Dynamic Hybrid Unrolled Multi-scale Network for Accelerated MRI Reconstruction

Xiao-Xin Li, Fang-Zheng Zhu, Junwei Yang, Yong Chen, Dinggang Shen

Abstract: In accelerated magnetic resonance imaging (MRI) reconstruction, the anatomy of a patient is recovered from a set of under-sampled measurements. Currently, unrolled hybrid architectures, incorporating both the beneficial bias of convolutions with the power of Transformers have been proven to be successful in solving this ill-posed inverse problem. The multi-scale strategy of the intra-cascades and that of the inter-cascades are used to decrease the high compute cost of Transformers and to rectify the spectral bias of Transformers, respectively. In this work, we proposed a dynamic Hybrid Unrolled Multi-Scale Network (dHUMUS-Net) by incorporating the two multi-scale strategies. A novel Optimal Scale Estimation Network is presented to dynamically create or choose the multi-scale Transformer-based modules in all cascades of dHUMUS-Net. Our dHUMUS-Net achieves significant improvements over the state-of-the-art methods on the publicly available fastMRI dataset.