Statistical characterization of noise for spatial standardization of CT scans: Enabling comparison with multiple kernels and doses
Statistical characterization of noise for spatial standardization of CT scans: Enabling comparison with multiple kernels and doses
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DOI：
10.1016/j.media.2017.06.001
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发表时间：
2017-08-01
影响因子：
10.9
通讯作者：
San Jose Estepar, Raid
中科院分区：
文献类型：
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作者：
Vegas-Sanchez-Ferrero, Gonzalo;Ledesma-Carbayo, Maria J.;San Jose Estepar, Raid
计算机断层扫描（CT）是一种被广泛采用的通过图像特征直接或间接分析功能、生物和形态过程的方法。然而，当考虑到涉及不同设备、采集协议或重建算法的定量信息分析时，从CT图像中获得的信息的潜在利用往往受到限制。虽然CT扫描仪的校准是成像工作流程的一部分，但校准仅限于全局参考值，并不能规避成像模式固有的问题。其中之一是缺乏噪声平稳性，这使得从图像中提取的定量生物标志物的鲁棒性和稳定性降低。已经提出了一些方法来评估重建CT扫描中的非平稳噪声。然而，这些方法只关注重建几何形状引起的非平稳性，并且主要基于噪声方差在整个重建过程中的传播。此外，最先进的方法所遵循的哲学是基于减少噪音，而不是标准化。这意味着，即使降低了噪声，信号的统计量仍然是非平稳的，这不足以使具有不同统计特征的不同采集之间进行比较。在这项工作中，我们提出了重建CT扫描噪声的统计特征，从而产生了一个通用的统计模型，可以有效地表征不同的剂量、重建核和设备。将统计模型推广到通过局部混合模型来处理部分体积效应，该模型还描述了噪声的非平稳性。最后，我们提出了一种稳定方案来实现平稳方差。通过物理模型和不同配置（核、剂量、包括迭代重建在内的算法）获得的临床CT扫描来验证所提出的方法。结果证实了其适用性，可以与不同剂量和获取方案进行比较。(C) 2017 Elsevier B.V.版权所有
Computerized tomography (CT) is a widely adopted modality for analyzing directly or indirectly functional, biological and morphological processes by means of the image characteristics. However, the potential utilization of the information obtained from CT images is often limited when considering the analysis of quantitative information involving different devices, acquisition protocols or reconstruction algorithms. Although CT scanners are calibrated as a part of the imaging workflow, the calibration is circumscribed to global reference values and does not circumvent problems that are inherent to the imaging modality. One of them is the lack of noise stationarity, which makes quantitative biomarkers extracted from the images less robust and stable. Some methodologies have been proposed for the assessment of non-stationary noise in reconstructed CT scans. However, those methods focused on the non-stationarity only due to the reconstruction geometry and are mainly based on the propagation of the variance of noise throughout the whole reconstruction process. Additionally, the philosophy followed in the state-of-the-art methods is based on the reduction of noise, but not in the standardization of it. This means that, even if the noise is reduced, the statistics of the signal remain non-stationary, which is insufficient to enable comparisons between different acquisitions with different statistical characteristics. In this work, we propose a statistical characterization of noise in reconstructed CT scans that leads to a versatile statistical model that effectively characterizes different doses, reconstruction kernels, and devices. The statistical model is generalized to deal with the partial volume effect via a localized mixture model that also describes the non-stationarity of noise. Finally, we propose a stabilization scheme to achieve stationary variance. The validation of the proposed methodology was performed with a physical phantom and clinical CT scans acquired with different configurations (kernels, doses, algorithms including iterative reconstruction). The results confirmed its suitability to enable comparisons with different doses, and acquisition protocols. (C) 2017 Elsevier B.V. All rights reserved.