Tang Minghui
| Faculty of Medicine Internal Medicine Department of Radiology | Specially Appointed Lecturer |
Last Updated :2025/06/13
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■Career
Career
Educational Background
■Research activity information
Awards
- Nov. 2022, 第12回 核医学画像解析研究会, Outstanding research award
SRCNN を用いた短時間収集 PET 画像の画質改善
Hiroki Endo;Takaaki Yoshimura;Minghui Tang;Hiroyuki Sugimori;Atsushi Hasegawa;Shoki Kogame;Keiichi Magota;Rina Kimura;Shiro Watanabe;Kenji Hirata;Kohsuke Kudo - Nov. 2022, Medical AI Research and Development Center, Hokkaido University Hospital, Outstanding research award
AIを用いた頭蓋外内バイパス術の手術スキルの評価
髙張 廉, 杉森 博行, 吉村 高明, 小笠原 克彦, 杉山 拓, 唐 明輝 - Sep. 2022, Japan Society of Medical Physics, Encouragement Award The 124th Meeting of Japan Society of Medical Physics
PET検査における医療被ばく低減を目指したSRCNNの構築
Hiroki Endo, Takaaki Yoshimura, Minghui Tang, Hiroyuki Sugimori, Atsushi Hasegawa, Shoki Kogame, Keiichi Magota, Rina Kimura, Shiro Watanabe, Kenji Hirata, and Kohsuke Kudo - Mar. 2018, 日本磁気共鳴医学会, ISMRM Travel Award
Torque abnormality of Ti alloy in a static magnetic field - Oct. 2015, Japan Society of Medical Physics, Excellent Research Award The 110th Meeting of Japan Society of Medical Physics
MRI検査における熱傷事故を予知する方法
Minghui Tang - Apr. 2015, Japan Society of Medical Physics, CyPos Presentation Award The 109th Meeting of Japan Society of Medical Physics
Brain arteriolar elastic mapping obtained from magnetic resonance signal fluctuations - Application to dementia patients –
Minghui Tang
Papers
- Alginate Oligosaccharide and Gut Microbiota: Exploring the Key to Health
Meiling Song, Lin Chen, Chen Dong, Minghui Tang, Yuan Wei, Depeng Lv, Quancai Li, Zhen Chen
Nutrients, 11 Jun. 2025
Scientific journal - Prediction of Post-Bath Body Temperature Using Fuzzy Inference Systems with Hydrotherapy Data
Feng Han, Minghui Tang, Ziheng Zhang, Kenji Hirata, Yoji Okugawa, Yunosuke Matsuda, Jun Nakaya, Katsuhiko Ogasawara, Kohsuke Kudo
Healthcare, 23 Apr. 2025, [Peer-reviewed], [Corresponding author]
Scientific journal - Continual Self-supervised Learning Considering Medical Domain Knowledge in Chest CT Images
Ren Tasai, Guang Li, Ren Togo, Minghui Tang, Takaaki Yoshimura, Hiroyuki Sugimori, Kenji Hirata, Takahiro Ogawa, Kohsuke Kudo, Miki Haseyama
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1, 5, IEEE, 06 Apr. 2025
International conference proceedings - 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]
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 - Artificial Intelligence for Patient Safety and Surgical Education in Neurosurgery
Taku Sugiyama, Hiroyuki Sugimori, Mighui Tang, Miki Fujimura
JMA Journal, Aug. 2024, [Peer-reviewed] - The Effectiveness of Semi-Supervised Learning Techniques in Identifying Calcifications in X-ray Mammography and the Impact of Different Classification Probabilities
Miu Sakaida, Takaaki Yoshimura, Minghui Tang, Shota Ichikawa, Hiroyuki Sugimori, Kenji Hirata, Kohsuke Kudo
Applied Sciences, 14, 14, 5968, 5968, MDPI AG, 09 Jul. 2024, [Peer-reviewed]
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], [Lead author], [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. - Imaging of 17O-labeled Water Using Fast T2 Mapping with T2-preparation: A Phantom Study.
