Department of Radiology, The University of Tennessee Medical Center, Knoxville, TN, USA.
The University of Tennessee Graduate School of Medicine, Knoxville, TN, USA.
Med Phys. 2019 Jan;46(1):229-237. doi: 10.1002/mp.13269. Epub 2018 Nov 27.
Y-microsphere radioembolization or selective internal radiation therapy is increasingly being used as a treatment option for tumors that are not candidates for surgery and external beam radiation therapy. Recently, volumetric Y-dosimetry techniques have been implemented to explore tumor dose-response on the basis of 3D Y-activity distribution from PET imaging. Despite being a theranostic study, the optimization of quantitative Y-PET image reconstruction still uses the mean activity concentration recovery coefficient (RC) as the objective function, which is more relevant to diagnostic and detection tasks than is to dosimetry. The aim of this study was to optimize Y-PET image reconstruction by minimizing errors in volumetric dosimetry via the dose volume histogram (DVH). We propose a joint optimization of the number of equivalent iterations (the product of the iterations and subsets) and the postreconstruction filtration (FWHM) to improve the accuracy of voxel-level Y dosimetry.
A modified NEMA IEC phantom was used to emulate clinically relevant Y-PET imaging conditions through various combinations of acquisition durations, activity concentrations, sphere-to-background ratios, and sphere diameters. PET data were acquired in list mode for 300 min in a single-bed position; we then rebinned the list mode PET data to 60, 45, 30, 15, and 5 min per bed, with 10 different realizations. Errors in the DVH were calculated as root mean square errors (RMSE) of the differences in the image-based DVH and the expected DVH. The new optimization approach was tested in a phantom study, and the results were compared with the more commonly used objective function of the mean activity concentration RC.
In a wide range of clinically relevant imaging conditions, using 36 equivalent iterations with a 5.2-mm filtration resulted in decreased systematic errors in volumetric Y dosimetry, quantified as image-based DVH, in Y-PET images reconstructed using the ordered subset expectation maximization (OSEM) iterative reconstruction algorithm with time of flight (TOF) and point spread function (PSF) modeling. Our proposed objective function of minimizing errors in DVH, which allows for joint optimization of Y-PET iterations and filtration for volumetric quantification of the Y dose, was shown to be superior to conventional RC-based optimization approaches for image-based absorbed dose quantification.
Our proposed objective function of minimizing errors in DVH, which allows for joint optimization of iterations and filtration to reduce errors in the PET-based volumetric quantification Y dose, is relevant to dosimetry in therapy procedures. The proposed optimization method using DVH as the objective function could be applied to any imaging modality used to assess voxel-level quantitative information.
Y 微球放射性栓塞或选择性内部放射治疗越来越多地被用作不能进行手术和外束放射治疗的肿瘤的治疗选择。最近，基于 PET 成像的 3D Y 放射性分布，已经实施了容积 Y 剂量测定技术来探索肿瘤剂量反应。尽管这是一项治疗诊断研究，但定量 Y-PET 图像重建的优化仍然使用平均放射性浓度恢复系数（RC）作为目标函数，该函数与诊断和检测任务比剂量测定更相关。本研究的目的是通过最小化基于剂量体积直方图（DVH）的容积剂量测定误差来优化 Y-PET 图像重建。我们提出了一种联合优化等效迭代次数（迭代次数和子集的乘积）和重建后滤波（FWHM）的方法，以提高体素水平 Y 剂量测定的准确性。
使用改进的 NEMA IEC 体模，通过各种采集时间、放射性浓度、球与背景比和球径组合，模拟临床相关的 Y-PET 成像条件。在单个床位位置以列表模式采集 300 分钟的 PET 数据；然后将列表模式 PET 数据重新分组为 60、45、30、15 和 5 分钟/床位，有 10 种不同的实现。通过图像基 DVH 和期望 DVH 之间差异的均方根误差（RMSE）计算 DVH 的误差。在体模研究中测试了新的优化方法，并将结果与更常用的平均放射性浓度 RC 目标函数进行了比较。
在广泛的临床相关成像条件下，使用 36 次等效迭代和 5.2mm 滤波，使用具有飞行时间（TOF）和点扩散函数（PSF）建模的有序子集期望最大化（OSEM）迭代重建算法重建的 Y-PET 图像中，体素 Y 剂量的容积定量的系统误差减小，定量为基于图像的 DVH。我们提出的最小化 DVH 误差的目标函数允许对 Y-PET 迭代和滤波进行联合优化，以减少基于 PET 的体素 Y 剂量的容积定量误差，这与治疗过程中的剂量测定相关。使用 DVH 作为目标函数的建议优化方法可应用于任何用于评估体素水平定量信息的成像方式。
我们提出的最小化 DVH 误差的目标函数允许对迭代和滤波进行联合优化，以减少基于 PET 的体素 Y 剂量的容积定量误差，这与治疗过程中的剂量测定相关。使用 DVH 作为目标函数的建议优化方法可应用于任何用于评估体素水平定量信息的成像方式。
