Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China.
Med Phys. 2011 Oct;38(10):5713-31. doi: 10.1118/1.3638125.
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.
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.
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.
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.
在当前的计算机断层扫描（CT）检查中，患者和操作人员都非常关注相关的 X 射线辐射剂量。一种简单且经济有效的检查方法是尽可能降低毫安秒（mAs）或千伏（kVp）参数（即向身体输送较少的 X 射线能量）。然而，降低 mAs 参数将不可避免地增加数据噪声，如果在图像重建过程中不应用适当的噪声控制，则噪声将传播到 CT 图像中。由于在某些临床应用中，例如 CT 灌注成像和 CT 血管造影（CTA），可能已经有了之前正常剂量的高诊断性 CT 扫描图像，因此本文提出了一种创新的方法，利用正常剂量扫描作为先验信息，来诱导当前低剂量 CT 图像序列的信号恢复。
与传统的邻域图像体素局部操作不同，非局部均值（NLM）算法利用了整个图像中的信息冗余。本文采用 NLM 算法来利用之前正常剂量扫描中的信息冗余，并进一步探索了在 NLM 框架内优化低剂量图像恢复中非局部权重的方法。由此得到的算法被称为先前正常剂量扫描诱导的非局部均值（ndiNLM）算法。由于非局部权重计算的优化性质，ndiNLM 算法对当前低剂量和之前正常剂量 CT 扫描之间的图像配准依赖性不强。此外，ndiNLM 算法中涉及的平滑参数可以根据当前低剂量和之前正常剂量扫描协议之间的图像噪声关系自适应地估计。
在物理体模以及临床腹部和脑部灌注 CT 扫描中，从准确性和分辨率特性方面进行了定性和定量评估。与不使用先前正常剂量扫描的类似方法相比，使用本文提出的 ndiNLM 算法通过先前正常剂量扫描获得的增益是显而易见的。
对于低剂量 CT 图像恢复，本文提出的 ndiNLM 方法在保持空间分辨率和识别低对比度结构方面非常有效。作者可以得出结论，本文提出的 ndiNLM 算法可能对一些临床应用有用，例如灌注成像、放疗、肿瘤监测等。