Hiroyuki Kameda, Yumi Nakada, Yuta Urushibata, Hiroyuki Sugimori, Takaaki Fujii, Naoya Kinota, Daisuke Kato, Minghui Tang, Keita Sakamoto, Kohsuke Kudo
Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine, 15 Mar. 2024, [Peer-reviewed], [Domestic magazines]
English, Scientific journal, 17O-labeled water is a T2-shortening contrast agent used in proton MRI and is a promising method for visualizing cerebrospinal fluid (CSF) dynamics because it provides long-term tracking of water molecules. However, various external factors reduce the accuracy of 17O-concentration measurements using conventional signal-intensity-based methods. In addition, T2 mapping, which is expected to provide a stable assessment, is generally limited to temporal-spatial resolution. We developed the T2-prepared based on T2 mapping used in cardiac imaging to adapt to long T2 values and tested whether it could accurately measure 17O-concentration in the CSF using a phantom. The results showed that 17O-concentration in a fluid mimicking CSF could be evaluated with an accuracy comparable to conventional T2-mapping (Carr-Purcell-Meiboom-Gill multi-echo spin-echo method). This method allows 17O-imaging with a high temporal resolution and stability in proton MRI. This imaging technique may be promising for visualizing CSF dynamics using 17O-labeled water. - Deep learning-based video-analysis of instrument motion in microvascular anastomosis training.
Taku Sugiyama, Hiroyuki Sugimori, Minghui Tang, Yasuhiro Ito, Masayuki Gekka, Haruto Uchino, Masaki Ito, Katsuhiko Ogasawara, Miki Fujimura
Acta neurochirurgica, 166, 1, 6, 6, 12 Jan. 2024, [Peer-reviewed], [International Magazine]
English, Scientific journal, PURPOSE: Attaining sufficient microsurgical skills is paramount for neurosurgical trainees. Kinematic analysis of surgical instruments using video offers the potential for an objective assessment of microsurgical proficiency, thereby enhancing surgical training and patient safety. The purposes of this study were to develop a deep-learning-based automated instrument tip-detection algorithm, and to validate its performance in microvascular anastomosis training. METHODS: An automated instrument tip-tracking algorithm was developed and trained using YOLOv2, based on clinical microsurgical videos and microvascular anastomosis practice videos. With this model, we measured motion economy (procedural time and path distance) and motion smoothness (normalized jerk index) during the task of suturing artificial blood vessels for end-to-side anastomosis. These parameters were validated using traditional criteria-based rating scales and were compared across surgeons with varying microsurgical experience (novice, intermediate, and expert). The suturing task was deconstructed into four distinct phases, and parameters within each phase were compared between novice and expert surgeons. RESULTS: The high accuracy of the developed model was indicated by a mean Dice similarity coefficient of 0.87. Deep learning-based parameters (procedural time, path distance, and normalized jerk index) exhibited correlations with traditional criteria-based rating scales and surgeons' years of experience. Experts completed the suturing task faster than novices. The total path distance for the right (dominant) side instrument movement was shorter for experts compared to novices. However, for the left (non-dominant) side, differences between the two groups were observed only in specific phases. The normalized jerk index for both the right and left sides was significantly lower in the expert than in the novice groups, and receiver operating characteristic analysis showed strong discriminative ability. CONCLUSION: The deep learning-based kinematic analytic approach for surgical instruments proves beneficial in assessing performance in microvascular anastomosis. Moreover, this methodology can be adapted for use in clinical settings. - 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]
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. - Tissue Acceleration as a Novel Metric for Surgical Performance During Carotid Endarterectomy.
Taku Sugiyama, Masaki Ito, Hiroyuki Sugimori, Minghui Tang, Toshitaka Nakamura, Katsuhiko Ogasawara, Hitoshi Matsuzawa, Naoki Nakayama, Sanju Lama, Garnette R Sutherland, Miki Fujimura
Operative neurosurgery (Hagerstown, Md.), 04 Jul. 2023, [Peer-reviewed], [International Magazine]
English, Scientific journal, BACKGROUND AND OBJECTIVES: Gentle tissue handling to avoid excessive motion of affected fragile vessels during surgical dissection is essential for both surgeon proficiency and patient safety during carotid endarterectomy (CEA). However, a void remains in the quantification of these aspects during surgery. The video-based measurement of tissue acceleration is presented as a novel metric for the objective assessment of surgical performance. This study aimed to evaluate whether such metrics correlate with both surgeons' skill proficiency and adverse events during CEA. METHODS: In a retrospective study including 117 patients who underwent CEA, acceleration of the carotid artery was measured during exposure through a video-based analysis. Tissue acceleration values and threshold violation error frequencies were analyzed and compared among the surgeon groups with different surgical experience (3 groups: novice, intermediate, and expert). Multiple patient-related variables, surgeon groups, and video-based surgical performance parameters were compared between the patients with and without adverse events during CEA. RESULTS: Eleven patients (9.4%) experienced adverse events after CEA, and the rate of adverse events significantly correlated with the surgeon group. The mean maximum tissue acceleration and number of errors during surgical tasks significantly decreased from novice, to intermediate, to expert surgeons, and stepwise discriminant analysis showed that the combined use of surgical performance factors could accurately discriminate between surgeon groups. The multivariate logistic regression analysis revealed that the number of errors and vulnerable carotid plaques were associated with adverse events. CONCLUSION: Tissue acceleration profiles can be a novel metric for the objective assessment of surgical performance and the prediction of adverse events during surgery. Thus, this concept can be introduced into futuristic computer-aided surgeries for both surgical education and patient safety. - 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]
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. - Utility of the deep learning technique for the diagnosis of orbital invasion on CT in patients with a nasal or sinonasal tumor.
