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Abstract #1004

FITs-CNN: A Very Deep Cascaded Convolutional Neural Networks Using Folded Image Training Strategy for Abdominal MRI Reconstruction

Satoshi Funayama1, Tetsuya Wakayama2, Hiroshi Onishi1, and Utaroh Motosugi1
1Department of Radiology, University of Yamanashi, Yamanashi, Japan, 2GE Healthcare Japan, Tokyo, Japan

For faster abdominal MR imaging, deep learning-based reconstruction is expected to be a powerful reconstruction method. One of the challenges in deep learning-based reconstruction is its memory consumption when it is combined with parallel imaging. To handle the problem, we propose a very deep cascaded convolutional neural networks (CNNs) using folded image training strategy (FITs). We also present that the network can be trained with FITs and shows good quality of reconstructed images.

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