| Thymic Hyperplasia and Thymus Gland Tumors: Differentiation with Chemical Shift MR Imaging1 T Inaoka, K Takahashi, M Mineta, T Yamada, N Shuke, A Okizaki, ... Radiology 243 (3), 869-876, 2007 | 236 | 2007 |
| Identification and further differentiation of subendocardial and transmural myocardial infarction by fast strain-encoded (SENC) magnetic resonance imaging at 3.0 Tesla N Oyama-Manabe, N Ishimori, H Sugimori, M Van Cauteren, K Kudo, ... European Radiology 21, 2362-2368, 2011 | 58 | 2011 |
| Simple prediction of right ventricular ejection fraction using tricuspid annular plane systolic excursion in pulmonary hypertension T Sato, I Tsujino, N Oyama-Manabe, H Ohira, YM Ito, H Sugimori, ... The international journal of cardiovascular imaging 29, 1799-1805, 2013 | 51 | 2013 |
| Classification of computed tomography images in different slice positions using deep learning H Sugimori Journal of healthcare engineering 2018, 2018 | 49 | 2018 |
| VIBE MRI for evaluating the normal and abnormal gastrointestinal tract in fetuses T Inaoka, H Sugimori, Y Sasaki, K Takahashi, K Sengoku, N Takada, ... American Journal of Roentgenology 189 (6), W303-W308, 2007 | 48 | 2007 |
| VIBE MRI for evaluating the normal and abnormal gastrointestinal tract in fetuses T Inaoka, H Sugimori, Y Sasaki, K Takahashi, K Sengoku, N Takada, ... American Journal of Roentgenology 189 (6), W303-W308, 2007 | 47 | 2007 |
| Quantification of myocardial blood flow with dynamic perfusion 3.0 Tesla MRI: Validation with 15o‐water PET Y Tomiyama, O Manabe, N Oyama‐Manabe, M Naya, H Sugimori, ... Journal of Magnetic Resonance Imaging 42 (3), 754-762, 2015 | 38 | 2015 |
| Comparison of 1H MR spectroscopy, 3-point DIXON, and multi-echo gradient echo for measuring hepatic fat fraction K Ishizaka, N Oyama, S Mito, H Sugimori, M Nakanishi, T Okuaki, ... Magnetic Resonance in Medical Sciences 10 (1), 41-48, 2011 | 28 | 2011 |
| Development of a deep learning-based algorithm to detect the distal end of a surgical instrument H Sugimori, T Sugiyama, N Nakayama, A Yamashita, K Ogasawara Applied Sciences 10 (12), 4245, 2020 | 24 | 2020 |
| A deep-learning method using computed tomography scout images for estimating patient body weight S Ichikawa, M Hamada, H Sugimori Scientific reports 11 (1), 15627, 2021 | 23 | 2021 |
| Automatic detection of a standard line for brain magnetic resonance imaging using deep learning H Sugimori, M Kawakami Applied Sciences 9 (18), 3849, 2019 | 23 | 2019 |
| Visualization of normal pulmonary fissures on sagittal multiplanar reconstruction MDCT K Takahashi, B Thompson, W Stanford, Y Sato, K Nagasawa, H Sato, ... American Journal of Roentgenology 187 (2), 389-397, 2006 | 19 | 2006 |
| Acceleration of ASL‐based time‐resolved MR angiography by acquisition of control and labeled images in the same shot (ACTRESS) Y Suzuki, N Fujima, T Ogino, JA Meakin, A Suwa, H Sugimori, ... Magnetic Resonance in Medicine 79 (1), 224-233, 2018 | 18 | 2018 |
| Classification of type of brain magnetic resonance images with deep learning technique H Sugimori, H Hamaguchi, T Fujiwara, K Ishizaka Magnetic Resonance Imaging 77, 180-185, 2021 | 16 | 2021 |
| Three-dimensional magnetic resonance imaging after ultrasonography for assessment of fetal gastroschisis Y Sasaki, T Miyamoto, Y Hidaka, H Satoh, N Takuma, K Sengoku, ... Magnetic Resonance Imaging 24 (2), 201-203, 2006 | 16 | 2006 |
| Evaluation of cerebral blood flow using multi-phase pseudo continuous arterial spin labeling at 3-tesla H Sugimori, N Fujima, Y Suzuki, H Hamaguchi, M Sakata, K Kudo Magnetic Resonance Imaging 33 (10), 1338-1344, 2015 | 14 | 2015 |
| Artificial intelligence for nuclear medicine in oncology K Hirata, H Sugimori, N Fujima, T Toyonaga, K Kudo Annals of Nuclear Medicine, 1-10, 2022 | 13 | 2022 |
| FDG PET/CT diagnostic criteria may need adjustment based on MRI to estimate the presurgical risk of extrapelvic infiltration in patients with uterine endometrial cancer S Sudo, N Hattori, O Manabe, F Kato, R Mimura, K Magota, H Sugimori, ... European Journal of Nuclear Medicine and Molecular Imaging 42, 676-684, 2015 | 13 | 2015 |
| Improvement in the convolutional neural network for computed tomography images K Manabe, Y Asami, T Yamada, H Sugimori Applied Sciences 11 (4), 1505, 2021 | 12 | 2021 |
| Evaluating the overall accuracy of additional learning and automatic classification system for CT images H Sugimori Applied sciences 9 (4), 682, 2019 | 12 | 2019 |
