A Model of Regularization Parameter Determination in Low-Dose X-Ray CT Reconstruction Based on Dictionary Learning.
A Model of Regularization Parameter Determination in Low-Dose X-Ray CT Reconstruction Based on Dictionary Learning.
复制标题
基于字典学习的低剂量X射线CT重建正则化参数确定模型
DOI：
10.1155/2015/831790
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发表时间：
2015
影响因子：
--
通讯作者：
Guan Y
中科院分区：
文献类型：
--
作者：
Zhang C;Zhang T;Zheng J;Li M;Lu Y;You J;Guan Y
近年来，X射线计算机断层扫描（CT）被广泛用于显示患者的解剖信息。然而，辐射的副作用，与遗传或癌症有关，引起了公众的极大关注。问题是如何在保持图像质量的同时最大限度地减少辐射剂量。作为压缩感知理论的一种实际应用，其中一类方法以总变差（TV）最小为稀疏约束，使得在欠采样情况下获得高质量的重建图像成为可能和有效。另一方面，基于字典学习的低剂量CT重建的初步尝试似乎是另一种有效的选择。但是，一些关键参数，如正则化参数，不能通过检测数据集来确定。在本文中，我们提出了一个重新加权的目标函数，有助于数值计算模型的正则化参数。大量的实验表明，该策略具有良好的性能，具有更好的重建图像和节省大量的时间。
In recent years, X-ray computed tomography (CT) is becoming widely used to reveal patient's anatomical information. However, the side effect of radiation, relating to genetic or cancerous diseases, has caused great public concern. The problem is how to minimize radiation dose significantly while maintaining image quality. As a practical application of compressed sensing theory, one category of methods takes total variation (TV) minimization as the sparse constraint, which makes it possible and effective to get a reconstruction image of high quality in the undersampling situation. On the other hand, a preliminary attempt of low-dose CT reconstruction based on dictionary learning seems to be another effective choice. But some critical parameters, such as the regularization parameter, cannot be determined by detecting datasets. In this paper, we propose a reweighted objective function that contributes to a numerical calculation model of the regularization parameter. A number of experiments demonstrate that this strategy performs well with better reconstruction images and saving of a large amount of time.