Low-dose X-ray CT reconstruction via dictionary learning.
Low-dose X-ray CT reconstruction via dictionary learning.
复制标题
通过字典学习进行低剂量 X 射线 CT 重建
DOI：
10.1109/tmi.2012.2195669
复制
发表时间：
2012-09
影响因子：
10.6
通讯作者：
Wang G
中科院分区：
文献类型：
--
作者：
Xu Q;Yu H;Mou X;Zhang L;Hsieh J;Wang G
尽管诊断医学成像在各种疾病的早期检测和准确诊断方面提供了巨大的好处，但人们越来越关注辐射引起的遗传、癌症和其他疾病的潜在副作用。如何在保持诊断性能的同时降低辐射剂量是计算机断层扫描（CT）领域的主要挑战。受压缩感知理论的启发，总变差（TV）最小化方面的稀疏约束已经为低剂量CT重建带来了有希望的结果。与TV方法中使用的离散梯度变换相比，字典学习被证明是一种有效的稀疏表示方法。另一方面，在低剂量CT情况下，重要的是要考虑投影数据的统计特性。最近，我们开发了一种基于字典学习的低剂量X射线CT方法。在本文中，我们详细介绍了这种方法，并在实验中进行了评估。在我们的方法中，稀疏约束的冗余字典被纳入到一个目标函数的统计迭代重建框架。该字典可以在图像重建任务之前预先确定，或者在重建过程期间自适应地定义。一个交替的最小化计划，以最小化目标函数。我们的方法进行了评估，在动物和人类CT研究中收集的低剂量X射线投影，并量化与字典学习相关的改进相对于过滤反投影和基于电视的重建。结果表明，该方法可以产生更好的图像，更低的噪声和更详细的结构特征，在我们选定的情况下。然而，没有证据表明这对所有类型的结构都是正确的。
Although diagnostic medical imaging provides enormous benefits in the early detection and accuracy diagnosis of various diseases, there are growing concerns on the potential side effect of radiation induced genetic, cancerous and other diseases. How to reduce radiation dose while maintaining the diagnostic performance is a major challenge in the computed tomography (CT) field. Inspired by the compressive sensing theory, the sparse constraint in terms of total variation (TV) minimization has already led to promising results for low-dose CT reconstruction. Compared to the discrete gradient transform used in the TV method, dictionary learning is proven to be an effective way for sparse representation. On the other hand, it is important to consider the statistical property of projection data in the low-dose CT case. Recently, we have developed a dictionary learning based approach for low-dose X-ray CT. In this paper, we present this method in detail and evaluate it in experiments. In our method, the sparse constraint in terms of a redundant dictionary is incorporated into an objective function in a statistical iterative reconstruction framework. The dictionary can be either predetermined before an image reconstruction task or adaptively defined during the reconstruction process. An alternating minimization scheme is developed to minimize the objective function. Our approach is evaluated with low-dose X-ray projections collected in animal and human CT studies, and the improvement associated with dictionary learning is quantified relative to filtered backprojection and TV-based reconstructions. The results show that the proposed approach might produce better images with lower noise and more detailed structural features in our selected cases. However, there is no proof that this is true for all kinds of structures.