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Abstract #0812

Ultrafast motion-minimized shoulder MRI with a deep learning constrained Compressed SENSE reconstruction

Jihun Kwon1, Masami Yoneyama1, Takashige Yoshida2, Kohei Yuda2, Yuki Furukawa2, Johannes M. Peeters3, and Marc Van Cauteren3
1Philips Japan, Tokyo, Japan, 2Tokyo Metropolitan Police Hospital, Nakano, Japan, 3Philips Healthcare, Best, Netherlands

Shoulder MRI is typically acquired with multiple number of signals averaged (NSA) in order to average out breathing motion artifacts. However, higher NSA leads to a longer scan time and patient discomfort. In this study, we investigated the use of a deep learning-based reconstruction algorithm to highly accelerate shoulder MRI. Adaptive-CS-Net, a deep neural network previously introduced at the 2019 fastMRI challenge, was expanded and presented here as a Compressed-SENSE Artificial Intelligence (CS-AI) reconstruction. The purpose of this study was to compare the image quality of shoulder MRI between reference and accelerated methods; SENSE, Compressed-SENSE, and CS-AI.

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