Department of Radiation Physics, University of Texas M D Anderson Cancer Center, Houston, TX 77030, USA.
Phys Med Biol. 2012 Jun 7;57(11):3537-53. doi: 10.1088/0031-9155/57/11/3537. Epub 2012 May 16.
In this paper, we study the feasibility of using the stochastic origin ensemble (SOE) algorithm for reconstructing images of secondary gammas emitted during proton radiotherapy from data measured with a three-stage Compton camera. The purpose of this study was to evaluate the quality of the images of the gamma rays emitted during proton irradiation produced using the SOE algorithm and to measure how well the images reproduce the distal falloff of the beam. For our evaluation, we performed a Monte Carlo simulation of an ideal three-stage Compton camera positioned above and orthogonal to a proton pencil beam irradiating a tissue phantom. Scattering of beam protons with nuclei in the phantom produces secondary gamma rays, which are detected by the Compton camera and used as input to the SOE algorithm. We studied the SOE reconstructed images as a function of the number of iterations, the voxel probability parameter, and the number of detected gammas used by the SOE algorithm. We quantitatively evaluated the capabilities of the SOE algorithm by calculating and comparing the normalized mean square error (NMSE) of SOE reconstructed images. We also studied the ability of the SOE reconstructed images to predict the distal falloff of the secondary gamma production in the irradiated tissue. Our results show that the images produced with the SOE algorithm converge in ~10,000 iterations, with little improvement to the image NMSE for iterations above this number. We found that the statistical noise of the images is inversely proportional to the ratio of the number of gammas detected to the SOE voxel probability parameter value. In our study, the SOE predicted distal falloff of the reconstructed images agrees with the Monte Carlo calculated distal falloff of the gamma emission profile in the phantom to within ±0.6 mm for the positions of maximum emission (100%) and 90%, 50% and 20% distal falloff of the gamma emission profile. We conclude that the SOE algorithm is an effective method for reconstructing images of a proton pencil beam from the data collected by an ideal Compton camera and that these images accurately model the distal falloff of secondary gamma emission during proton irradiation.
在本文中,我们研究了使用随机起源集合(SOE)算法从使用三阶段康普顿相机测量的数据中重建质子放射治疗过程中发射的二次伽马图像的可行性。本研究的目的是评估使用 SOE 算法生成的质子辐照过程中发射的伽马射线图像的质量,并测量图像对束流远侧衰减的再现程度。为了进行评估,我们对位于质子铅笔束上方且与其正交的理想三阶段康普顿相机进行了蒙特卡罗模拟,该铅笔束辐照组织体模。束质子与体模中的原子核散射产生二次伽马射线,这些射线被康普顿相机检测并用作 SOE 算法的输入。我们研究了 SOE 重建图像作为迭代次数、体素概率参数和 SOE 算法使用的检测伽马数量的函数。我们通过计算和比较 SOE 重建图像的归一化均方误差(NMSE)来定量评估 SOE 算法的能力。我们还研究了 SOE 重建图像预测辐照组织中二次伽马产生远侧衰减的能力。我们的结果表明,SOE 算法生成的图像在约 10,000 次迭代后收敛,在此次数以上,图像 NMSE 几乎没有改善。我们发现,图像的统计噪声与检测到的伽马数量与 SOE 体素概率参数值的比值成反比。在我们的研究中,SOE 预测的重建图像的远侧衰减与蒙特卡罗计算的体模中伽马发射轮廓的远侧衰减一致,在发射(100%)和 90%、50%和 20%的最大发射位置,伽马发射轮廓的远侧衰减的偏差在±0.6mm 以内。我们得出结论,SOE 算法是从理想康普顿相机收集的数据中重建质子铅笔束图像的有效方法,并且这些图像准确地模拟了质子辐照过程中二次伽马发射的远侧衰减。