Jia Huidi, Chen Xi'ai, Han Zhi, Liu Baichen, Wen Tianhui, Tang Yandong
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China.
Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China.
Front Neuroinform. 2022 Apr 25;16:880301. doi: 10.3389/fninf.2022.880301. eCollection 2022.
Limited by hardware conditions, imaging devices, transmission efficiency, and other factors, high-resolution (HR) images cannot be obtained directly in clinical settings. It is expected to obtain HR images from low-resolution (LR) images for more detailed information. In this article, we propose a novel super-resolution model for single 3D medical images. In our model, nonlocal low-rank tensor Tucker decomposition is applied to exploit the nonlocal self-similarity prior knowledge of data. Different from the existing methods that use a convex optimization for tensor Tucker decomposition, we use a tensor folded-concave penalty to approximate a nonlocal low-rank tensor. Weighted 3D total variation (TV) is used to maintain the local smoothness across different dimensions. Extensive experiments show that our method outperforms some state-of-the-art (SOTA) methods on different kinds of medical images, including MRI data of the brain and prostate and CT data of the abdominal and dental.
受硬件条件、成像设备、传输效率等因素限制，在临床环境中无法直接获取高分辨率（HR）图像。期望从低分辨率（LR）图像中获取HR图像以获得更详细的信息。在本文中，我们提出了一种用于单幅三维医学图像的新型超分辨率模型。在我们的模型中，应用非局部低秩张量塔克分解来利用数据的非局部自相似性先验知识。与现有的使用凸优化进行张量塔克分解的方法不同，我们使用张量折叠凹惩罚来近似非局部低秩张量。加权三维全变差（TV）用于保持不同维度之间的局部平滑性。大量实验表明，我们的方法在不同类型的医学图像上优于一些先进的（SOTA）方法，包括脑部和前列腺的MRI数据以及腹部和牙科的CT数据。