Junichi Nakagawa, Noriyuki Fujima, Kenji Hirata, Minghui Tang, Satonori Tsuneta, Jun Suzuki, Taisuke Harada, Yohei Ikebe, Akihiro Homma, Satoshi Kano, Kazuyuki Minowa, Kohsuke Kudo
Cancer imaging : the official publication of the International Cancer Imaging Society, 22, 1, 52, 52, 22 Sep. 2022, [Peer-reviewed], [International Magazine]
English, Scientific journal, BACKGROUND: In nasal or sinonasal tumors, orbital invasion beyond periorbita by the tumor is one of the important criteria in the selection of the surgical procedure. We investigated the usefulness of the convolutional neural network (CNN)-based deep learning technique for the diagnosis of orbital invasion, using computed tomography (CT) images. METHODS: A total of 168 lesions with malignant nasal or sinonasal tumors were divided into a training dataset (n = 119) and a test dataset (n = 49). The final diagnosis (invasion-positive or -negative) was determined by experienced radiologists who carefully reviewed all of the CT images. In a CNN-based deep learning analysis, a slice of the square target region that included the orbital bone wall was extracted and fed into a deep-learning training session to create a diagnostic model using transfer learning with the Visual Geometry Group 16 (VGG16) model. The test dataset was subsequently tested in CNN-based diagnostic models and by two other radiologists who were not specialized in head and neck radiology. At approx. 2 months after the first reading session, two radiologists again reviewed all of the images in the test dataset, referring to the diagnoses provided by the trained CNN-based diagnostic model. RESULTS: The diagnostic accuracy was 0.92 by the CNN-based diagnostic models, whereas the diagnostic accuracies by the two radiologists at the first reading session were 0.49 and 0.45, respectively. In the second reading session by two radiologists (diagnosing with the assistance by the CNN-based diagnostic model), marked elevations of the diagnostic accuracy were observed (0.94 and 1.00, respectively). CONCLUSION: The CNN-based deep learning technique can be a useful support tool in assessing the presence of orbital invasion on CT images, especially for non-specialized radiologists. - Changes in Magnetic Resonance Signal Fluctuation in Superior Sagittal Sinus: Deterioration of Arteriolar Vasomotor Function of Young Smokers
Minghui Tang, Masaya Kubota, Yusuke Nitanda, Toru Yamamoto
Tomography, 8, 2, 657, 666, MDPI AG, 02 Mar. 2022, [Peer-reviewed], [Lead author]
English, Scientific journal, (1) Cerebral arteriolar vasomotor function is vital for brain health and has been examined through CO2 inhalation or breath-holding, which are both challenging for patients. We have developed a non-invasive method to evaluate this function with magnetic resonance imaging (MRI) by utilizing respiration-induced natural changes in partial pressure of arterial CO2 (PaCO2). In this study, we applied this method for 20s to evaluate the chronic effect of a few years smoking on the cerebral arteriolar vasomotor function. (2) A single slice (five slice thicknesses: 15 mm to 7 mm) perpendicular to the superior sagittal sinus of was imaged successively for 45 s using spin-echo echo-planar imaging by 3T MRI for ten smokers (24.5 ± 1.6 years) and ten non-smokers (24.3 ± 1.4 years), respectively. The venous oxygenation fluctuation (ΔYr) caused by the respiration-induced changes of PaCO2, which reflects the arteriolar vasomotor function, was calculated from the time series MR signal changes of superior sagittal sinus. (3) The ΔYr values of the smokers (0.7 ± 0.6) were significantly lower than those of the non-smokers (1.3 ± 0.8) (p = 0.04). (4) Degeneration of the cerebral arteriolar vasomotor function due to chronic smoking (even after 20s) was demonstrated by our non-invasive MRI-based method., 33102257 - Progress in Understanding Radiofrequency Heating and Burn Injuries for Safer MR Imaging
