Keywords: Machine Learning/Artificial Intelligence, Parallel ImagingJSENSE iteratively optimizes sensitivity maps and the image, so the sensitivity profile can be gradually improved during the reconstruction process. The initially reconstructed image in the first iteration is obtained by the initially estimated coil sensitivity maps. The initial coil sensitivity profiles may be inaccurate and therefore degrade the quality of the subsequent image quality and coil sensitivity map estimation in the iterative optimization process. We propose to use unrolled deep network prior to replace the initial reconstruction in the conventional JSENSE for improving the image reconstruction quality. Experimental results show that the proposed method outperforms CG-SENSE and JSENSE.
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