  The main focus of this work is 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. The
proposed approach is the deep-learning (DL) based generalization of local
low-rank based approaches for uncalibrated PMRI recovery including CLEAR [6].
Since the image domain approach exploits additional annihilation relations
compared to k-space based approaches, we expect it to offer improved
performance. To minimize segmentation errors resulting from undersampling
artifacts, we combined the proposed scheme with a segmentation network and
trained it in an end-to-end fashion. In addition to reducing segmentation
errors, this approach also offers improved reconstruction performance by
reducing overfitting; the reconstructed images exhibit reduced blurring and
sharper edges than independently trained reconstruction network.
