Published on Tue Aug 06 2019

Model-based Convolutional De-Aliasing Network Learning for Parallel MR Imaging

Yanxia Chen, Taohui Xiao, Cheng Li, Qiegen Liu, Shanshan Wang

Parallel imaging has been an essential technique to accelerate MR imaging. The acceleration rate is still limited due to the ill-condition and challenges associated with the undersampled reconstruction. We propose a model-based convolutional de-aliasing network with adaptive parameter learning.

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Abstract

Parallel imaging has been an essential technique to accelerate MR imaging. Nevertheless, the acceleration rate is still limited due to the ill-condition and challenges associated with the undersampled reconstruction. In this paper, we propose a model-based convolutional de-aliasing network with adaptive parameter learning to achieve accurate reconstruction from multi-coil undersampled k-space data. Three main contributions have been made: a de-aliasing reconstruction model was proposed to accelerate parallel MR imaging with deep learning exploring both spatial redundancy and multi-coil correlations; a split Bregman iteration algorithm was developed to solve the model efficiently; and unlike most existing parallel imaging methods which rely on the accuracy of the estimated multi-coil sensitivity, the proposed method can perform parallel reconstruction from undersampled data without explicit sensitivity calculation. Evaluations were conducted on \emph{in vivo} brain dataset with a variety of undersampling patterns and different acceleration factors. Our results demonstrated that this method could achieve superior performance in both quantitative and qualitative analysis, compared to three state-of-the-art methods.

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