We present a novel framework for the joint reconstruction and segmentation of parallel MRI (PMRI) brain data. We introduce an image domain deep network for calibrationless recovery of undersampled PMRI data. It is deep-learning based generalization of local low-rank approaches for uncalibrated PMRI recovery including CLEAR. The image domain approach exploits additional annihilation relations compared to k-space based approaches and hence offers improved performance. To minimize segmentation errors resulting from undersampling artifacts, we combined the proposed scheme with a segmentation network and trained it end-to-end. It offers improved reconstruction with reduced blurring and sharper edges than independently trained reconstruction network.
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