T. Inoue, T. Hashimoto, K. Nishioka, S. Takao, T. Matsuura, N. Miyamoto, Y.M. Ito, K. Umegaki, H. Shirato, International Journal of Radiation Oncology*Biology*Physics, 102, 3, S182, S183, Nov. 2018, [Peer-reviewed]
Elsevier BV, Summary international conference - Intensity-Modulated Proton Therapy with Dose Painting based on Hypoxia Imaging for Nasopharyngeal Cancer
K. Yasuda, S. Takao, Y. Matsuo, T. Yoshimura, M. Tamura, H. Minatogawa, Y. Dekura, T. Matsuura, R. Onimaru, T. Shiga, S. Shimizu, K. Umegaki, H. Shirato, International Journal of Radiation Oncology*Biology*Physics, 102, 3, e378, e378, Nov. 2018, [Peer-reviewed]
Elsevier BV, Summary international conference - Big Data Analysis of Treatment Process Time for the Real-Time-Image Gated-Spot-Scanning Proton-Beam Therapy (RGPT) System
T. Yoshimura, S. Shimizu, T. Hashimoto, N. Katoh, T. Inoue, K. Nishioka, T. Matsuura, S. Takao, M. Tamura, H. Tamura, K. Horita, K. Umegaki, H. Shirato, International Journal of Radiation Oncology*Biology*Physics, 102, 3, e501, e502, Nov. 2018, [Peer-reviewed]
Elsevier BV, Summary international conference - Prostate position deviation analysis using real-time-image gated spot-scanning proton therapy (RGPT) system for prostate cancer
TAMURA Hiroshi, TAMURA Hiroshi, SHIMIZU Shinichi, NISHIOKA Kentaro, HASHIMOTO Takayuki, YOSHIMURA Takaaki, MATSUO Yuto, MATSUURA Taeko, MATSUURA Taeko, TAKAO Seishin, UMEGAKI Kikuo, UMEGAKI Kikuo, SHIRATO Hiroki, 医学物理 Supplement, 38, 1, 2018 - An investigation of biological impact caused by using a collimator in pencil beam scanning proton therapy.
UENO Koki, MATSUURA Taeko, MATSUURA Taeko, TAKAO Seishin, HIRAYAMA Shusuke, HIRAYAMA Shusuke, UEDA Hideaki, YOSHIMURA Takaaki, MATSUO Yuto, UMEGAKI Kikuo, UMEGAKI Kikuo, 医学物理 Supplement, 38, 1, 71, 2018
English - Time saving lateral profile validation procedure utilizing simplified Monte Carlo calculation for patient specific QA of proton beam therapy
YAMADA Takahiro, TAKAYANAGI Taisuke, MATSUURA Taeko, TAKAO Seishin, YOSHIMURA Takaaki, HIRAYAMA Shusuke, FUJII Takaaki, UMEGAKI Kikuo, 医学物理 Supplement, 37, 1, 2017 - Commissioning of Eclipse Treatment Planning System for Spot-scanning Nozzle in Hokkaido University Proton Therapy Center
YOSHIMURA Takaaki, YOSHIMURA Takaaki, MATSUO Yuto, MATSUURA Taeko, MATSUURA Taeko, TAKAO Seishin, FUJII Takaaki, HIRAYAMA Shusuke, UMEGAKI Kikuo, SHIMIZU Shinichi, SHIRATO Hiroki, 医学物理 Supplement, 37, 1, 176, 2017
English - Analysis of Durations and Dose Rates for Treatment of Moving Liver Tumors Using Real-Time Image Gated Spot Scanning Proton Beam Therapy
S. Shimizu, N. Katoh, T. Hashimoto, K. Nishioka, T. Yoshimura, S. Takao, T. Matsuura, N. Miyamoto, K. Umegaki, H. Shirato, International Journal of Radiation Oncology*Biology*Physics, 96, 2, S211, S212, Oct. 2016, [Peer-reviewed]
Elsevier BV, Summary international conference - Autonomous Quality Assurance for Spot-Scanning Proton Therapy and SRS/SBRT Using Radioluminescent Phantoms, Optical Imaging, and Machine Vision
