Extension of the ML-EM algorithm for dose estimation using PET in proton therapy: application to an inhomogeneous target
Extension of the ML-EM algorithm for dose estimation using PET in proton therapy: application to an inhomogeneous target
复制标题
质子治疗中使用 PET 进行剂量估计的 ML-EM 算法的扩展：应用于不均匀目标
DOI：
10.1088/1361-6560/ab98cf
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
K. Karasawa
中科院分区：
文献类型：
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作者：
T. Masuda;T. Nishio;A. Sano;K. Karasawa
正电子发射断层扫描（PET）已被用于体内治疗验证，主要是范围验证，在质子治疗。评价PET测量的直接剂量仍然具有挑战性;然而，从临床角度来看，这是非常可取的。在这项研究中，使用最大似然期望最大化算法开发了一种用于从正电子发射体分布估计剂量分布的方法。基于滤波器框架，将非均匀靶中正电子发射源分布与剂量分布之间的一维空间关系输入到系统矩阵中。相反，在3D图像空间中考虑PET系统的空间分辨率和总变差正则化（作为剂量分布的先验知识）。使用蒙特卡罗模拟的PET放射性分布，在头部和颈部体模中具有大量噪声，证明了剂量估计。这模拟了临床剂量水平下展开的布拉格峰光束的单场照射。除了算法的简单实现外，该策略还实现了高速计算（3D剂量估计为30 s）和准确的剂量和范围估计（1-σ值分别小于10%和2 mm）。所提出的方法可能是使用PET进行体内剂量监测的关键。
Positron emission tomography (PET) has been used for in vivo treatment verification, mainly for range verification, in proton therapy. Evaluating the direct dose from PET measurements remains challenging; however, it is highly desirable from a clinical perspective. In this study, a method for estimating the dose distribution from the positron emitter distributions was developed using the maximum likelihood expectation maximization algorithm. The 1D spatial relationship between positron emitter distributions and a dose distribution in an inhomogeneous target was inputted into the system matrix based on a filter framework. In contrast, spatial resolution of the PET system and total variation regularization (as prior knowledge for dose distribution) were considered in the 3D image-space. The dose estimation was demonstrated using Monte Carlo simulated PET activity distributions with substantial noise in a head and neck phantom. This mimicked the single field irradiation of the spread-out Bragg peak beams at clinical dose levels. Besides the simple implementation of the algorithm, this strategy achieved a high-speed calculation (30 s for a 3D dose estimation) and accurate dose and range estimations (less than 10% and 2 mm errors at 1-σ values, respectively). The proposed method could be key for using PET for in vivo dose monitoring.