Pramanik Aniket, Jacob Mathews
The University of Iowa, Iowa City, USA.
Proc IEEE Int Symp Biomed Imaging. 2021 Apr;2021. doi: 10.1109/isbi48211.2021.9434056. Epub 2021 May 25.
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.
这项工作的主要重点是用于并行磁共振成像（PMRI）脑数据联合重建与分割的新型框架。我们引入了一种图像域深度网络，用于欠采样PMRI数据的无校准恢复。所提出的方法是基于深度学习（DL）的对包括CLEAR [6]在内的用于未校准PMRI恢复的基于局部低秩方法的推广。由于与基于k空间的方法相比，图像域方法利用了额外的消除关系，我们期望它能提供更好的性能。为了最小化由欠采样伪影导致的分割误差，我们将所提出的方案与分割网络相结合，并以端到端的方式进行训练。除了减少分割误差外，这种方法还通过减少过拟合提供了更好的重建性能；与独立训练的重建网络相比，重建图像的模糊度降低且边缘更清晰。
