Abstract #2654

Deep learning fat-suppressed images for musculoskeletal diseases

Shimpei Kato^1,2, Akihiko Wada¹, Yuya Saito^1,3, Christina Andica¹, Shohei Fujita^1,2, Kotaro Fujimoto^1,2, Yutaka Ikenouchi¹, Akifumi Hagiwara¹, Junko Kikuta⁴, Kanako Sato^1,2, Michimasa Suzuki¹, Toshiaki Akashi¹, Maki Amano¹, Koji Kamagata¹, Kanako Kumamaru^1,2, Masaaki Hori^1,5, 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.

This abstract and the presentation materials are available to members only; a login is required.

Join Here