Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 75 Francis St., Boston, MA 02115, USA.
Phys Med Biol. 2012 Feb 7;57(3):685-701. doi: 10.1088/0031-9155/57/3/685. Epub 2012 Jan 13.
A new method of compensating for tissue-fraction and count-spillover effects, which require tissue segmentation only within a small volume surrounding the primary lesion of interest, was evaluated for SPECT imaging. Tissue-activity concentration estimates are obtained by fitting the measured projection data to a statistical model of the segmented tissue projections. Multiple realizations of two simulated human-torso phantoms, each containing 20 spherical 'tumours', 1.6 cm in diameter, with tumour-to-background ratios of 8:1 and 4:1, were simulated. Estimates of tumour- and background-activity concentration values for homogeneous as well as inhomogeneous tissue activities were compared to the standard uptake value (SUV) metrics on the basis of accuracy and precision. For perfectly registered, high-contrast, superficial lesions in a homogeneous background without scatter, the method yielded accurate (<0.4% bias) and precise (<6.1%) recovery of the simulated activity values, significantly outperforming the SUV metrics. Tissue inhomogeneities, greater tumour depth and lower contrast ratios degraded precision (up to 11.7%), but the estimates remained almost unbiased. The method was comparable in accuracy but more precise than a well-established matrix inversion approach, even when errors in tumour size and position were introduced to simulate moderate inaccuracies in segmentation and image registration. Photon scatter in the object did not significantly affect the accuracy or precision of the estimates.
一种新的补偿组织分数和计数溢出效应的方法,仅需要在感兴趣的主要病变周围的小体积内进行组织分割,已被评估用于 SPECT 成像。通过将测量的投影数据拟合到分段组织投影的统计模型,获得组织活性浓度估计值。模拟了两个包含 20 个直径为 1.6 厘米的“肿瘤”的模拟人体躯干体模的多次实现,肿瘤与背景的比例为 8:1 和 4:1。基于准确性和精密度,将均匀和不均匀组织活性的肿瘤和背景活性浓度值的估计值与标准摄取值 (SUV) 指标进行了比较。对于均匀背景中无散射的完全注册、高对比度、浅层病变,该方法能够准确(<0.4%的偏差)且精确(<6.1%)地恢复模拟的活性值,显著优于 SUV 指标。组织不均匀性、更大的肿瘤深度和更低的对比度比会降低精度(高达 11.7%),但估计值仍然几乎没有偏差。该方法在准确性上与一种成熟的矩阵反演方法相当,但更精确,即使在引入肿瘤大小和位置的误差以模拟分割和图像注册中的中度不准确性时也是如此。物体中的光子散射不会显著影响估计值的准确性或精密度。