In this paper we present a generalized Deep Learning-based approach for solving ill-posed large-scale inverse problems occuring in medical image reconstruction. Recently, Deep Learning methods using iterative neural networks (NNs) and cascaded NNs have been reported to achieve state-of-the-art results with respect to various quantitative quality measures as PSNR, NRMSE and SSIM across different imaging modalities. However, the fact that these approaches employ the application of the forward and adjoint operators repeatedly in the network architecture requires the network to process the whole images or volumes at once, which for some applications is computationally infeasible. In this work, we follow a different reconstruction strategy by strictly separating the application of the NN, the regularization of the solution and the consistency with the measured data. The regularization is given in the form of an image prior obtained by the output of a previously trained NN which is us...

中文翻译：

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基于神经网络的正则化用于大规模医学图像重建

在本文中，我们提出了一种基于通用深度学习的方法来解决医学图像重建中出现的不适定大规模逆问题。最近，据报道，使用迭代神经网络（NN）和级联NN的深度学习方法在不同的成像方式上，针对各种定量质量度量（例如PSNR，NRMSE和SSIM）实现了最新的结果。但是，这些方法在网络体系结构中反复采用前向和伴随运算符的应用这一事实要求网络立即处理整个图像或卷，这对于某些应用程序在计算上是不可行的。在这项工作中，我们通过严格区分NN的应用遵循不同的重建策略，解决方案的规范化以及与测量数据的一致性。正则化以图像的形式给出，该图像是通过先前训练过的NN的输出而获得的图像，这是我们...