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arXiv · 1803.08763

A Deep Error Correction Network for Compressed Sensing MRI

Liyan Sun·Zhiwen Fan·Yue Huang·Xinghao Ding·John Paisley
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Abstract

Compressed sensing for magnetic resonance imaging (CS-MRI) exploits image sparsity properties to reconstruct MRI from very few Fourier k-space measurements. The goal is to minimize any structural errors in the reconstruction that could have a negative impact on its diagnostic quality. To this end, we propose a deep error correction network (DECN) for CS-MRI. The DECN model consists of three parts, which we refer to as modules: a guide, or template, module, an error correction module, and a data fidelity module. Existing CS-MRI algorithms can serve as the template module for guiding the reconstruction. Using this template as a guide, the error correction module learns a convolutional neural network (CNN) to map the k-space data in a way that adjusts for the reconstruction error of the template image. Our experimental results show the proposed DECN CS-MRI reconstruction framework can considerably improve upon existing inversion algorithms by supplementing with an error-correcting CNN.

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Liyan Sun, Zhiwen Fan, Yue Huang, Xinghao Ding, John Paisley. 2018-03-23. A Deep Error Correction Network for Compressed Sensing MRI. https://arxiv.org/abs/1803.08763

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