Sun Liyan, Wu Yawen, Fan Zhiwen, Ding Xinghao, Huang Yue, Paisley John
Fujian Key Laboratory of Sensing and Computing for Smart City, Xiamen University, Xiamen, China.
Department of Electrical Engineering, Columbia University, New York, USA.
BMC Biomed Eng. 2020 Feb 27;2:4. doi: 10.1186/s42490-020-0037-5. eCollection 2020.
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.
磁共振成像压缩感知（CS-MRI）利用图像稀疏特性，通过极少的傅里叶k空间测量来重建磁共振成像。由于逆成像中的建模不完善，当前最先进的CS-MRI方法往往会留下结构重建误差。在重建过程中补偿此类误差有助于进一步提高重建质量。
在这项工作中，我们提出了一种用于CS-MRI的深度误差校正网络（DECN）。DECN模型由三个部分组成，我们将其称为模块：一个引导或模板模块、一个误差校正模块和一个数据保真度模块。现有的CS-MRI算法可以用作引导重建的模板模块。以该模板为引导，误差校正模块学习一个卷积神经网络（CNN），以一种针对模板图像重建误差进行调整的方式对k空间数据进行映射。我们提出了一种深度误差校正网络。我们的实验结果表明，所提出的DECN CS-MRI重建框架通过补充一个误差校正CNN，能够在很大程度上改进现有的反演算法。
在所提出的深度误差校正框架中，任何现成的CS-MRI算法都可以用作模板生成。然后使用深度神经网络来补偿重建误差。有前景的实验结果验证了所提出框架的有效性和实用性。
