Hu Zhanli, Zheng Hairong
Paul Lauterbur Center for Biomedical Imaging Research, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Biomed Eng Online. 2014 Jun 5;13:70. doi: 10.1186/1475-925X-13-70.
Due to the harmful radiation dose effects for patients, minimizing the x-ray exposure risk has been an area of active research in medical computed tomography (CT) imaging. In CT, reducing the number of projection views is an effective means for reducing dose. The use of fewer projection views can also lead to a reduced imaging time and minimizing potential motion artifacts. However, conventional CT image reconstruction methods will appears prominent streak artifacts for few-view data. Inspired by the compressive sampling (CS) theory, iterative CT reconstruction algorithms have been developed and generated impressive results.
In this paper, we propose a few-view adaptive prior image total variation (API-TV) algorithm for CT image reconstruction. The prior image reconstructed by a conventional analytic algorithm such as filtered backprojection (FBP) algorithm from densely angular-sampled projections.
To validate and evaluate the performance of the proposed algorithm, we carried out quantitative evaluation studies in computer simulation and physical experiment.
The results show that the API-TV algorithm can yield images with quality comparable to that obtained with existing algorithms.
由于对患者存在有害辐射剂量影响，将X射线暴露风险降至最低一直是医学计算机断层扫描（CT）成像领域的一个活跃研究方向。在CT中，减少投影视图数量是降低剂量的有效手段。使用更少的投影视图还可以缩短成像时间并最大程度减少潜在的运动伪影。然而，传统的CT图像重建方法对于少视图数据会出现明显的条纹伪影。受压缩采样（CS）理论的启发，迭代CT重建算法已被开发出来并取得了令人瞩目的成果。
在本文中，我们提出了一种用于CT图像重建的少视图自适应先验图像全变差（API-TV）算法。先验图像由传统解析算法（如滤波反投影（FBP）算法）从密集角度采样投影中重建得到。
为了验证和评估所提算法的性能，我们在计算机模拟和物理实验中进行了定量评估研究。
结果表明，API-TV算法能够生成质量与现有算法相当的图像。
