Low-dose computed tomography image restoration using previous normal-dose scan
Low-dose computed tomography image restoration using previous normal-dose scan
复制标题
使用先前的正常剂量扫描进行低剂量计算机断层扫描图像恢复
DOI：
10.1118/1.3638125
复制
发表时间：
2011-10-01
期刊：
影响因子：
3.8
通讯作者：
Chen, Wufan
中科院分区：
文献类型：
--
作者：
Ma, Jianhua;Huang, Jing;Chen, Wufan
目的：在当前的计算机断层扫描（CT）检查中，相关的X射线辐射剂量是患者和操作员的重要关注点。执行检查的一种简单且具有成本效益的方法是将毫安秒（mAs）或kVp参数（或向身体输送更少的X射线能量）降低到数据采集中可合理实现的最低值。然而，降低mAs参数将不可避免地增加数据噪声，并且如果在图像重建期间没有应用足够的噪声控制，则噪声将传播到CT图像中。由于常规剂量高诊断CT图像可用于CT灌注成像和CT血管造影（CTA）等临床应用，本文提出了一种利用常规剂量扫描作为先验信息来诱导当前低剂量CT图像序列信号恢复的创新方法。与传统的对相邻图像体素的局部操作不同，非局部均值（NLM）算法利用了整个图像的信息冗余。本文适应NLM利用冗余的信息在以前的正常剂量扫描，并进一步利用NLM框架中的低剂量图像恢复的非局部权重的优化方法。由此产生的算法被称为以前的正常剂量扫描引起的非局部均值（ndiNLM）。由于非局部权重计算的优化性质，ndiNLM算法并不严重依赖于当前低剂量和先前正常剂量CT扫描之间的图像配准。此外，在ndiNLM算法中所涉及的平滑参数可以自适应估计的基础上，当前的低剂量和以前的正常剂量扫描protocols.Results之间的图像噪声的关系：进行了定性和定量评价的物理幻影，以及临床腹部和脑灌注CT扫描的准确性和分辨率属性。与不使用之前的正常剂量扫描的类似方法相比，通过所提出的ndiNLM算法使用之前的正常剂量扫描的增益是显着的。结论：对于低剂量CT图像恢复，所提出的ndiNLM方法在保持空间分辨率和识别低对比度结构方面具有鲁棒性。作者可以得出结论，提出的ndiNLM算法可能是有用的一些临床应用，如灌注成像，放射治疗，肿瘤监测等（C）2011年美国医学物理学家协会。[DOI 10.1118/1.3638125]
Purpose: In current computed tomography (CT) examinations, the associated x-ray radiation dose is of a significant concern to patients and operators. A simple and cost-effective means to perform the examinations is to lower the milliampere-seconds (mAs) or kVp parameter (or delivering less x-ray energy to the body) as low as reasonably achievable in data acquisition. However, lowering the mAs parameter will unavoidably increase data noise and the noise would propagate into the CT image if no adequate noise control is applied during image reconstruction. Since a normal-dose high diagnostic CT image scanned previously may be available in some clinical applications, such as CT perfusion imaging and CT angiography (CTA), this paper presents an innovative way to utilize the normal-dose scan as a priori information to induce signal restoration of the current low-dose CT image series.Methods: Unlike conventional local operations on neighboring image voxels, nonlocal means (NLM) algorithm utilizes the redundancy of information across the whole image. This paper adapts the NLM to utilize the redundancy of information in the previous normal-dose scan and further exploits ways to optimize the nonlocal weights for low-dose image restoration in the NLM framework. The resulting algorithm is called the previous normal-dose scan induced nonlocal means (ndiNLM). Because of the optimized nature of nonlocal weights calculation, the ndiNLM algorithm does not depend heavily on image registration between the current low-dose and the previous normal-dose CT scans. Furthermore, the smoothing parameter involved in the ndiNLM algorithm can be adaptively estimated based on the image noise relationship between the current low-dose and the previous normal-dose scanning protocols.Results: Qualitative and quantitative evaluations were carried out on a physical phantom as well as clinical abdominal and brain perfusion CT scans in terms of accuracy and resolution properties. The gain by the use of the previous normal-dose scan via the presented ndiNLM algorithm is noticeable as compared to a similar approach without using the previous normal-dose scan.Conclusions: For low-dose CT image restoration, the presented ndiNLM method is robust in preserving the spatial resolution and identifying the low-contrast structure. The authors can draw the conclusion that the presented ndiNLM algorithm may be useful for some clinical applications such as in perfusion imaging, radiotherapy, tumor surveillance, etc. (C) 2011 American Association of Physicists in Medicine. [DOI: 10.1118/1.3638125]