Minghui Tang, Toru Yamamoto
Magnetic Resonance in Medical Sciences, Japanese Society for Magnetic Resonance in Medicine, Feb. 2022, [Peer-reviewed], [Invited], [Lead author]
English, Scientific journal - Translocator protein imaging with 18F-FEDAC-positron emission tomography in rabbit atherosclerosis and its presence in human coronary vulnerable plaques
Kazunari Maekawa, Atsushi B. Tsuji, Atsushi Yamashita, Aya Sugyo, Chietsugu Katoh, Minghui Tang, Kensaku Nishihira, Yoshisato Shibata, Chihiro Koshimoto, Ming-Rong Zhang, Ryuichi Nishii, Keiichiro Yoshinaga, Yujiro Asada
Atherosclerosis, 337, 7, 17, Elsevier BV, Nov. 2021, [Peer-reviewed], [International Magazine]
English, Scientific journal, BACKGROUND AND AIMS: This study aimed to investigate whether N-benzyl-N-methyl-2-[7,8-dihydro-7-(2-[18F]fluoroethyl)-8-oxo-2-phenyl-9H-purin-9-yl]acetamide (18F-FEDAC), a probe for translocator protein (TSPO), can visualize atherosclerotic lesions in rabbits and whether TSPO is localized in human coronary plaques. METHODS: 18F-FEDAC-PET of a rabbit model of atherosclerosis induced by a 0.5% cholesterol diet and balloon injury of the left carotid artery (n = 7) was performed eight weeks after the injury. The autoradiography intensity of 18F-FEDAC in carotid artery tissue sections was measured, and TSPO expression was evaluated immunohistochemically. TSPO expression was examined in human coronary arteries obtained from autopsy cases (n = 16), and in human coronary plaques (n = 12) aspirated from patients with acute myocardial infarction (AMI). RESULTS: 18F-FEDAC-PET visualized the atherosclerotic lesions in rabbits as high-uptake areas, and the standard uptake value was higher in injured arteries (0.574 ± 0.24) than in uninjured arteries (0.277 ± 0.13, p < 0.05) or myocardium (0.189 ± 0.07, p < 0.05). Immunostaining showed more macrophages and more TSPO expression in atherosclerotic lesions than in uninjured arteries. TSPO was localized in macrophages, and arterial autoradiography intensity was positively correlated with macrophage concentration (r = 0.64) and TSPO (r = 0.67). TSPO expression in human coronary arteries was higher in AMI cases than in non-cardiac death, or in the vulnerable plaques than in early or stable lesions, respectively. TSPO was localized in macrophages in all aspirated coronary plaques with thrombi. CONCLUSIONS: 18F-FEDAC-PET can visualize atherosclerotic lesions, and TSPO-expression may be a marker of high-risk coronary plaques. - Electromagnetic simulation of RF burn injuries occurring at skin-skin and skin-bore wall contact points in an MRI scanner with a birdcage coil
Minghui Tang, Kiyoi Okamoto, Takuya Haruyama, Toru Yamamoto
Physica Medica, 82, 219, 227, Elsevier BV, Feb. 2021, [Peer-reviewed], [Lead author]
Scientific journal - Torque property of titanium alloy cerebral aneurysm clips in a magnetic resonance scanner
Minghui Tang, Shingo Kawahira, Naoyuki Nomura, Toru Yamamoto
Journal of Materials Science: Materials in Medicine, 31, 1, Springer Science and Business Media LLC, Jan. 2020, [Peer-reviewed], [Lead author]
Scientific journal - Dependence of Scan Parameters on Nerve Fiber Crossing Depiction in Diffusion Spectrum Imaging in Clinical Practice.
