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.

中文翻译：

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用于压缩感知 MRI 的深度纠错网络。

CS-MRI（磁共振成像压缩传感）利用图像稀疏特性，从极少的傅里叶 k 空间测量中重建 MRI。由于逆向成像中的模型不完善，最先进的 CS-MRI 方法往往会留下结构重建错误。补偿重建中的此类误差有助于进一步提高重建质量。在这项工作中，我们提出了用于 CS-MRI 的 DECN（深度纠错网络）。DECN 模型由三部分组成，我们将其称为模块：指南或模板模块、纠错模块和数据保真度模块。现有的CS-MRI算法可以作为指导重建的模板模块。使用该模板作为指导，纠错模块学习 CNN（卷积神经网络）以调整模板图像的重建误差的方式映射 k 空间数据。我们提出了一个深度纠错网络。我们的实验结果表明，所提出的 DECN CS-MRI 重建框架可以通过补充纠错 CNN 来显着改进现有的反演算法。在提出的深度纠错框架中，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。有希望的实验结果验证了所提出框架的有效性和实用性。