C.H. Jenkins, Y. Yang, S.J. Yu, H. Yu, Y. Matsuzaki, T. Yoshimura, Y. Fujii, K. Umegaki, H. Shirato, L. Xing, International Journal of Radiation Oncology*Biology*Physics, 96, 2, S170, S171, Oct. 2016, [Peer-reviewed]
Elsevier BV, Summary international conference - TU-FG-BRB-12: Real-Time Visualization of Discrete Spot Scanning Proton Therapy Beam for Quality Assurance
Y Matsuzaki, C Jenkins, Y Yang, T Yoshimura, Y Fujii, K Umegaki, L Xing, Medical Physics, 43, 6Part35, 3758, 3759, Jun. 2016, [Peer-reviewed]
Wiley, Summary international conference - Treatment Time and Dose Rate Analysis for Respiratory Moving Liver Tumor Using Real-time-image Gated Spot Scanning Proton Beam Therapy System
SHIMIZU Shinichi, KATOH Norio, TAKAO Seishin, MATSUURA Taeko, MIYAMOTO Naoki, HASHIMOTO Takayuki, NISHIOKA Kentaro, YOSHIMURA Takaaki, UMEGAKI Kikuo, SHIRATO Hiroki, 日本医学放射線学会総会抄録集, 75th, S225, S225, 29 Feb. 2016
(公社)日本医学放射線学会, English - NTCP Modeling Analysis of Acute Hematologic Toxicity in Whole-Pelvic Radiation Therapy for Gynecologic Malignancies: A Dosimetric Comparison of IMRT and Spot-Scanning Proton Therapy
T. Yoshimura, R. Kinoshita, S. Onodera, C. Toramatsu, R. Suzuki, Y.M. Ito, S. Takao, T. Matsuura, Y. Matsuzaki, S. Shimizu, K. Umegaki, H. Shirato, International Journal of Radiation Oncology*Biology*Physics, 93, 3, E254, E254, Nov. 2015, [Peer-reviewed]
Elsevier BV, Summary international conference - Development of a Real-Time Image Gated Proton Beam Therapy (RGPT) System and Its Initial Clinical Application to Respiratory Moving Liver Tumors
S. Shimizu, N. Katoh, S. Takao, T. Matsuura, N. Miyamoto, T. Hashimoto, K. Nishioka, T. Yoshimura, Y. Matsuzaki, R. Kinoshita, Y. Nishikawa, R. Onimaru, K. Umegaki, H. Shirato, International Journal of Radiation Oncology*Biology*Physics, 93, 3, S42, S42, Nov. 2015
Elsevier BV - 婦人科腫瘍におけるIMRTの利用経験
小野寺 俊輔, 吉村 高明, Japanese Journal of Radiology, 33, Supplement, 11, 11, 2015
(公社)日本医学放射線学会, Japanese - Initial dosimetoric study of whole pelvic irradiation using spot scanning proton therapy
YOSHIMURA Takaaki, ONODERA Shunsuke, TORAMATSU Chie, KINOSHITA Rumiko, SHIMIZU Shinichi, UMEGAKI Kikuo, SHIRATO Hiroki, 医学物理 Supplement, 34, 1, 143, 2014
English - 頸部転移性骨腫瘍におけるスポットスキャニング照射法を用いた陽子線治療の線量分布評価
吉村 高明, 安田 耕一, 寅松 千枝, 高尾 聖心, 松浦 妙子, 二本木 英明, 木下 留美子, 鬼丸 力也, 白土 博樹, 石川 正純, Japanese Journal of Radiology, 32, Supplement, 7, 7, 2014
(公社)日本医学放射線学会, Japanese
Books and other publications
Courses
- Medical Imaging EquipmentⅡ
Hokkaido University
Apr. 2025 - Present - Overview of Medical Artificial Intelligence (Inter-Faculty Classes(General Subject):Inter-Disciplinary Sciences)
Hokkaido University
Apr. 2025 - Present - Inter-Graduate School Classes(Educational Program):Health, Society and Environment Health, Society and Environment IV _Health Sciences
Hokkaido University
Apr. 2024 - Present, Japan - Health Sciences
Hokkaido University
Apr. 2024 - Present, Postgraduate courses, Japan - Laboratory Exercise in Radiochemistry
Hokkaido University
Apr. 2024 - Present, Undergraduate special subjects, Japan - Practical medical imaging
Hokkaido University
Apr. 2024 - Present, Undergraduate special subjects, Japan - Principles of Medicine Medical AI: Data Handling Workshops
