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CBCT Iterative Image Reconstruction Method Using Energy Spectrum Information for Adaptive Proton Therapy

机译:能量谱信息的CBCT迭代图像重建用于自适应质子治疗

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Cone-beam computed tomography (CBCT) is used to determine a patient's position in proton therapy. Its image quality is low compared to that of a conventional CT because data measured by a two-dimensional detector used in CBCT contain scattered X-ray components. Correcting for scattered X-rays using the Klein-Nishina’s formula can improve CBCT image quality, but the formula requires the atomic number and atomic number density of substances. In this work, I developed a photoncounting image reconstruction method for estimating the atomic number Z and atomic number density N using the energy information of X-rays. In this study, NZ and Z4 were calculated as variables in consideration of the convergence of the optimization algorithm. I applied the developed method to an X-ray energy spectrum of a gantry-mounted CBCT, which was simulated with a Monte Carlo simulation code. The result was possible to distinguish soft tissues from water in the simulated object, which was not possible without the energy information. An atomic number and number density obtained with our method allow calculating the stopping power of protons more accurately, which can contribute to improving dose calculation accuracy.
机译:锥形束计算机断层扫描(CBCT)用于确定患者在质子治疗中的位置。与常规CT相比,其图像质量低,因为CBCT中使用的二维检测器测量的数据包含散射的X射线分量。使用Klein-Nishina公式校正散射X射线可以提高CBCT图像质量,但是该公式要求物质的原子序数和原子序数密度。在这项工作中,我开发了一种光子计数图像重建方法,用于使用X射线的能量信息来估计原子序数Z和原子序数密度N。在这项研究中,NZ和Z 4 考虑到优化算法的收敛性,将它们作为变量进行计算。我将开发的方法应用于安装在龙门架上的CBCT的X射线能谱,并使用Monte Carlo仿真代码对其进行了仿真。结果有可能在模拟对象中将软组织与水中区分开,而如果没有能量信息,这是不可能的。用我们的方法获得的原子序数和数密度可以更精确地计算质子的终止能力,这有助于提高剂量计算的准确性。

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