Abstract #4668

Deep MRI Reconstruction without Ground Truth for Training

Peizhou Huang¹, Chaoyi Zhang², Hongyu Li², Sunil Kumar Gaire², Ruiying Liu², Xiaoliang Zhang¹, Xiaojuan Li³, and Leslie Ying^1,2

¹Biomedical Engineering, State University of New York at Buffalo, Buffalo, NY, United States, ²Electrical Engineering, State University of New York at Buffalo, Buffalo, NY, United States, ³Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH, United States

Deep learning has recently been applied to image reconstruction from undersampled k-space data with success. Most existing works require both undersampled data and ground truth image as the training pair. It is not practical to obtain a large number of ground truth images for training in some MR applications. Here a novel deep learning network is studied for image reconstruction using only undersampled data for training. Experiment results demonstrate the feasibility of training without the ground truth images for image reconstruction.

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