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Abstract #2024

Application of Deep Learning Reconstruction to Compressed-sensing Thin-slice Fat-suppressed T2-weighted Imaging of the Orbit

Satoshi Nakajima1, Yasutaka Fushimi1, Yusuke Yokota1, Sonoko Oshima1, Sayo Otani1, Azusa Sakurama1, Krishna Pandu Wicaksono1, Yuichiro Sano2, Ryo Matsuda2, Masahito Nambu2, Koji Fujimoto3, Hitomi Numamoto4, Kanae Kawai Miyake4, Tsuneo Saga4, and Kaori Togashi1
1Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan, 2MRI Systems Division, Canon Medical Systems Corporation, Otawara, Japan, 3Human Brain Research Center, Kyoto University Graduate School of Medicine, Kyoto, Japan, 4Department of Advanced Medical Imaging Research, Kyoto University Graduate School of Medicine, Kyoto, Japan

Deep learning reconstruction (DLR) is a novel denoising processing. We applied DLR to a compressed sensing (CS) sequence of orbital thin-slice fat-suppressed T2-weighted imaging with one number of excitation (NEX). A CS sequence with one NEX without DLR and a conventional sequence with two NEX were also obtained to evaluate the denoising performance. Combined usage of DLR with CS reduced image noise and improved the image quality of the optic nerves and the medial rectus muscles, while achieving shorter acquisition time, compared with the CS and the conventional sequences without DLR.

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