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Reconstruction and Segmentation of Parallel MR Data Using Image Domain Deep-SLR
Conference proceeding

Reconstruction and Segmentation of Parallel MR Data Using Image Domain Deep-SLR

Aniket Pramanik and Mathews Jacob
2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp.1095-1098
04/13/2021
DOI: 10.1109/ISBI48211.2021.9434056
PMCID: PMC8330410
PMID: 34354795
url
https://arxiv.org/pdf/2102.01172View
Open Access

Abstract

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 [1]. 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.
 Image segmentation CNN Parallel MRI Magnetic resonance imaging Image edge detection calibrationless Data models Image reconstruction Biomedical imaging

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