MixRecon: A neural network mixing CNN and Transformer utilizes hybrid representations of image features for Accelerated MRI Reconstruction

Accelerated reconstruction for magnetic resonance imaging (MRI) is a challenging ill-posed problem because of the excessive under-sampling operation in k-space. Existing CNN-based and Transformer-based solutions face difficulties obtaining powerful representation due to relatively unitary local or global feature modeling capability. In this study, we develop a dual-branch network that can simultaneously exploit the complementarity of the two-style features by leveraging the merits of CNN and Transformer, to generate high-quality reconstruction from zero-filled images in the spatial domain. Qualitative and quantitative results from the fastMRI dataset demonstrate that the proposed method can achieve improved performance compared with other benchmark methods.