Hokkaido University
Apr. 2023 - Present, Postgraduate courses, Japan - Principles of Medicine Medical AI: AI for Diagnosis and Therapy
Hokkaido University
Apr. 2023 - Present, Postgraduate courses, Japan - Principles of Medicine Medical AI: Medical AI Seminar
Hokkaido University
Apr. 2023 - Present, Postgraduate courses, Japan - Basic Principles of Medicine Medical AI: Medical AI Seminar
Hokkaido University
Apr. 2023 - Present, Postgraduate courses, Japan - Basic Principles of Medicine Medical AI: AI for Diagnosis and Therapy
Hokkaido University
Apr. 2023 - Present, Postgraduate courses, Japan - Basic Principles of Medicine Medical AI: Data Handling Workshops
Hokkaido University
Apr. 2023 - Present, Postgraduate courses, Japan - Supervised Individual Research of Health Sciences
Hokkaido University
Apr. 2022 - Present, Postgraduate courses, Japan - Supervised Individual Study of Health Sciences
Hokkaido University
Apr. 2022 - Present, Postgraduate courses, Japan - Introduction to Health Sciences and Medicine
Hokkaido University
Apr. 2021 - Present - Medical Informatics
Hokkaido University
Apr. 2019 - Present - Practice in Medical Equipment
Hokkaido University
Apr. 2019 - Present - Practice in Radiation Therapy Technology
Hokkaido University
Apr. 2019 - Present - Practice in Basic Clinical Imaging
Hokkaido University
Apr. 2019 - Present - Radiation Oncology
Hokkaido University
Apr. 2019 - Present - Seminar on Anatomy
Hokkaido University
Apr. 2019 - Present - Clinical Practice Ⅰ～Ⅵ
Hokkaido University
Apr. 2019 - Present - Graduation Research
Hokkaido University
Apr. 2019 - Present - Practice in Radiation Detection and Dosimetry
Hokkaido University
Apr. 2019 - Present
Affiliated academic society
- Jan. 2024 - Present
Japanese Association for Medical Artificial Intelligence - Apr. 2023 - Present
Japan Association for Medical Informatics - Apr. 2019 - Present
Particle Therapy Co-Operative Group (PTCOG) - Apr. 2017 - Present
JAPANESE SOCIETY OF RADIOLOGICAL TECHNOLOGY - Jun. 2011 - Present
JAPANESE SOCIETY FOR RADIATION ONCOLOGY - Apr. 2011 - Present
JAPAN SOCIETY OF MEDICAL PHYSICS - Apr. 2011 - Present
THE JAPAN ASSOCIATION OF RADIOLOGICAL TECHNOLOGISTS
Research Themes
- An Integrated Approach for Uncertainty Reduction of DIR-based Dose Accumulation in Adaptive Radiotherapy
Grants-in-Aid for Scientific Research
01 Apr. 2024 - 31 Mar. 2027
小橋 啓司, 橋本 孝之, 吉村 高明, 西岡 健太郎
Japan Society for the Promotion of Science, Grant-in-Aid for Scientific Research (C), Hokkaido University, 24K10878 - Development of MR-only urethra-sparing proton treatment planning method for prostate cancer
Grants-in-Aid for Scientific Research
01 Apr. 2022 - 31 Mar. 2026
吉村 高明
Japan Society for the Promotion of Science, Grant-in-Aid for Early-Career Scientists, Hokkaido University, 22K15797 - AIを活用した小児がん陽子線照射のトリプルハイブリッド遠隔治療計画システムの開発
科学研究費助成事業 基盤研究(C)
01 Apr. 2022 - 31 Mar. 2025
橋本 孝之, 高尾 聖心, 小橋 啓司, 吉村 高明, 西岡 健太郎
日本学術振興会, 基盤研究(C), 北海道大学, 22K07631 - ADL動作評価・治療用パーソナライズアプリの開発
札幌バイオシーズ事業化支援事業（札幌バイオシーズ事業化支援補助金）
Jul. 2024 - Mar. 2025
澤村大輔, 杉森博行, 石田知也, 吉村高明, 稲垣侑士, 田村洋史
