Dynamic HUMUS-Net for Fast MRI Reconstruction

Jia-Yao He, Yanlin Chen, Lei Chen, Xuhua Yang, Xiao-Xin Li

Abstract: To accelerate magnetic resonance imaging (MRI), image reconstruction from under-sampled measurements has been widely used. Recently, the convolutional-Transformer hybrid architecture has dominated the field of MRI reconstruction. To improve calculation performance, two multi-scale (MS) strategies are usually adopted: the one imposed in the intra-cascades in a U-shape style and the one lying in the inter-cascades in a pyramid manner. The two MS strategies have their own benefits but have not been combined together for boosting performance. In this work, we proposed a dynamic Hybrid Unrolled Multi-Scale Network (dHUMUS-Net) by incorporating the two MS strategies together. A novel Optimal Scale Prediction Network is presented to dynamically estimate the optimal scales for all cascades of dHUMUS-Net. Experiments on the fastMRI dataset demonstrate the effectiveness of our method over the state-of-the-art methods.