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Abstract #2654

Deep learning fat-suppressed images for musculoskeletal diseases

Shimpei Kato1,2, Akihiko Wada1, Yuya Saito1,3, Christina Andica1, Shohei Fujita1,2, Kotaro Fujimoto1,2, Yutaka Ikenouchi1, Akifumi Hagiwara1, Junko Kikuta4, Kanako Sato1,2, Michimasa Suzuki1, Toshiaki Akashi1, Maki Amano1, Koji Kamagata1, Kanako Kumamaru1,2, Masaaki Hori1,5, Atsushi Nakanishi1, Osamu Abe2, and Shigeki Aoki1
1Department of Radiology, Juntendo University, Tokyo, Japan, 2Department of Radiology, The University of Tokyo, Tokyo, Japan, 3Faculty of Health Sciences Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan, 4Department of Radiology, Juntendo University Nerima Hospital, Tokyo, Japan, 5Department of Radiology, Toho University, Tokyo, Japan

Fat-suppressed MR images of the musculoskeletal system help the visualization of T2-prolonged lesions, such as tumors, infections/inflammations, and trauma, with better contrast, while also contributing to the qualitative diagnosis of fatty lesions.However, the addition of a fat-suppressing sequence to clinical routine is time-consuming. Increasing the imaging time may lead to deterioration of the image quality due to body movement.In this study, we generated fat-suppressed images through post-processing by using deep learning. The images were generated using U-Net, with T1WIs and T2WIs as input. The generated images were very similar to Dixon images that were used as targets.

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