Nothern Advancement Center for Science & Technology, Coinvestigator, Competitive research funding - PET検査における患者被ばく線量低減診断補助システムの開発
橋渡し研究プログラム/北海道大学拠点/シーズA
Jul. 2024 - Mar. 2025
Yoshimura T, Sugimori H, Hirata K, Kobashi K, Kuge Y, Kudo K
Japan Agency for Medical Research and Development (AMED), Hokkaido University, Principal investigator, Competitive research funding - Support for the employment of technical assistants over life events
Apr. 2023 - Mar. 2025
Takaaki Yoshimura
Office of Diversity, Equity, and Inclusion, Hokkaido University, Hokkaido University, Principal investigator, Others - 超低線量PET画像診断支援システムと生成AIの融合
The 10th Hokkaido University Cross-Departmental Symposium Bronze Prize
Sep. 2024 - Sep. 2024
吉村高明, 杉森博行, 藤後廉, 平田健司
Hokkaido University, Hokkaido University - 医療AIを活用した前立腺癌に対する寡分割Adaptive陽子線治療技術開発
Promotion for Young Research Talent and Network
Aug. 2023 - Mar. 2024
Takaaki Yoshimura
Northern Advancement Center for Science & Technology (NOASTEC), Hokkaido University, Principal investigator, Competitive research funding - 超低投与線量PET検査を実現するDeep Learning技術の確立
Next Generation Fostering Grant
Apr. 2023 - Mar. 2024
Yoshimura T
Akiyama Life Science Foundation, Principal investigator, Competitive research funding - 超解像深層学習を用いた低投与線量PET検査の実現に向けたシステム開発
The 9th Hokkaido University Cross-Departmental Symposium Bronze Prize
Oct. 2023 - Oct. 2023
Yoshimura T, Sugimori H, Hirata K, Togo R
Hokkaido University, Hokkaido University, Principal investigator - Development of Non-invasive Diagnosis Support Technology for Prostate Cancer using Artificial Intelligence
Promotion for Young Research Talent and Network
Aug. 2022 - Mar. 2023
Takaaki Yoshimura
Northern Advancement Center for Science & Technology (NOASTEC), Hokkaido University, Principal investigator - 人獣連携によりMR画像-CT画像変換を高精度化する技術の開発~前立腺癌に対するMR画像誘導即時適応尿道線量低減陽子線治療の実現に向けて~
第8回北海道大学部局横断シンポジウム研究助成採択 銅賞
Oct. 2022 - Oct. 2022
Yoshimura T, Shinbo G, Matsuura T, Hashimoto T, Nishioka K, Mori T, Kanehira T, Sugimori H
Hokkaido University, Hokkaido University, Principal investigator - 非侵襲的に前立腺癌と正常組織を識別するMRI画像を用いた放射線治療計画技術の開発
Early-Career Scientists
01 Apr. 2018 - 31 Mar. 2022
Yoshimura Takaaki
Urethra-sparing radiation therapy for localized prostate cancer can reduce the risk of radiation-induced genitourinary toxicity by intentionally underdosing the periurethral transitional zone. We proposed a method using post-urination high resolution T2-weighted 3.0 T MRI (PU-MRI) in urethra-sparing treatment planning. Our results demonstrated that the inter-operator prostatic urinary tract Region of Interest (ROI) matched with a high accuracy, not only in CT with a urethral catheter, but also in PU-MRI. Thus, PU-MRI is useful in identifying the urethra non-invasively, without using a urethral catheter. Moreover, compared with the conventional clinically applied plans, urethra-sparing proton therapy have potential clinical advantages and may reduce the risk of genitourinary toxicities, while maintaining the same tumor control probability and normal tissue complication probability in the rectum and bladder. And there were expectations for the promotion of future research.
