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Activity estimation in small volumes with non-uniform radiotracer uptake using a local projection-based fitting approach

机译:使用局部投影的配合方法具有非均匀放射性机构吸收的小体积的活性估计

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We have previously evaluated a local projection-based approach that provides robust estimates of activity concentration in small volumes-of-interest (VOI) affected by partial volume and tissue crosstalk in clinical SPECT imaging. The approach requires local segmentation of functionally distinct tissues within a VOI from a registered, high-resolution anatomical image of the object. Measured projection data are fitted to a statistical model of segmented-tissue projections. The resulting linear equations are solved to recover corrected values of tissue-activity concentration. In this work, we extended the approach to incorporate models of non-uniform radiotracer uptake into the fitting procedure. We evaluated the modified method using 25 independent noise realizations of simulated torso phantoms, each containing 20 identical, spherical “tumors” within a homogenous activity background. Radially varying quadratic functions were used to simulate two degrees (50% and 90%) of reduced central uptake inside the tumors (e.g., from necrosis), with an average integrated tumor-to-background concentration ratio of 7∶1. Tumor-activity estimates were obtained by fitting projection data to models that assumed either uniform tracer uptake (UF) or radially varying non-uniform uptake (NUF). The sensitivity of the approach to registration errors was investigated by simulating a 1-pixel misspecification of the locations of the VOI. The NUF approach achieved better than 1% bias and 12% precision for total tumor-activity estimates. Increasing the degree of central count loss from 50% to 90% did not significantly affect the NUF bias. In comparison, the UF method yielded 16% bias and 1% precision (48% bias, 5% precision) for 50% (90%) central count loss. The simulated registration error did not affect the UF estimates, but degraded the accuracy (and precision) of NUF estimates to 11% (and 13%). Despite slightly inferior precision, NUF may permit improved assessment of- inhomogeneous tumor uptake.
机译:我们之前评估了基于局部投影的方法,其为受部分体积和组织串扰影响的小体积患者(VOI)的活性浓度提供稳健估计,所述临床SPECT成像。该方法需要从对象的注册的高分辨率解剖图像的VOI内功能上不同组织的局部分割。测量的投影数据适用于分段组织投影的统计模型。得到了所得到的线性方程以恢复组织活性浓度的校正值。在这项工作中,我们扩展了将非均匀放射性机构吸收的模型延伸到配件程序中的方法。我们评估了使用模拟躯干幽灵的25个独立噪声实现的修改方法,每个噪声实现在同质活动背景下含有20个相同的球形“肿瘤”。径向变化的二次函数用于模拟肿瘤内部减少的两度(50%和90%)(例如,从坏死),平均整合肿瘤到背景浓度比为7:1。通过将投影数据拟合到假定均匀示踪剂摄取(UF)或径向变化的非均匀摄取(NUF)的模型来获得肿瘤活性估计。通过模拟VOI的位置的1像素误导,研究了对登记误差的方法的灵敏度。 NuF方法达到了1%的偏差和总肿瘤活性估计的12%精度。增加50%至90%的中央计数损失的程度没有显着影响Nuf偏差。相比之下,UF方法产生16%的偏差和1%精度(48%偏差,5%精度)50%(90%)中央计数损失。模拟登记误差不影响UF估计,但降低了NUF估计的准确度(和精度)至11%(和13%)。尽管精确略有较差,但诺夫可能允许改善对不均匀肿瘤摄取的评估。

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