A lightweight self-ensemble feedback recurrent network for fast MRI reconstruction

Abstract

Improving the speed of MRI acquisition is a key issue in modern medical practice. However, existing deep learning-based methods are often accompanied by a large number of parameters and ignore the use of deep features. In this work, we propose a novel Self-Ensemble Feedback Recurrent Network (SEFRN) for fast MRI reconstruction inspired by recursive learning and ensemble learning strategies. Specifically, a lightweight but powerful Data Consistency Residual Group (DCRG) is proposed for feature extraction and data stabilization. Meanwhile, an efficient Wide Activation Module (WAM) is introduced between different DCRGs to encourage more activated features to pass through the model. In addition, a Feedback Enhancement Recurrent Architecture (FERA) is designed to reuse the model parameters and deep features. Moreover, combined with the specially designed Automatic Selection and Integration Module (ASIM), different stages of the recurrent model can elegantly implement self-ensemble learning and synergize the sub-networks to improve the overall performance. Extensive experiments demonstrate that our model achieves competitive results and strikes a good balance between the size, complexity, and performance of the model.