Japan Society for the Promotion of Science (JSPS), Grant-in-Aid for Early-Career Scientists, Hokkaido University, Principal investigator, Competitive research funding, 18K15577 - 人工知能を用いた前立腺がんに対する 動体追跡陽子線治療計画技術の開発
NOASTEC Presidential Prize
Mar. 2022 - Mar. 2022
Yoshimura T
Northern Advancement Center for Science & Technology (NOASTEC), Hokkaido University, Principal investigator - PET検査における超解像深層学習を用いた被ばく線量低減の試み
Grants-in-Aid for Regional R&D Proposal-Based Program
Aug. 2021 - Mar. 2022
Yoshimura Takaaki
Nothern Advancement Center for Science & Technology, Principal investigator - Development of minimum invasive real-time markerless image guided radiotherapy technique using minimum space information.
Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B)
01 Apr. 2018 - 31 Mar. 2021
Shimizu Shinichi
In the real-time tumor tracking radiotherapy, feature point information, that is used for gating, is obtained by fluoroscopic X-ray images. There still problems exist:1.X-ray exposure from diagnostic X ray that used for gating 2.insertion of a gold marker etc. as a feature pointReduce or eliminate X-ray exposure by making the observing area through diagnostic X-rays as small as possible to acquire target movement information or establishing the method of processing gating information obtained using MRI technology without using fluoroscopy were considered to be the goal of this study.
X-rays are still optimal for acquiring organ motion information within human body necessary for gating treatment in real time, but a method for limiting the range was achieved in this study. In addition, we succeeded in trying to visualize the position of organs with MRI image data without using X-rays when preparing for treatment, and there were expectations for the promotion of future research.
Japan Society for the Promotion of Science, Grant-in-Aid for Scientific Research (B), Hokkaido University, 18H02758 - 人工知能を用いた前立腺癌に対する動体追跡陽子線治療計画技術の開発
Grants-in-Aid for Regional R&D Proposal-Based Program
Aug. 2020 - Mar. 2021
Takaaki Yoshimura
Nothern Advancement Center for Science & Technology, Principal investigator - 婦人科腫瘍に対する陽子線治療の予後予測をin-slico surrogate markerに用いた治療計画技術の開発
Grants-in-Aid for Regional R&D Proposal-Based Program
Aug. 2019 - Mar. 2020
Takaaki Yoshimura
Nothern Advancement Center for Science & Technology, Principal investigator, Competitive research funding - the 58th PTCOG Travel Fellowship Award
PTCOG Travel Fellowship Program
Jun. 2019 - Jun. 2019
Yoshimura T
Particle Therapy Co-Operative Group, Hokkaido University, Principal investigator - The evaluation of late adverse effects of adjuvant radiotherapy using in silico sarrogate end marker
Grants-in-Aid for Scientific Research Grant-in-Aid for Young Scientists (B)
01 Apr. 2015 - 31 Mar. 2019
Kinoshita Rumiko, Yoshimura Takaaki
IMRT and spot sccaning proton therapy(SSPT) plans were created for 13 gynecologic malignancy patients who had received hysterectomies and irradiation to the pelvic region. The Bone marrow(BM）dose using SSPT was significantly lower than with IMRT. The NTCP modeling analysis showed that the risk of grade≧3 hematologic toxicity (HT3+) using SSPT was significantly lower than with IMRT The SSPT achieves significant reductions in the dose to BM without compromising target coverage, compared with IMRT. The NTCP value for HT3+ in SSPT was significantly lower than in IMRT.
Dose volume anlaysis was carried out to eveluate the dose to the internal mammary lymph node(IMN) and normal tissure using 50 patients who reieved breaset concervation therapy(BCT).
The IMN-PTV mean doses were less than 30 Gy in all cases.The IMN dose in standard tangential irradiation for BCT is insufficient for treatment.
Japan Society for the Promotion of Science, Grant-in-Aid for Young Scientists (B), Hokkaido University, 15K19760