Tang M, Oshinomi K, Ishizaka K, Tha KK, Yamamoto T
Journal of computer assisted tomography, Jan. 2018, [Peer-reviewed], [Lead author] - Pixel-by-Pixel Arterial Spin Labeling Blood Flow Pattern Variation Analysis for Discrimination of Rheumatoid Synovitis: A Pilot Study
Taro Sakashita, Tamotsu Kamishima, Hiroyuki Sugimori, Minghui Tang, Atsushi Noguchi, Michihito Kono, Kenneth Sutherland, Tatsuya Atsumi
MAGNETIC RESONANCE IN MEDICAL SCIENCES, 16, 1, 78, 83, 2017, [Peer-reviewed]
English, Scientific journal - Analysis of Fluctuation in Cerebral Venous Oxygenation Using MR Imaging: Quantitative Evaluation of Vasomotor Function of Arterioles
Minghui Tang, Keigo Nishi, Toru Yamamoto
MAGNETIC RESONANCE IN MEDICAL SCIENCES, 16, 1, 45, 53, 2017, [Peer-reviewed], [Lead author]
English, Scientific journal - Accurate quantitative assessment of synovitis in rheumatoid arthritis using pixel-by-pixel, time-intensity curve shape analysis
Taro Sakashita, Tamotsu Kamishima, Yuto Kobayashi, Hiroyuki Sugimori, Minghui Tang, Kenneth Sutherland, Atsushi Noguchi, Michihito Kono, Tatsuya Atsumi
BRITISH JOURNAL OF RADIOLOGY, 89, 1061, 20151000, 2016, [Peer-reviewed]
English, Scientific journal
Other Activities and Achievements
- 血行再建に残された課題 CEAにおける患者安全と外科教育のための手術映像分析研究 頸動脈剥離における組織加速度評価
杉山 拓, 伊東 雅基, 杉森 博行, 唐 明輝, 中村 俊孝, 小笠原 克彦, 藤村 幹, The Mt. Fuji Workshop on CVD, 41, 76,82, 83, Jul. 2024
The Mt. Fuji Workshop on CVD事務局, Japanese - 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 - 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, 2024 - 労災疾病等医学研究令和5年度開始「じん肺」テーマについて
大塚 義紀, 平田 健司, 唐 明輝, 中川 純一, 宇佐美 郁治, 岸本 卓巳, 水橋 啓一, 飯塚 幹也, 五十嵐 毅, 横山 多佳子, 木村 清延, 日本職業・災害医学会会誌, 71, 臨増, 別96, 別96, Nov. 2023
(一社)日本職業・災害医学会, Japanese - 造影心臓CT画像を用いた深層学習による大動脈弁自動抽出法の検討
猪股 壮一郎, 吉村 高明, 唐 明輝, 市川 翔太, 杉森 博行, 北海道放射線技術雑誌, 95, 95, 46, 46, Nov. 2023
(公社)日本放射線技術学会-北海道支部, Japanese - マンモグラフィにおける石灰化識別のための半教師あり学習の適用と評価
境田 みう, 吉村 高明, 唐 明輝, 市川 翔太, 杉森 博行, 北海道放射線技術雑誌, 95, 95, 48, 48, Nov. 2023
(公社)日本放射線技術学会-北海道支部, Japanese - 労災疾病等医学研究令和5年度開始「じん肺」テーマについて
大塚 義紀, 平田 健司, 唐 明輝, 中川 純一, 宇佐美 郁治, 岸本 卓巳, 水橋 啓一, 飯塚 幹也, 五十嵐 毅, 横山 多佳子, 木村 清延, 日本職業・災害医学会会誌, 71, 臨増, 別96, 別96, Nov. 2023
(一社)日本職業・災害医学会, Japanese - Deep Learningを用いたマンモグラフィ石灰化検出方法の開発
