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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