Poster Session

Poster Session
AI2

Sat. Sep 23, 2023 10:00 AM - 11:00 AM Poster 1 (Karuizawa Prince Hotel West, Asama International Conference Hall A-E)

Chairs: Shigeru Kiryu（Department of Radiology, International University of Health and Welfare, School of Medicine）, Soichiro Miki（Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital）

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[PS11-1] Diagnosis of Pituitary Adenomas using Dynamic MRI: A New Segmentation Method based on Time Signal Changes

Bahadeer Aibibuli¹, Akihiko Wada², Mitsuo Nishizawa^1,2, Keigo Shimoji^1,2, Koji Kamagata², Toshiaki Akashi², Akifumi Hagiwara², Shigeki Aoki^1,2 (1.Faculty of Health Data Science, Juntendo University, 2.Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo)

[PS11-2] Deep Learning Reconstruction in MRI

Shigeru Kiryu¹, Hiroyuki Akai², Koichiro Yasaka³, Taku Tajima⁴, Akira Kunimatsu⁴, Naoki Yoshioka¹, Masaaki Akahane¹, Osamu Abe³, Kuni Ohtomo⁵ (1.Department of Radiology, International University of Health and Welfare Narita Hospital, 2.Department of Radiology, The Institute of Medical Science, The University of Tokyo, 3.Department of Radiology, Graduate School of Medicine, The University of Tokyo, 4.Department of Radiology, International University of Health and Welfare Mita Hospital, 5.International University of Health and Welfare)

[PS11-3] Comparing the robustness of a physics-guided MRI reconstruction neural network under self-supervised and fully-supervised learning paradigms

Ziyu Fu, Naoto Fujita, Yasuhiko Terada (Institute of Pure and Applied Sciences, University of Tsukuba)

[PS11-4] Optimal segmentation model for 3D medical images

Kazuki Tsuji¹, Keiho Imanishi², Mami Iima^3,4, Ryosuke Mizuno⁵, Masako Kataoka³, Aika Okazawa³, Yuji Nakamoto³ (1.Kyoto University Faculty of Medicine, 2.e-Growth Co., Ltd., 3.Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 4.Institute for Advancement of Clinical and Translational Science (iACT), Kyoto University Hospital, 5.A.I. System Research CO.,Ltd)

[PS11-5] Reconstruction of High-resolution T2-weighted Images of Human and Monkey Brains on a 7T System Using Deep-Learning algorithm

Chisato Suzuki¹, Kenichi Ueno¹, Allen R. Waggoner¹, Kentaro Miyamoto¹, Hiroyuki Kamiguchi¹, Arihito Nauchi², Atsushi Nozaki², Tetsuya Wakayama² (1.RIKEN Center for Brain Science, 2.GE HealthCare)

[PS11-6] Convolutional neural network based deep learning noise reduction algorithm for radial sampling on Brain imaging

Chikako Moriwake, Keisuke Nishio, Kosuke Ito, Masahiro Takizawa (MR Development Department MS R&D Division Medical System Research & Development Center)

[PS11-7] Deep learning reconstruction enables high-resolution 3D imaging at 7.0T with improved image quality and consistent contrast and volumetry

Arihito Nauchi¹, Tetsuya Wakayama¹, Chisato Suzuki², Kenichi Ueno², Allen R. Waggoner², Hiroyuki Kamiguchi² (1.GE HealthCare, 2.RIKEN Center for Brain Science)