Statistical image reconstruction for low-dose CT using nonlocal means-based regularization. Part II: An adaptive approach.
Statistical image reconstruction for low-dose CT using nonlocal means-based regularization. Part II: An adaptive approach.
复制标题
使用基于非局部均值的正则化进行低剂量 CT 统计图像重建。
DOI：
10.1016/j.compmedimag.2015.02.008
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发表时间：
2015-07
影响因子：
5.7
通讯作者：
Liang, Zhengrong
中科院分区：
文献类型：
--
作者：
Zhang, Hao;Ma, Jianhua;Wang, Jing;Liu, Yan;Han, Hao;Lu, Hongbing;Moore, William;Liang, Zhengrong
为了减少X射线计算机断层摄影（CT）成像中的辐射剂量，一种常见的策略是在投影数据采集期间降低管电流和曝光时间设置。然而，这种策略将不可避免地增加投影数据噪声，并且通过传统的滤波反投影（FBP）方法得到的图像可能遭受过多的噪声和条纹伪影。边缘保持非局部均值（NLM）滤波可以有效地消除FBP重建图像中的噪声伪影，但有时不能完全消除伪影，特别是在低剂量情况下图像质量严重下降时。提出了一种基于NLM正则化的统计图像重建方法，该方法可以有效抑制噪声伪影，显著提高重建图像质量。从我们之前对基于NLM的策略的研究中，我们注意到，在正则化中使用空间不变的滤波参数对于整个视场（FOV）来说很少是最佳的。因此，在这项研究中，我们开发了一种新的策略，用于设计空间变化的滤波参数，这是自适应的局部特征的图像重建。使用低对比度体模和临床患者数据对这种自适应NLM正则化统计图像重建方法进行了评价，以显示（1）引入空间自适应的必要性和（2）自适应在从低剂量采集重建CT图像方面实现优越性的有效性。
To reduce radiation dose in X-ray computed tomography (CT) imaging, one common strategy is to lower the tube current and exposure time settings during projection data acquisition. However, this strategy would inevitably increase the projection data noise, and the resulting image by the conventional filtered back-projection (FBP) method may suffer from excessive noise and streak artifacts. The well-known edge-preserving nonlocal means (NLM) filtering can reduce the noise-induced artifacts in the FBP reconstructed image, but it sometimes cannot completely eliminate the artifacts, especially under the very low-dose circumstance when the image is severely degraded. Instead of taking NLM filtering, we proposed a NLM-regularized statistical image reconstruction scheme, which can effectively suppress the noise-induced artifacts and significantly improve the reconstructed image quality. From our previous investigation on NLM-based strategy, we noted that using a spatially-invariant filtering parameter in the regularization was rarely optimal for the entire field of view (FOV). Therefore, in this study we developed a novel strategy for designing spatially-variant filtering parameters which are adaptive to the local characteristics of the image to be reconstructed. This adaptive NLM-regularized statistical image reconstruction method was evaluated with low-contrast phantoms and clinical patient data to show (1) the necessity in introducing the spatial adaptivity and (2) the efficacy of the adaptivity in achieving superiority in reconstructing CT images from low-dose acquisitions.
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