Self-supervised Edge-Fusion Network for MRI Reconstruction
LI Zhongnian1, ZHANG Tao1, ZHANG Daoqiang1
1. MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211100
Abstract The research on compressed sensing magnetic resonance imaging(CS-MRI) suggests that the edge information is the hardest part of medical image reconstruction. In most deep-learning based methods, the explicit consideration for edge information is not taken into account. To tackle this problem, a self-supervised edge-fusion network(SEN) is proposed to explore beneficial edge properties to reconstruct MRI. Firstly, edge annotations are generated by utilizing canny edge detector without involving any time-consuming and expensive human labeling. Secondly, a self-supervised auxiliary network is introduced to incorporate edge annotations into a feature learning to capture fusible representations. A top-down fusion strategy is proposed to fuse the learned representations into reconstruction network for CS-MRI restoring. Experimental results show that SEN catches the edge information effectively and achieves better performance in CS-MRI reconstruction.
Fund:National Key Research and Development Program of China(No.2018YFC2001600,2018YFC2001602,2018ZX10201002), National Natural Science Foundation of China(No.61876082,61732006,61861130366)
Corresponding Authors:
ZHANG Daoqiang, Ph.D., professor. His research interests include pattern recognition.
About author:: LI Zhongnian, Ph.D. candidate. His research interests include machine learning and medical image reconstruction. ZHANG Tao, master student. His research interests include machine learning and image super-resolution.
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2021, Vol. 34 
