Abstract #2992

Model-based Deep Learning Reconstruction using Folded Image Training Strategy (FITS-MoDL) for Liver MRI Reconstruction

Satoshi Funayama^1,2, Utaroh Motosugi³, and Hiroshi Onishi¹

¹Department of Radiology, University of Yamanashi, Yamanashi, Japan, ²Graduate School of Medicine, University of Yamanashi, Yamanashi, Japan, ³Department of Radiology, Kofu-Kyoritsu Hospital, Yamanashi, Japan

Short acquisition time is one of the key features of liver MRI to acquire images during breath-holding. A combination of undersampling and deep learning-based reconstruction would be a powerful reconstruction method to achieve sufficient speed and SNR. However, it is challenging due to high memory consumption in network training. The folded image training strategy (FITS) is one of the methods to handle this problem. In this study, we demonstrated that the model-based deep learning reconstruction using FITS had better image quality in liver MRI acquired with multiple coils.

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