ML-EM algorithm for dose estimation using PET in proton therapy
ML-EM algorithm for dose estimation using PET in proton therapy
复制标题
质子治疗中使用 PET 进行剂量估计的 ML-EM 算法
DOI：
10.1088/1361-6560/ab3276
复制
发表时间：
2019
影响因子：
3.5
通讯作者：
Masuda,T.; Nishio,T.; Kataoka,J.; Arimoto,M.; Sano,A.; Karasawa,K.
中科院分区：
文献类型：
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作者：
T. Masuda;T. Nishio;A. Sano;K. Karasawa;Masuda,T.; Nishio,T.; Kataoka,J.; Arimoto,M.; Sano,A.; Karasawa,K.
正电子发射断层扫描 (PET) 已被广泛研究和临床研究，用于质子治疗中的剂量验证。然而，正电子发射体的产生分布与剂量分布不成比例。因此，当使用传统的基于 PET 的方法时，直接剂量评估受到限制。我们提出了一种使用最大似然（ML）期望最大化（EM）算法与滤波相结合的正电子发射体分布来估计剂量分布的方法。在验证该方法有效性的实验中，由同步加速器发射单能且分散的布拉格峰质子束，并以临床剂量水平照射水靶标。平面 PET 测量是在光束暂停期间和照射后总共 200 秒的时间内进行的。此外，我们还进行了蒙特卡洛模拟，以获得所需的滤波器函数，并分析了算法迭代次数对估计的影响。即使在 PET 图像的统计噪声下，我们也成功地估计了二维剂量分布。相对误差为 1 时，两个光束的二维剂量估计精度约为 10%。该值与测量的 PET 活性分布的偏差相当。对于沿光束方向的横向积分轮廓，每个辐照值获得了 5% 以内的低误差。此外，估计的质子范围的差异在 1 毫米以内，并且 PET 图像的二维估计在 21 毫秒内完成。因此，所提出的算法可应用于实时剂量监测。尽管这是首次尝试使用 ML-EM 算法进行剂量估计，但所提出的方法在根据 PET 数据估计质子剂量分布方面表现出较高的准确性和速度。因此，所提出的方法是充分利用 PET 进行体内剂量验证潜力的一步。
Positron emission tomography (PET) has been extensively studied and clinically investigated for dose verification in proton therapy. However, the production distributions of positron emitters are not proportional to the dose distribution. Thus, direct dose evaluation is limited when using the conventional PET-based approach. We propose a method for estimating the dose distribution from the positron emitter distributions using the maximum likelihood (ML) expectation maximization (EM) algorithm combined with filtering. In experiments to verify the effectiveness of the proposed method, mono-energetic and spread-out Bragg-peak proton beams were delivered by a synchrotron, and a water target was irradiated at clinical dose levels. Planar PET measurements were performed during beam pauses and after irradiation over a total period of 200 s. In addition, we conducted a Monte Carlo simulation to obtain the required filter functions and analyze the influence of the number of algorithm iterations on estimation. We successfully estimated the 2D dose distributions even under statistical noise in the PET images. The accuracy of the 2D dose estimation was about 10% for both beams at the 1-values of relative error. This value is comparable to the deviations in the measured PET activity distributions. For the laterally integrated profile along the beam direction, a low error within 5% was obtained per irradiation value. Moreover, the difference of estimated proton ranges was within 1 mm, and 2D estimation from the PET images was completed in 21 ms. Hence, the proposed algorithm may be applied to real-time dose monitoring. Although this is the first attempt to use the ML-EM algorithm for dose estimation, the proposed method showed high accuracy and speed in the estimation of proton dose distribution from PET data. The proposed method is thus a step forward to exploit the full potential of PET for in vivo dose verification.