Oral Session

Oral Session
Deep Learning Techniques 3

Sat. Sep 21, 2024 1:40 PM - 2:40 PM Room 5 (Makuhari Messe International Conference Hall, Meeting Room 302)

Chairs: Yasuhiko Terada(Institute of Pure and Applied Physics, University of Tsukuba), Ota Junko(National Institutes for Quantum Science and Technology)

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[OS2-48] Proposal for New MR Simultaneous Multi-slice Imaging Using Data-driven Science

*Satoshi Ito¹, Naoya Endo¹, Yuki Sato¹, Shohei Ouchi² (1. Utsunomiya University, Graduate Program in Information, Electrical and Electronic Systems Engineering, 2. National Institute of Technology, Oyama College)

[OS2-49] By using deep learning reconstruction, both high speed and high resolution can be achieved even at 1.5T : Consideration of brain 2D FLAIR

*Takanori Yoshida¹, Takaaki Yamazaki², Shouichi Iwamoto¹, Masaaki Ichinoseki¹ (1. Department of Radiology, Hakodate Neurosurgical Hospital)

[OS2-50] Usefulness of super resolution deep learning reconstruction for reducing acquisition time of high resolution proton density weighted image with STIR

*Asako Masuda¹, Yasunori Iwai¹ (1. Department of Radiology,National Hospital Organization Nara Medical Center)

[OS2-51] Image quality alteration by Deep Learning reconstruction in Single Shot TSE

*Toshi Matsushita¹, Daichi Kobayashi¹, Shunsuke Fujii¹, Akira Kurozumi¹, Naoki Nishida¹, Mitsugi Honda¹ (1. Division of Radiological Technology, Okayama University Hospital)

[OS2-52] Consideration of CNR and Resolution of 3D T2 CUBE Combined with Deep Learning Reconstruction Aimed at Reducing Examination Time

*Hiroaki Minami¹, Yuuta Isono¹, Tatsuhito Tsukui¹, Yoshito Nakajima¹, Hiroaki Hagiwara¹ (1. Department of Radiology, Yokohama Minami Kyousai Hospital)

[OS2-53] Development of CNN-based truncation artifact reduction technology for 3D image

*Toru Shirai¹, Takenori Murase², Katsunari Nagashima², Yuya Hirakawa², Masahiro Takizawa², Yoshihisa Soutome¹ (1. Medical Systems Research & Development Center, FUJIFILM Corporation.)