境田 みう, 吉村 高明, 唐 明輝, 杉森 博行, 日本放射線技術学会雑誌, 79, 9, 1027, 1027, Sep. 2023
(公社)日本放射線技術学会, Japanese - 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 - 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 - 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 - 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 (Basel, Switzerland), 23, 94, 56, 57, Apr. 2023, [International Magazine]
(公社)日本放射線技術学会-北海道支部, 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, 核医学, 60, Suppl., S208, S208, 2023
(一社)日本核医学会, Japanese - SRCNNを用いた短時間収集PET画像の画質改善モデルの開発と定量性評価
遠藤 大輝, 吉村 高明, 唐 明輝, 杉森 博行, 長谷川 淳, 小亀 翔揮, 孫田 惠一, 木村 理奈, 渡邊 史郎, 平田 健司, 工藤 與亮, 核医学, 60, Suppl., S209, S209, 2023
(一社)日本核医学会, Japanese - SRCNNを用いた短時間収集PET画像の画質改善モデルの開発と定量性評価
遠藤大輝, 吉村高明, 唐明輝, 杉森博行, 長谷川淳, 小亀翔揮, 孫田惠一, 木村理奈, 渡邊史郎, 平田健司, 工藤與亮, 核医学(Web), 60, Supplement, 2023 - MIP類似アルゴリズムによるFDG-PET体表画像の有用性
平田健司, 木村理奈, 唐明輝, 渡邊史郎, 竹中淳規, 若林直人, 杉森博行, 吉村高明, 工藤與亮, 平田健司, 木村理奈, 唐明輝, 渡邊史郎, 竹中淳規, 若林直人, 杉森博行, 吉村高明, 工藤與亮, 平田健司, 唐明輝, 渡邊史郎, 杉森博行, 吉村高明, 工藤與亮, 平田健司, 杉森博行, 吉村高明, 工藤與亮, 平田健司, 工藤與亮, 核医学(Web), 60, Supplement, 2023 - MIP類似アルゴリズムによるFDG-PET体表画像の有用性
平田 健司, 木村 理奈, 唐 明輝, 渡邊 史郎, 竹中 淳規, 若林 直人, 杉森 博行, 吉村 高明, 工藤 與亮, 核医学, 60, Suppl., S208, S208, 2023
(一社)日本核医学会, Japanese - SRCNNを用いた短時間収集PET画像の画質改善モデルの開発と定量性評価
遠藤 大輝, 吉村 高明, 唐 明輝, 杉森 博行, 長谷川 淳, 小亀 翔揮, 孫田 惠一, 木村 理奈, 渡邊 史郎, 平田 健司, 工藤 與亮, 核医学, 60, Suppl., S209, S209, 2023
(一社)日本核医学会, Japanese - Comparison of evaluation metrics by devising supervised images in cerebral infarction
森谷竜馬, 吉村高明, TANG Minghui, 杉森博行, 北海道放射線技術雑誌(Web), 94, 94, 54, 55, 2023
(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 - 演繹法と帰納法の視点から見た医療AI
平田 健司, 杉森 博行, 唐 明輝, 中谷 純, 小笠原 克彦, 豊永 拓哉, 工藤 與亮, 北海道放射線医学雑誌, 2, 1, 6, Mar. 2022
(NPO)メディカルイメージラボ, Japanese - 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, 93, 27, 27, 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(Web), 93, 93, 27, 27, 2022
(公社)日本放射線技術学会-北海道支部, Japanese - 【MRI室のヒヤリ・ハットをつぶせ!】RF熱傷事故の現状と電磁界解析による知見
唐 明輝, 山本 徹, 臨床画像, 37, 3, 385, 389, Mar. 2021
(株)メジカルビュー社, Japanese - 大脳細動脈収縮拡張による静脈血酸素飽和度揺らぎ
西慶悟, 唐明輝, 山本徹, 酸素ダイナミクス研究会プログラム・抄録集, 19th, 2015 - 二重構造による椎間ケージのMRアーチファクトの低減
押野見一哉, 唐明輝, 西慶悟, 石本卓也, 中野貴由, 山本徹, 日本放射線技術学会雑誌, 70, 9, 964, 964, Sep. 2014
