Reconstruction and Segmentation of Parallel MR Data using Image Domain DEEP-SLR

TL;DR

This paper introduces a deep learning framework for joint reconstruction and segmentation of undersampled parallel MRI brain data, improving image quality and segmentation accuracy by leveraging image domain relations.

Contribution

It presents a novel image domain deep network for calibrationless PMRI recovery and integrates segmentation in an end-to-end training scheme, enhancing reconstruction and segmentation performance.

Findings

Reduced blurring and sharper edges in reconstructed images.

Improved segmentation accuracy with lower artifacts.

Enhanced reconstruction quality over traditional methods.

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 [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.