论文标题
质子治疗治疗验证中梁内宠物图像的分析方法:基于蒙特卡洛模拟的比较
Analysis methods for in-beam PET images in proton therapy treatment verification: a comparison based on Monte Carlo simulations
论文作者
论文摘要
背景和目的:内部正电子发射断层扫描(PET)是可用于质子治疗中的体内非侵入性治疗监测的方式之一。可以比较在各种治疗过程中获得的宠物分布,以识别具有解剖变化的区域。这项工作的目的是测试和比较质子治疗验证的分裂间PET图像比较的不同分析方法。 方法:在我们的研究中,我们使用了Fluka Monte Carlo代码和人为生成的CT扫描来模拟质子治疗期间不同阶段的束内宠物分布。我们比较了Beam-eye-View方法,最可能的移位方法,基于体素的模型方法和Gamma评估方法,以在治疗开始时和几周的治疗后比较PET图像。将结果与CT扫描进行了比较。 结果和结论:如果有很多统计信息,则优选的三维方法(例如VBM和GAMMA)在MLS和BEV等二维方法之上首选,因为这些方法允许这些方法识别具有异常活动的区域。 VBM方法是需要大量MC模拟的缺点。伽马分析的缺点是,在耐受性标准上不存在临床指示。就计算时间而言,首选BEV和MLS方法。我们建议一起使用四种方法,以最好地确定活动变化的位置和原因。
Background and purpose: In-beam Positron Emission Tomography (PET) is one of the modalities that can be used for in-vivo non-invasive treatment monitoring in proton therapy. PET distributions obtained during various treatment sessions can be compared in order to identify regions that have anatomical changes. The purpose of this work is to test and compare different analysis methods in the context of inter-fractional PET image comparison for proton treatment verification. Methods: For our study we used the FLUKA Monte Carlo code and artificially generated CT scans to simulate in-beam PET distributions at different stages during proton therapy treatment. We compared the Beam-Eye-View method, the Most-Likely-Shift method, the Voxel-Based-Morphology method and the gamma evaluation method to compare PET images at the start of treatment, and after a few weeks of treatment. The results were compared to the CT scan. Results and conclusions: Three-dimensional methods like VBM and gamma are preferred above two-dimensional methods like MLS and BEV if much statistics is available, since the these methods allow to identify the regions with anomalous activity. The VBM approach has as disadvantage that a larger number of MC simulations is needed. The gamma analysis has the disadvantage that no clinical indication exist on tolerance criteria. In terms of calculation time, the BEV and MLS method are preferred. We recommend to use the four methods together, in order to best identify the location and cause of the activity changes.