Keywords: Image Reconstruction, Machine Learning/Artificial Intelligence
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.
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