[P-139-J] FASE vs. EPI: Image Quality and Diagnostic Performance on DWI with Deep Learning Reconstruction in Suspected Head and Neck Tumor

^○Hirotaka Ikeda¹, Yoshiharu Ohno¹, Kaori Yamamoto², Kazuhiro Murayama³, Masato Ikedo², Masao Yui², Satomu Hanamatsu¹, Akiyoshi Iwase⁴, Takashi Fukuba⁴, Yuki Obama¹, Takahiro Ueda¹, Shigeki Kobayashi⁵, Hiroshi Toyama¹ (1.Department of Radiology, Fujita Health University School of Medicine, 2.Canon Medical Systems Corporation, 3.Joint Laboratory Research of Advanced Medical Imaging, Fujita Health University School of Medicine, 4.Department of Radiology, Fujita Health University Hospital, 5.Faculty of Radiological Technology, Fujita Health University School of Medicine)

[P-140-J] Verification of image quality improvement effect by applying Deep Learning Reconstruction to 1.5T MRI in hippocampus T2-weighted image

^○Yoshiomi Sato¹, Isao Fujita¹, Takashi Ishida¹, Katsuki Enomoto¹, Kiyoshi Ookuma², Hiromi Watanabe², Sayuri Kodama², you Ogawa², Yuuta Asami² (1.Department of Radiology,Saitama City Hospital, 2.Department of Diagnostic Radiology,Saitama City Hospital)

[P-141-J] Pyramidal tract visualization ability with automatic brain white matter extraction software in patients with brain arteriovenous malformations

^○Yuichi Suzuki¹, Yuki Shinya², Tsuyoshi Ueyama¹, Kentaro Sakata¹, Takahiro Iwasaki¹, Nobuhito Siato², Osamu Abe¹ (1.Department of Radiological Technology, The University of Tokyo Hospital, 2.Department of Neurosurgery, The University of Tokyo Hospital)

[P-142-J] Improving the Quality of Ultrashort TE 4D MR Angiography by Machine Learning

^○Akihiko Wada, Yutaka Ikenouchi, Toshiya Akatsu, Toshiaki Akashi, Katsuhiro Sano, Shohei Fujita, Kanako Sato, Koji Kamagata, Junko Kikuta, Nobuo Tomizawa, Yayoi Hayakawa, Atsushi Nakanishi, Shigeki Aoki (Department of Radiology, Juntendo University)

[P-143-J] Improved performance of deep-learning-based super-resolution of clinical brain images improved by decreasing reduction factor

^○Erika Takahashi¹, Tomoki Miyasaka¹, Satoshi Funayama², Daiki Tamada², Utaroh Motosugi³, Hiroyuki Morisaka², Hiroshi Onishi², Yasuhiko Terada¹ (1.Institute of Applied Physics, University of Tsukuba, 2.Department of Radiology, University of Yamanashi, 3.Department of Radiology, Kofu-Kyoritsu Hospital)

[P-144-J] Accelerated T1 weighted PROPELLER of the brain with model based deep learning

^○Motohide Kawamura¹, Daiki Tamada¹, Kazuyuki Sato², Masahiro Hamasaki², Satoshi Funayama¹, Tetsuya Wakayama³, Utaroh Motosugi⁴, Hiroyuki Morisaka¹, Hiroshi Onishi¹ (1.Department of Radiology, University of Yamanashi, 2.Division of Radiology, University of Yamanashi Hospital, 3.MR Collaboration and Development, GE Healthcare, 4.Department of Radiology, Kofu-Kyoritsu Hospital)

[P-145-J] Local pattern analysis in DWI and dMRI parameter maps for synthetic X-Q space learning

^○Yoshitaka Masutani (Graduate School of Information Sciences, Hiroshima City University)

Session information

Poster
Brain: AI/ML/DS

[P-139-J] FASE vs. EPI: Image Quality and Diagnostic Performance on DWI with Deep Learning Reconstruction in Suspected Head and Neck Tumor

[P-140-J] Verification of image quality improvement effect by applying Deep Learning Reconstruction to 1.5T MRI in hippocampus T2-weighted image

[P-141-J] Pyramidal tract visualization ability with automatic brain white matter extraction software in patients with brain arteriovenous malformations

[P-142-J] Improving the Quality of Ultrashort TE 4D MR Angiography by Machine Learning

[P-143-J] Improved performance of deep-learning-based super-resolution of clinical brain images improved by decreasing reduction factor

[P-144-J] Accelerated T1 weighted PROPELLER of the brain with model based deep learning

[P-145-J] Local pattern analysis in DWI and dMRI parameter maps for synthetic X-Q space learning

Session information

PosterBrain: AI/ML/DS

[P-139-J] FASE vs. EPI: Image Quality and Diagnostic Performance on DWI with Deep Learning Reconstruction in Suspected Head and Neck Tumor

[P-140-J] Verification of image quality improvement effect by applying Deep Learning Reconstruction to 1.5T MRI in hippocampus T2-weighted image

[P-141-J] Pyramidal tract visualization ability with automatic brain white matter extraction software in patients with brain arteriovenous malformations

[P-142-J] Improving the Quality of Ultrashort TE 4D MR Angiography by Machine Learning

[P-143-J] Improved performance of deep-learning-based super-resolution of clinical brain images improved by decreasing reduction factor

[P-144-J] Accelerated T1 weighted PROPELLER of the brain with model based deep learning

[P-145-J] Local pattern analysis in DWI and dMRI parameter maps for synthetic X-Q space learning

Poster
Brain: AI/ML/DS