bibtext: @InProceedings{ Li_Dynamic_MICCAI2024,
                   author = { Li, Xiao-Xin and Zhu, Fang-Zheng and Yang, Junwei and Chen, Yong and Shen, Dinggang },
                   title = { { Dynamic Hybrid Unrolled Multi-Scale Network for Accelerated MRI Reconstruction } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15007 },
                   month = {October},
                   pages = { pending },
                }
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.
title: Dynamic Hybrid Unrolled Multi-Scale Network for Accelerated MRI Reconstruction
authors: Li, Xiao-Xin Zhu, Fang-Zheng Yang, Junwei Chen, Yong Shen, Dinggang