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Abstract #0819

Erasing artifacts from arterial phase MRI: Motion Artifact Reduction using a Convolutional network (MARC)

Shinya Kojima1,2, Daiki Tamada 2, Tetsuya Wakayama 3, Shintaro Ichikawa 2, Hiroyuki Morisaka 4, Shigeru Suzuki 1, and Utaroh Motosugi 2
1Department of Radiology, Tokyo Women’s Medical University Medical Center East, Arakawa, Japan, 2Department of Radiology, University of Yamanashi, Yamanashi, Japan, 3MR Collaboration and Development, GE Healthcare, Hino, Japan, 4Department of Radiology, Saitama Medical University International Medical Center, Saitama, Japan

Motion artifact by irregular respiration disturbs accurate diagnosis in dynamic contrast-enhanced MRI of the liver. We developed a motion artifact reduction algorithm using a convolutional network (MARC). The training was performed using U-net with the arterial phase images with and without simulated artifacts. For verifying the ability of MARC algorithm, contrast-to-noise ratio measurement and visual assessment were performed in 120 cases. The image quality of arterial phase images with motion artifacts were significantly improved after applying MARC algorithm, while no particular difference was observed in the images without motion artifacts. MARC provides motion artifacts reduction without variation of image contrast.

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