Practical considerations for noise power spectra estimation for clinical CT scanners
Practical considerations for noise power spectra estimation for clinical CT scanners
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DOI：
10.1120/jacmp.v17i3.5841
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发表时间：
2016-01-01
影响因子：
2.1
通讯作者：
Li, Hua
中科院分区：
文献类型：
--
作者：
Dolly, Steven;Chen, Hsin-Chen;Li, Hua
局部噪声功率谱（Local Noise Power Spectrum，简称SNR）通常被用来表征CT成像系统的噪声特性，但其特性受计算方法的影响很大。本文分析了不同计算参数对局部扩散的影响，并对临床CT扫描仪局部扩散的估计提供了实用建议。Catphan体模的均匀性模块用Philips Brilliance 64层CT模拟器以不同的扫描协议扫描。使用FBP和iDose（4）迭代重建重建图像，降噪水平为1、3和6。计算并比较了不同感兴趣区域（ROI）位置和大小、图像背景去除方法和窗口函数的局部阈值。另外，以预定的SNR作为基础事实，针对计算机模拟的ROI比较局部SNR计算精度，除了ROI数量之外还改变上述参数。分析了这些不同的计算参数对地震波的大小和形状的影响。局部反射率随计算参数而变化，特别是在低于0.15 mm（-1）的低空间频率下。对于模拟研究，ROI计算误差随着ROI数量的增加呈指数下降。对于Catphan研究，振幅作为ROI位置的函数而变化，当使用较小的ROI尺寸时可以更好地观察到。背景去除的图像相减方法是最有效的，在减少低频背景噪声，并产生类似的结果，无论使用的ROI大小或窗口函数。具有Hann窗函数的PCA背景去除方法产生与图像减法最接近的匹配，平均百分比差异为17.5%。图像噪声应通过计算小ROI尺寸的SNR进行局部分析。根据所选的径向箱大小和图像像素尺寸，建议最小ROI大小。随着感兴趣区域尺寸的减小，背景去除方法和窗口函数的选择对信噪比的影响越来越大。图像减法是最准确的，但如果应用某些窗口函数，其他方法也可以达到类似的精度。在考虑对基于任务的图像质量评估的解释时，应分析并考虑所有相关性。
Local noise power spectra (NPS) have been commonly calculated to represent the noise properties of CT imaging systems, but their properties are significantly affected by the utilized calculation schemes. In this study, the effects of varied calculation parameters on the local NPS were analyzed, and practical suggestions were provided regarding the estimation of local NPS for clinical CT scanners. The uniformity module of a Catphan phantom was scanned with a Philips Brilliance 64 slice CT simulator with varied scanning protocols. Images were reconstructed using FBP and iDose(4) iterative reconstruction with noise reduction levels 1, 3, and 6. Local NPS were calculated and compared for varied region of interest (ROI) locations and sizes, image background removal methods, and window functions. Additionally, with a predetermined NPS as a ground truth, local NPS calculation accuracy was compared for computer simulated ROIs, varying the aforementioned parameters in addition to ROI number. An analysis of the effects of these varied calculation parameters on the magnitude and shape of the NPS was conducted. The local NPS varied depending on calculation parameters, particularly at low spatial frequencies below similar to 0.15 mm(-1). For the simulation study, NPS calculation error decreased exponentially as ROI number increased. For the Catphan study the NPS magnitude varied as a function of ROI location, which was better observed when using smaller ROI sizes. The image subtraction method for background removal was the most effective at reducing low-frequency background noise, and produced similar results no matter which ROI size or window function was used. The PCA background removal method with a Hann window function produced the closest match to image subtraction, with an average percent difference of 17.5%. Image noise should be analyzed locally by calculating the NPS for small ROI sizes. A minimum ROI size is recommended based on the chosen radial bin size and image pixel dimensions. As the ROI size decreases, the NPS becomes more dependent on the choice of background removal method and window function. The image subtraction method is most accurate, but other methods can achieve similar accuracy if certain window functions are applied. All dependencies should be analyzed and taken into account when considering the interpretation of the NPS for task-based image quality assessment.