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Abstract #4714

SSTN: Self-supervised Triple-Network with Multi-scale Dilated-Convolution for Fast MRI Reconstruction

Yuekai Sun1, Jun Lv1, Weibo Chen2, He Wang3,4, and Chengyan Wang4
1School of Computer and Control Engineering, Yantai University, Yantai, China, 2Philips Healthcare, Shanghai, China, 3Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China, 4Human Phenome Institute, Fudan University, Shanghai, China


To solve the problem that fully-sampled reference data is difficult to acquire, we proposed a self-supervised triple-network (SSTN) for fast MRI reconstruction. Each pipeline of SSTN is composed of multiple parallel ISTA-Net blocks which consists of different scales dilated convolution layers. The results demonstrated that the proposed SSTN can generate better quality reconstructions than competing methods at high acceleration rates.

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