(公社)日本放射線技術学会, Japanese - インプラント装着患者のMRI検査におけるポジショニングの影響―RF発熱の低減―
小林洸貴, 唐明輝, 山口大樹, 伊東学, 山本徹, 日本放射線技術学会雑誌, 70, 9, 1005, 1005, Sep. 2014
(公社)日本放射線技術学会, Japanese - MRI検査時のRF熱傷を予知する方法
菊地侑, 唐明輝, 江刈内英輝, 村上昂史, 山本徹, 日本放射線技術学会雑誌, 70, 9, 1006, 1006, Sep. 2014
(公社)日本放射線技術学会, Japanese - 手関節のリウマチ滑膜炎におけるPixel‐by‐Pixel TIC解析
坂下太郎, 神島保, 杉森博行, 唐明輝, 河野通仁, 渥美達也, 日本放射線技術学会雑誌, 70, 9, 965, 965, Sep. 2014
(公社)日本放射線技術学会, Japanese - 3D撮像によるインプラントの高精度磁場歪みマッピング
金田貴彦, 唐明輝, 西慶悟, 山本徹, 日本放射線技術学会雑誌, 70, 9, 964, 964, Sep. 2014
(公社)日本放射線技術学会, Japanese - 多時相造影MRIのTIC解析を用いた滑膜炎抽出
小野雅人, 神島保, 杉森博行, 唐明輝, 河野通仁, 渥美達也, 北海道医学雑誌, 89, 1, 93, 93, May 2014
北海道医学会, Japanese - 局所注入による再発鼻腔悪性黒色腫の細胞免疫療法
西浦勇一郎, 坂本菊男, 宮嶋義巳, 前田明輝, 高根陽子, 中島格, 唐宇飛, 山名秀明, 日本耳鼻咽喉科学会会報, 108, 9, 2005 - 再発鼻腔悪性黒色腫の免疫療法
坂本菊男, 西浦勇一郎, 宮嶋義巳, 前田明輝, 高根陽子, 中島格, 唐宇飛, 山名秀明, 日本耳鼻咽喉科学会会報, 108, 4, 2005
Books and other publications
Courses
Affiliated academic society
Research Themes
- 術中ヒヤリハットを未然に防止する脳神経外科手術支援AI
科学研究費助成事業
Apr. 2024 - Mar. 2027
伊藤 康裕, 杉山 拓, 杉森 博行, 唐 明輝, 伊東 雅基, 小笠原 克彦
日本学術振興会, 基盤研究(C), 北海道大学, 24K15785 - 神経細胞賦活による細胞内酸素濃度変動を直接検出するfMRI法の開発
科学研究費助成事業
01 Apr. 2023 - 31 Mar. 2026
唐 明輝
日本学術振興会, 若手研究, 北海道大学, 23K14883 - Development of treatment approaches for vulnerable plaque uing targeted radionuclide therapy
Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B)
Apr. 2018 - Mar. 2022
吉永 恵一郎, 加藤 千恵次, 永津 弘太郎, 東 達也, 西井 龍一, 浅田 祐士郎, 山下 篤, 唐 明輝
本研究開発は、虚血性脳血管障害の発症原因の主因となる頚動脈の不安定プラークを近年悪性腫瘍の治療へ応用が始まったα線標識薬剤アスタチン-211により不安定プラークの制御を試み、本邦初のアイソトープ治療に基づく虚血性脳血管障害の発症阻止への展開を目指すものである。当該年度においては、不安定プラークの治療適応例検出や治療候補薬剤の不安定プラークへの集積性を評価する事を目的としてPETイメージング法および病理オートラジオグラフィー法を行い、以下の研究実績を得た。
治療候補標識薬剤として、動脈硬化病変アポトーシス発現部位への選択的薬剤を候補とした。アポトーシス発現細胞に結合するAnnexin-A5 (ANXV5)は画像診断法として有用性の報告が有ったが血中クリアランスが遅く治療用薬剤としては不適格であった。そこで、炎症細胞浸潤を伴った炎症病変に発現するトランスローケータプロテイン(TSPO)に特異的に結合する性質を持ったFEDACをフッ素-18 (18F)により標識し合成を行った。頚動脈の動脈硬化症モデルウサギに対し、動物用CTで動脈硬化病変を形態的に評価し、PET画像の解析の補助とした。ポジトロン断層撮像を用い血管壁プラークへの薬剤集積を検討した。18F-FEDACでは全例で動脈硬化病変の血管壁への薬剤集積度が対照とした対側の未処置病変よりも高値であることが確認された。よって18F-FEDACは集積特異性が高いものと考えられた。また周囲の正常組織への集積はほぼ認められなかった。
本検討により、候補薬剤が動脈硬化病変に特異的に集積する事が示唆され、次年度の方針であるα線標識薬剤を用いたアイソトープ治療に基づく脳血管障害の予防治療へ展開する予定である。
