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| 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 |
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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.
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Received: 01 June 2020
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| Fund:National Key Research and Development Program of China(No.2018YFC2001600,2018YFC2001602,2018ZX10201002), National Natural Science Foundation of China(No.61876082,61732006,61861130366) |
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Corresponding Authors:
ZHANG Daoqiang, Ph.D., professor. His research interests include pattern recognition.
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| 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 
