基于自监督边缘融合网络的MRI影像重建

Self-supervised Edge-Fusion Network for MRI Reconstruction

医学影像的统计研究表明,组织的边缘信息是医学影像重建最难恢复的一个部分,但现有基于深度学习的重建方法均缺乏对边缘信息的显式考虑.为了在重建时考虑影像的边缘信息,文中提出自监督边缘融合网络,完成MRI影像的压缩感知重建.首先使用边缘检测算子,以无需人工标注的方式生成影像的边缘标记.再提出自监督的辅助网络,将边缘标记以特征学习的方式转换成可融合的特征.设计自顶向下的特征融合机制,将自监督网络学习的特征融入重建网络,实现对影像的压缩感知重建.实验表明,文中网络可较好地捕获影像的边缘信息,重建效果较优.

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.