Dose volume histogram-based optimization of image reconstruction parameters for quantitative 90Y-PET imaging
Dose volume histogram-based optimization of image reconstruction parameters for quantitative 90Y-PET imaging
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DOI：
10.1002/mp.13269
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发表时间：
2019-01-01
期刊：
影响因子：
3.8
通讯作者：
Kappadath, S. Cheenu
中科院分区：
文献类型：
--
作者：
Siman, Wendy;Mikell, Justin K.;Kappadath, S. Cheenu
目的Y-90微球放射栓塞术或选择性内放射治疗越来越多地被用作不适合手术和外照射治疗的肿瘤的治疗选择。最近，体积Y-90剂量学技术已被应用于探索肿瘤的剂量反应，其基础是来自PET成像的3D Y-90活度分布。定量Y-90-PET图像重建的优化仍然使用平均活度浓度恢复系数(RC)作为目标函数，它与诊断和检测任务的相关性大于与剂量学的相关性。这项研究的目的是通过剂量体积直方图(DVH)最小化体积剂量学误差来优化Y-90-PET图像重建。我们提出了一种联合优化等效迭代次数(迭代和子集的乘积)和构建后过滤(FWHM)的方法来提高体素级Y-90剂量测量的精度。方法使用改良的NEMA IEC体模模拟临床相关的Y-90-PET成像条件，通过不同的采集时间、活度浓度、球底比和球径组合。在单床位置以列表模式采集PET数据300分钟；然后我们将列表模式PET数据重新组合到每张床60、45、30、15和5分钟，并有10种不同的实现。DVH的误差被计算为基于图像的DVH和预期DVH的差值的均方根误差(RMSE)。新的优化方法在模型研究中进行了测试，并将结果与更常用的平均活性浓度Rc目标函数进行了比较。结果在广泛的临床相关成像条件下，在使用带有飞行时间(TOF)和点扩散函数(PSF)模型的有序子集期望最大化(OSEM)迭代重建算法重建的Y-90-PET图像中，使用具有5.2 mm滤波的36次等效迭代可以减少基于图像的DVH的体积Y-90剂量学的系统误差。我们提出的最小化DVH误差的目标函数，允许联合优化Y-90-PET迭代和Y-90剂量体积量化的过滤，被证明优于传统的基于RC的基于图像的吸收剂量定量的优化方法。结论我们提出的最小化DVH误差的目标函数，允许联合优化迭代和过滤以减少基于PET的体积定量Y-90剂量的误差，该目标函数与治疗过程中的剂量学相关。所提出的以DVH为目标函数的优化方法可以应用于任何用于评估体素级别定量信息的成像方式。
Purpose Y-90-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-90-dosimetry techniques have been implemented to explore tumor dose-response on the basis of 3D Y-90-activity distribution from PET imaging. Despite being a theranostic study, the optimization of quantitative Y-90-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-90-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-90 dosimetry. Methods A modified NEMA IEC phantom was used to emulate clinically relevant Y-90-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. Results 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-90 dosimetry, quantified as image-based DVH, in Y-90-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-90-PET iterations and filtration for volumetric quantification of the Y-90 dose, was shown to be superior to conventional RC-based optimization approaches for image-based absorbed dose quantification. Conclusion 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-90 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.