Improved regional activity quantitation in nuclear medicine using a new approach to correct for tissue partial volume and spillover effects.
Improved regional activity quantitation in nuclear medicine using a new approach to correct for tissue partial volume and spillover effects.
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DOI：
10.1109/tmi.2011.2169981
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发表时间：
2012-02
影响因子：
10.6
通讯作者：
Müller SP
中科院分区：
文献类型：
--
作者：
Moore SC;Southekal S;Park MA;McQuaid SJ;Kijewski MF;Müller SP
我们开发了一种新的方法来补偿双模态成像中的部分容积和溢出效应。该方法需要分割病变周围的小VOI内的几种组织类型;该算法同时从投影数据估计VOI内每个分割组织内的活性浓度。将测量的发射投影拟合到每个这样的组织的分辨率模糊投影的总和，由其未知的活性浓度缩放，加上通过VOI外部的重建图像体积的再投影获得的全局背景贡献。该方法使用多针孔μSPECT数据进行了评估，模拟MOBY小鼠体模包含两个球形肺肿瘤和一个肝肿瘤，以及使用多珠体模数据上获得的μSPECT和μCT扫描仪。模拟研究中的每个VOI为4.8 mm（12体素）的立方，并且根据位置，包含最多四个组织（肿瘤、肝脏、心脏、肺），具有不同的相对99 mTc浓度值。在~15次OSEM迭代（× 10个子集）后，所有肿瘤活性估计值均达到<3%偏倚，精确度优于8%（比Cramer-Rao下限高≤25%）。基于投影的拟合方法也优于三个SUV类指标，其中一个针对计数溢出进行了校正。在微珠体模实验中，微珠浓度VOI估计值偏倚的平均值±标准差为0.9 ± 9.5%，与微扰几何传递矩阵（pGTM）方法相当（-5.4 ± 8.6%）;然而，随着迭代次数的增加，VOI估计值比pGTM估计值更稳定，即使在μCT和μSPECT图像体积之间存在大量轴向未对准的情况下。
We have developed a new method of compensating for effects of partial volume and spillover in dual-modality imaging. The approach requires segmentation of just a few tissue types within a small VOI surrounding a lesion; the algorithm estimates simultaneously, from projection data, the activity concentration within each segmented tissue inside the VOI. Measured emission projections were fitted to the sum of resolution-blurred projections of each such tissue, scaled by its unknown activity concentration, plus a global background contribution obtained by reprojection through the reconstructed image volume outside the VOI. The method was evaluated using multiple-pinhole μSPECT data simulated for the MOBY mouse phantom containing two spherical lung tumors and one liver tumor, as well as using multiple-bead phantom data acquired on μSPECT and μCT scanners. Each VOI in the simulation study was 4.8 mm (12 voxels) cubed and, depending on location, contained up to four tissues (tumor, liver, heart, lung) with different values of relative 99mTc concentration. All tumor activity estimates achieved <3% bias after ~15 OSEM iterations (× 10 subsets), with better than 8% precision (≤25% greater than the Cramer-Rao lower bound). The projection-based fitting approach also outperformed three SUV-like metrics, one of which was corrected for count spillover. In the bead phantom experiment, the mean ± standard deviation of the bias of VOI estimates of bead concentration were 0.9 ± 9.5%, comparable to those of a perturbation geometric transfer matrix (pGTM) approach (-5.4 ± 8.6%); however, VOI estimates were more stable with increasing iteration number than pGTM estimates, even in the presence of substantial axial misalignment between μCT and μSPECT image volumes.