Japan Society for the Promotion of Science, Grant-in-Aid for Scientific Research (B), National Institutes for Quantum and Radiological Science and Technology, Coinvestigator, 18H02773 - 金属系バイオマテリアルの生体機能化−運動骨格系健康長寿の要−
戦略的イノベーション創出推進プログラム【S-イノベ】
Apr. 2012 - Mar. 2022
国立研究開発法人日本医療研究開発機構, 北海道大学, Coinvestigator - 骨塩定量ではわからない骨質の評価：コラーゲンの微細磁化構造に着目したMRIの応用
Grants-in-Aid for Scientific Research Grant-in-Aid for Early-Career Scientists
Apr. 2019 - Mar. 2021
唐 明輝
高齢者の骨折リスクは主に骨塩定量により把握されているが、骨折リスクの直接的指標である骨強度は骨塩定量により求まる骨密度のみでは決まらず骨質にも大きく依存する。骨質は骨軸方向への骨中コラーゲン配向性が主因であるが、それを非侵襲的に検出する方法は存在していない。そこで、本研究はコラーゲン分子の微細な磁化構造が磁気共鳴画像法（MRI）の信号に影響を与えることに着目し、MRIを用いて骨中コラーゲン配向性情報を非侵襲的に検出する方法の確立を目的としている。健常皮質骨からのMR信号は、皮質骨中のコラーゲン線維の磁化率異方性に起因し、静磁場方向と骨軸角度（コラーゲン配向方向）に依存することが予想される。本年度は、その予想の検証を目指し、牛大腿皮質骨（4ヶ月、2歳）試料のMR信号の角度依存性および年齢依存性を調べた。なお、皮質骨からのMR信号は減衰が速いため従来観測できずに着目されなかったが、近年開発されその臨床応用が模索されているUTE（Ultrashort echo time）法を用いて検出した。得られたMR信号は180°周期の骨軸角度依存性を示し、その周期信号の振幅は2歳骨の方が低下し、若年皮質骨中で集約していたコラーゲン線維が成熟し構造化される様子が現れているものと解釈でき、骨質を MRI で評価できる可能性が示唆された。また、遺伝子操作により骨粗鬆症または大理石骨病を発症させたマウス骨試料を対象に同様な測定を実施したが、試料サイズが小さくMRI撮像および解析方法に課題が残った。
Japan Society for the Promotion of Science, Grant-in-Aid for Early-Career Scientists, Hokkaido University, Principal investigator, 19K17158 - 喫煙による大脳細動脈収縮拡張機能劣化の年齢横断的評価―MRIによる新しい評価法の応用―
喫煙科学財団 研究助成
Apr. 2018 - Mar. 2021
喫煙科学財団, 若手研究助成, 北海道大学, Principal investigator - 神経細胞賦活を直接観る４次元的fMRI法の開発
Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (C)
Apr. 2018 - Mar. 2021
山本 徹, 黄田 育宏, 唐 明輝
昨年度実施した細胞内高粘を模擬したグリセロール水溶液（91% (w/w)）MRI信号の酸素濃度依存性実験結果に、グリセロールの水酸基と水分子の水素原子の化学交換状態の影響が判明したため、本年度はその化学交換状態の指標となる水酸基のケミカルシフトの値をもとに化学交換の影響を取り除く高精度な実験および解析を実施した。その結果、酸素分子によるMRI信号の横緩和時間短縮効果がグリセロール水溶液（粘度：75 cP）では、生理食塩水（1 cP）の230±50倍に増強されることが定量化された。また、本年度は当初予定していた「MRI信号に現れる細胞内酸素濃度変化の検証」に取り組み、3T MRI装置にて健常人頭部を対象に、高粘性状態の細胞内からのMRI信号を強調する拡散強調撮像法を用いて時系列連続撮像を行い、頭部MRI信号の安静時揺らぎを調べた。得られた結果は、酸素分子による縦緩和時間短縮に伴う信号増強効果と、横緩和時間短縮効果に伴う信号減弱効果が拮抗し酸素濃度変動によるMRI信号揺らぎが発生しない特異的なエコー時間の存在を示した。また、その特異的エコー時間には、時系列連続撮像繰り返し時間の増加とともに低下するという理論的特性があることも定量的に確認した。これらの結果より拡散強調撮像による頭部MRI信号には高粘性状態の細胞内の安静時酸素濃度揺らぎが反映されることが示唆された。また、画像雑音の影響も定量的に調べ、得られた結果が人頭部細胞内からのMRI信号由来の結果であるとの自信を深めた。
Japan Society for the Promotion of Science, Grant-in-Aid for Scientific Research (C), Hokkaido University, Coinvestigator, 18K07701
