A deep error correction network for compressed sensing MRI

BMC Biomedical Engineering - Tập 2 - Trang 1-12 - 2020
Liyan Sun1, Yawen Wu1, Zhiwen Fan1, Xinghao Ding1, Yue Huang1, John Paisley2
1Fujian Key Laboratory of Sensing and Computing for Smart City, Xiamen University, Xiamen, China
2Department of Electrical Engineering, Columbia University, New York, USA

Tóm tắt

CS-MRI (compressed sensing for magnetic resonance imaging) exploits image sparsity properties to reconstruct MRI from very few Fourier k-space measurements. Due to imperfect modelings in the inverse imaging, state-of-the-art CS-MRI methods tend to leave structural reconstruction errors. Compensating such errors in the reconstruction could help further improve the reconstruction quality. In this work, we propose a DECN (deep error correction network) 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 CNN (convolutional neural network) to map the k-space data in a way that adjusts for the reconstruction error of the template image. We propose a deep error correction network. 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. In the proposed a deep error correction framework, any off-the-shelf CS-MRI algorithm can be used as template generation. Then a deep neural network is used to compensate reconstruction errors. The promising experimental results validate the effectiveness and utility of the proposed framework.

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BMC Biomedical Engineering

Tập 2

1-12

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