A method for partial volume correction of PET-imaged tumor heterogeneity using expectation maximization with a spatially varying point spread function.
A method for partial volume correction of PET-imaged tumor heterogeneity using expectation maximization with a spatially varying point spread function.
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DOI：
10.1088/0031-9155/55/1/013
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发表时间：
2010-01-07
影响因子：
3.5
通讯作者：
Jeraj R
中科院分区：
文献类型：
--
作者：
Barbee DL;Flynn RT;Holden JE;Nickles RJ;Jeraj R
在正电子发射断层扫描（PET）成像中观察到的肿瘤异质性经常受到部分体积效应的影响，这可能会影响治疗预后、评估或未来的实施，例如生物优化治疗计划（剂量绘制）。本文提出了一种PET成像的异质性肿瘤的部分体积校正方法。点源在GE Discover LS上在距扫描仪中心半径增加的位置处扫描，以获得空间变化的点扩散函数（PSF）。使用最小二乘优化将PSF图像在三维中拟合为高斯分布。连续表达式被设计为每个高斯宽度作为径向距离的函数，允许在空间中的任何位置处生成系统PSF。一个空间变化的部分体积校正（SV-PVC）技术开发使用期望最大化（EM）和停止标准的基础上，该方法的校正矩阵生成的每次迭代。使用标准肿瘤体模和肿瘤异质性体模对SV-PVC进行了验证，并将其应用于异质性患者肿瘤。SV-PVC的结果进行了比较，从空间不变的部分体积校正（SINV-PVC），其中使用定向均匀的三维内核获得的结果。标准肿瘤体模的SV-PVC使10 mm和13 mm直径球体的最大观察到的球体活性分别增加了55%和40%。肿瘤异质性体模结果表明，随着EM校正矩阵的净变化降低到35%以下，进一步迭代将总体定量准确度提高不到1%。临床观察到的肿瘤的SV-PVC在异质性区域经常显示±30%的变化。SV-PVC方法实现了空间变化的内核宽度，并自动确定最佳恢复的迭代次数，参数可以在SINV-PVC中任意选择。比较SV-PVC和SINV-PVC表明，使用两种方法可以达到相似的结果，但是对于任意选择的SINV-PVC参数，结果有很大差异。所提出的SV-PVC方法在没有用户干预的情况下进行，仅需要肿瘤掩模作为输入。涉及PET成像肿瘤异质性的研究应包括校正部分容积效应，以提高结果的定量准确性。
Tumor heterogeneities observed in positron emission tomography (PET) imaging are frequently compromised of partial volume effects which may affect treatment prognosis, assessment, or future implementations such as biologically optimized treatment planning (dose painting). This paper presents a method for partial volume correction of PET-imaged heterogeneous tumors. A point source was scanned on a GE Discover LS at positions of increasing radii from the scanner’s center to obtain the spatially varying point spread function (PSF). PSF images were fit in three dimensions to Gaussian distributions using least squares optimization. Continuous expressions were devised for each Gaussian width as a function of radial distance, allowing for generation of the system PSF at any position in space. A spatially varying partial volume correction (SV-PVC) technique was developed using expectation maximization (EM) and a stopping criterion based on the method’s correction matrix generated for each iteration. The SV-PVC was validated using a standard tumor phantom and a tumor heterogeneity phantom, and was applied to a heterogeneous patient tumor. SV-PVC results were compared to results obtained from spatially invariant partial volume correction (SINV-PVC), which used directionally uniform three dimensional kernels. SV-PVC of the standard tumor phantom increased the maximum observed sphere activity by 55 and 40% for 10 and 13 mm diameter spheres, respectively. Tumor heterogeneity phantom results demonstrated that as net changes in the EM correction matrix decreased below 35%, further iterations improved overall quantitative accuracy by less than 1%. SV-PVC of clinically observed tumors frequently exhibited changes of ±30% in regions of heterogeneity. The SV-PVC method implemented spatially varying kernel widths and automatically determined the number of iterations for optimal restoration, parameters which are arbitrarily chosen in SINV-PVC. Comparing SV-PVC to SINV-PVC demonstrated that similar results could be reached using both methods, but large differences result for the arbitrary selection of SINV-PVC parameters. The presented SV-PVC method was performed without user intervention, requiring only a tumor mask as input. Research involving PET-imaged tumor heterogeneity should include correcting for partial volume effects to improve the quantitative accuracy of results.
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