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A lightweight and efficient convolutional neural network for MR image restoration

Aowen Liu¹, Meiling Ji², Xiaoqian Huang¹, Yawei Zhao², Renkuan Zhai², Guobin Li², Dinggang Shen¹, and Shu Liao¹
¹United Imaging Intelligence, Shanghai, China, ²United Imaging Healthcare, Shanghai, China

An efficient and lightweight neural network EEDN with a novel loss function for MRI restoration is proposed, which can not only greatly improve network efficiency with reduced network parameters and FLOPs, but also help enhance image quality with better visualization.

Fig. 1. The architecture of EEDN. FLB represents feature learning sub-module, which is implemented with EAM [5]. Up and Down are the up and down sampling operation, respectively. For FLB modules, multiple inputs of each FLB module are first concatenated and then reduced for channels by 1x1 conv.

Fig. 2. Comparison of different models using five different metrics.