As a fundamental task, medical image reconstruction has attracted growing attention in clinical diagnosis. Aiming at promising performance, it is critical to deeply understand and effectively design advanced model for image reconstruction. Indeed, one possible solution is to integrate the deep learning methods with the variational approaches to absorb benefits from both parts. In this paper, to protect more details and a better balance between the computational burden and the numerical performance, we carefully choose the multi-level wavelet convolutional neural network (MWCNN) for this issue. As the tight frame regularizer has the capability of maintaining edge information in image, we combine the MWCNN with the tight frame regularizer to reconstruct images. The proposed model can be solved by the celebrated proximal alternating minimization (PAM) algorithm. Furthermore, our method is a noise-adaptive framework as it can also handle real-world images. To prove the robustness of our strategy, we address two important medical image reconstruction tasks: Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). Extensive numerical experiments show clearly that our approach achieves better performance over several state-of-the-art methods.

中文翻译：

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使用多级深度学习降噪器和紧框架正则化进行医学图像重建

医学图像重建作为一项基本任务，在临床诊断中越来越受到关注。为了获得有希望的性能，深入理解和有效设计图像重建的先进模型至关重要。事实上，一种可能的解决方案是将深度学习方法与变分方法相结合，以吸收这两个部分的优点。在本文中，为了保护更多细节并在计算负担和数值性能之间取得更好的平衡，我们针对此问题仔细选择了多级小波卷积神经网络（MWCNN）。由于紧帧正则器具有保持图像边缘信息的能力，因此我们将MWCNN与紧帧正则器结合起来重建图像。所提出的模型可以通过著名的近端交替最小化（PAM）算法来求解。此外，我们的方法是一个噪声自适应框架，因为它还可以处理现实世界的图像。为了证明我们策略的稳健性，我们解决了两个重要的医学图像重建任务：磁共振成像（MRI）和正电子发射断层扫描（PET）。大量的数值实验清楚地表明，我们的方法比几种最先进的方法取得了更好的性能。