Compressed Sensing theory breaks through the limitation of the Nyquist sampling law and provides theoretical support for accelerating the imaging process of MRI while reconstructing high-quality images. It can use less sampling data through the reconstructed algorithm to restore the original signal. Reconstruction time and reconstruction algorithms play important roles in compressed sensing. However, it is difficult to apply the iterative reconstruction method in clinical applications because of the long reconstruction time. In addition, hand-crafted image prior which is widely used in traditional iterative method lacks of adaptivity for MRI reconstruction. In this paper, inspired by the total variation model, we propose a novel deep network for CSMRI, dubbed as TV-Inspired Network (TVINet), which incorporates the deep priors into the traditional iterative algorithm. We apply a general compressed sensing reconstruction framework inspired by TV regularization and solve it by combines the iterative method with deep learning. The design of TVINet comes from the inference process on the basis of Primal Dual Hybrid Gradient algorithm, which makes the network has preferable interpretability. The linear operator and regularization term are learned from the training dataset by using a convolution network. The proposed approach trains the network end-to-end and reconstructs the desired image directly from the under-sampling data. Experimental results show that the proposed method has better PSNR or SSIM than state-of-the-art methods, and maintains more complex and fine details in the reconstructed image.

压缩传感理论突破了奈奎斯特采样定律的局限，为加速MRI成像过程同时重建高质量图像提供了理论支持。通过重构算法，它可以使用更少的采样数据来恢复原始信号。重建时间和重建算法在压缩感测中起着重要作用。然而，由于重建时间长，难以将迭代重建方法应用于临床。另外，在传统的迭代方法中广泛使用的手工图像先验缺乏对MRI重建的适应性。在本文中，受总变异模型的启发，我们为CSMRI提出了一种新颖的深度网络，称为TV启发网络（TVINet），它将深的先验知识整合到传统的迭代算法中。我们应用了受电视规则化启发的通用压缩感知重建框架，并将迭代方法与深度学习相结合来解决。TVINet的设计来自基于原始对偶混合梯度算法的推理过程，使网络具有较好的可解释性。通过使用卷积网络从训练数据集中学习线性算子和正则项。所提出的方法端到端训练网络，并直接从欠采样数据中重建所需的图像。实验结果表明，与现有方法相比，该方法具有更好的PSNR或SSIM，并且在重建图像中保留了更复杂，更精细的细节。我们应用了受电视规则化启发的通用压缩感知重建框架，并将迭代方法与深度学习相结合来解决。TVINet的设计来自基于原始对偶混合梯度算法的推理过程，使网络具有较好的可解释性。通过使用卷积网络从训练数据集中学习线性算子和正则项。所提出的方法端到端训练网络，并直接从欠采样数据中重建所需的图像。实验结果表明，与现有方法相比，该方法具有更好的PSNR或SSIM，并且在重建图像中保留了更复杂，更精细的细节。我们应用了受电视规则化启发的通用压缩感知重建框架，并将迭代方法与深度学习相结合来解决。TVINet的设计来自基于原始对偶混合梯度算法的推理过程，使网络具有较好的可解释性。通过使用卷积网络从训练数据集中学习线性算子和正则项。所提出的方法端到端训练网络，并直接从欠采样数据中重建所需的图像。实验结果表明，与现有方法相比，该方法具有更好的PSNR或SSIM，并且在重建图像中保留了更复杂，更精细的细节。