We propose a Joint Deep Model-based MR Image and Coil Sensitivity Reconstruction Network (Joint-ICNet), which jointly reconstructs an MR image and coil sensitivity maps from undersampled multi-coil k-space data using deep learning networks combined with MR physical models. Joint-ICNet has two blocks, where one is an MR image reconstruction block that reconstructs an MR image from undersampled k-space data and the other is a coil sensitivity reconstruction block that estimates coil sensitivity from undersampled k-space data. The desired MR image and coil sensitivity maps can be obtained by sequentially estimating them with two blocks based on the unrolled network architecture.
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