School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, China.
Department of Geography and Anthropology, Louisiana State University, Baton Rouge, LA 70803, USA.
Sensors (Basel). 2022 Apr 9;22(8):2883. doi: 10.3390/s22082883.
As a detection method, X-ray Computed Tomography (CT) technology has the advantages of clear imaging, short detection time, and low detection cost. This makes it more widely used in clinical disease screening, detection, and disease tracking. This study exploits the ability of sparse representation to learn sparse transformations of information and combines it with image decomposition theory. The structural information of low-dose CT images is separated from noise and artifact information, and the sparse expression of sparse transformation is used to improve the imaging effect. In this paper, two different learned sparse transformations are used. The first covers more organizational information about the scanned object. The other can cover more noise artifacts. Both methods can improve the ability to learn sparse transformations to express various image information. Experimental results show that the algorithm is effective.
作为一种检测方法，X 射线计算机断层扫描（CT）技术具有成像清晰、检测时间短、检测成本低的优点。这使得它在临床疾病筛查、检测和疾病跟踪中得到了更广泛的应用。本研究利用稀疏表示的能力来学习信息的稀疏变换，并将其与图像分解理论相结合。从噪声和伪影信息中分离出低剂量 CT 图像的结构信息，并使用稀疏变换的稀疏表示来改善成像效果。在本文中，使用了两种不同的学习稀疏变换。第一种方法涵盖了更多关于被扫描物体的组织信息。另一种可以覆盖更多的噪声伪影。这两种方法都可以提高学习稀疏变换来表达各种图像信息的能力。实验结果表明，该算法是有效的。
