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) Fujita S, Hagiwara A, Hori M, Warntjes M, Kamagata K, Fukunaga I, Goto M, Takuya H, Takasu K, Andica C, Maekawa T, Takemura M, Irie R, Wada A, Suzuki M, Aoki S: 3D quantitative synthetic MRI-derived cortical thickness and subcortical brain volumes: Scan-rescan repeatability and comparison with conventional T1-weighted images. J Magn Reson Imaging, 2019. doi: 10.1002/jmri. 26744. [Epub ahead of print]) Kumamaru K, Kumamaru H, Yasunaga H, Matsui H, Omiya T, Hori M, Suzuki M, Wada A, Kamagata K, Takamura T, Irie R, Nakan‑ishi A, Aoki S: Large hospital variation in the utilization of Post-procedural CT to detect pulmonary embolism/Deep Vein Thrombosis in Patients Undergoing Total Knee or Hip Replacement Surgery: Japanese Nationwide Diagnosis Procedure Combination Database Study. Br J Radiol, 2019; 92: 20180825. doi:10. 1259/bjr.20180825) Hagiwara A, Otsuka Y, Hori M, Tachibana Y, Yokoyama K, Fujita S, Andica C, Kamagata K, Irie R, Koshino S, Maekawa T, Chougar L, Wada A, Takemura M, Hattori N, Aoki S: Improving the Quality of Synthetic FLAIR Images with Deep Learning Using a Condi‑tional Generative Adversarial Network for Pixel-by-Pixel Image Translation. AJNR Am J Neuroradiol, 2019; 40: 224-230. doi:10.3174/ajnr.A5927) Takamura K, Fujimoto S, Kawaguchi Y, Kato E, Aoshima C, Hiki M, Kumamaru K, Daida H: The usefulness of low radiation dose subtrac‑tion coronary computed tomography angiog‑raphy for patients with calcification using 320- An asterisk (＊) denotes doctoral works by Japanese students.A dagger (†) denotes doctoral works by non-Japanese students.Juntendo Medical Journal2022. 68(2), 163-183Publication ListPublications from Juntendo University Graduate School of Medicine, 2019 [6/6]Diagnostic Radiology〈Original Articles〉 1 2 3 4This is a reprint of content originally published in Juntendo University HP.row area detector CT. J Cardiol, 2019; 73: 58-64.) Nishioka K, Suzuki M, Nakajima M, Hara T, Iseki M, Hattori N: Painful legs and moving toes syndrome evaluated through brain single photon emission computed tomography: a case series. J Neurol, 2019; 266: 717-725.) Gros C, De Leener B, Badji A, Maranzano J, Eden D, Dupont SM, Talbott J5, Zhuoquiong R, Liu Y, Granberg T, Ouellette R, Tachibana Y, Hori M, Kamiya K, Chougar L, Stawiarz L, Hillert J, Bannier E, Kerbrat A, Edan G, Labauge P, Callot V, Pelletier J, Audoin B, Rasoanandrianina H, Brisset JC, Valsasina P, Rocca MA, Filippi M, Bakshi R, Tauhid S, Prados F, Yiannakas M, Kearney H, Cicca‑relli O, Smith S, Treaba CA, Mainero C, Lefeuvre J, Reich DS, Nair G, Auclair V, McLaren DG, Martin AR, Fehlings MG, Vahdat S, Khatibi A, Doyon J, Shepherd T, Charlson E, Narayanan S, Cohen-Adad J: Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks. Neuro‑image, 2019; 184: 901-915.) Fujita S, Nakazawa M, Hagiwara A, Ueda R, Horita M, Maekawa T, Irie R, Andica C, Kumamaru K, Hori M, Aoki S: Estimation of Gadolinium-Based Contrast Agent Concen‑tration Using Quantitative Synthetic MRI and Its Application to Brain Metastases: A Feasibility Study. Magn Reson Med Sci, 2019. [Epub ahead of print]) Alice Le Berre, Kamagata K, Otsuka Y, Andica C, Hatano T, Laetitia Saccenti, Ogawa T, Takeshige-Amano H, Wada A, Suzuki M, Hagiwara A, Irie R, Hori M, Oyama G, Shimo Y, Umemura A, Hattori N, Aoki S: Convolu‑tional neural network-based segmentation can help in assessing the substantia nigra in 5 6 7 8163