Deep Parallel MRI Reconstruction Network Without Coil Sensitivities

Wanyu Bian; Yunmei Chen; Xiaojing Ye

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arXiv · 2008.01410

Deep Parallel MRI Reconstruction Network Without Coil Sensitivities

Wanyu Bian·Yunmei Chen·Xiaojing Ye

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Abstract

We propose a novel deep neural network architecture by mapping the robust proximal gradient scheme for fast image reconstruction in parallel MRI (pMRI) with regularization function trained from data. The proposed network learns to adaptively combine the multi-coil images from incomplete pMRI data into a single image with homogeneous contrast, which is then passed to a nonlinear encoder to efficiently extract sparse features of the image. Unlike most of existing deep image reconstruction networks, our network does not require knowledge of sensitivity maps, which can be difficult to estimate accurately, and have been a major bottleneck of image reconstruction in real-world pMRI applications. The experimental results demonstrate the promising performance of our method on a variety of pMRI imaging data sets.

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Wanyu Bian, Yunmei Chen, Xiaojing Ye. 2020-08-04. Deep Parallel MRI Reconstruction Network Without Coil Sensitivities. https://arxiv.org/abs/2008.01410

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