Rapid Monte Carlo simulation of detector DQE(f)
Rapid Monte Carlo simulation of detector DQE(f)
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DOI：
10.1118/1.4865761
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发表时间：
2014-03-01
期刊：
影响因子：
3.8
通讯作者：
Abel, Eric
中科院分区：
文献类型：
--
作者：
Star-Lack, Josh;Sun, Mingshan;Abel, Eric
目的：间接X射线探测器的性能优化需要对非均匀介质中的电离(伽马)和光学光子传输进行适当的表征。作为探测器物理建模的首选工具，蒙特卡罗方法未能成为一种设计实用工具，主要是由于模拟时间过长和缺乏方便的模拟包。评估探测器性能的最重要指标是探测量子效率(DQE)，其大部分计算负担传统上与从洪水图像集合确定噪声功率谱(NPS)有关，每个集合通常具有10(7)-10(9)个探测到的伽马光子。在这项工作中，作者表明，数值模拟中固有的理想化条件允许精确预测NPS所需的伽马光子和光学光子数量的显著减少。方法：作者推导了使用国际电工委员会推荐的基于洪水图像的2D傅立叶变换技术计算模拟NPS时的均方误差(MSE)的表达式。结果表明，均方误差与洪水图像的个数成反比，并与输入通量无关，只要输入通量大于避免估计偏差的最小值。然后，作者建议进一步降低输入通量，以便每个事件创建一个点扩散函数，而不是一个洪泛场。作者将这一发现作为一种新算法的基础，在该算法中，特征MTF(F)、NPS(F)和DQE(F)曲线同时从单次运行的结果生成。作者还研究了减少闪烁体模拟中使用的光学光子数量，以进一步提高效率。将模拟结果与VARIAN AS1000门脉成像仪上的测量结果以及先前发表的使用临床通量水平进行的模拟结果进行比较。结果：为了避免NPS估计的偏差，每个洪水图像只需要检测10-100个伽马光子。这使得与临床水平相比，在不损失准确性的情况下，影响减少了10(7)倍。通过将洪水图像的数量从典型的100张增加到500张，获得了最佳的信噪比，从而说明了洪水图像数量相对于每次洪水的伽马数量的重要性。对于点扩散集成技术，入射伽马的数量额外减少了2倍。因此，当在厚像素化阵列中建模伽马传输时，如果使用临床通量水平，模拟时间从2.5x10(6)CPU分钟减少到3.1CPU分钟，同时还产生更高的SNR。AS1000DQE(F)模拟的光学输运和辐射输运都与实验结果吻合，在单个CPU上需要14.5min才能完成。结论：作者论证了用单个CPU精确建模X光探测器DQE(F)的可行性，完成时间在几分钟左右。使用GEANT4可以实现模拟的便利性，它同时提供伽马和光学光子传输功能。(C)2014年美国医学物理学家协会。
Purpose: Performance optimization of indirect x-ray detectors requires proper characterization of both ionizing (gamma) and optical photon transport in a heterogeneous medium. As the tool of choice for modeling detector physics, Monte Carlo methods have failed to gain traction as a design utility, due mostly to excessive simulation times and a lack of convenient simulation packages. The most important figure-of-merit in assessing detector performance is the detective quantum efficiency (DQE), for which most of the computational burden has traditionally been associated with the determination of the noise power spectrum (NPS) from an ensemble of flood images, each conventionally having 10(7) - 10(9) detected gamma photons. In this work, the authors show that the idealized conditions inherent in a numerical simulation allow for a dramatic reduction in the number of gamma and optical photons required to accurately predict the NPS.Methods: The authors derived an expression for the mean squared error (MSE) of a simulated NPS when computed using the International Electrotechnical Commission-recommended technique based on taking the 2D Fourier transform of flood images. It is shown that the MSE is inversely proportional to the number of flood images, and is independent of the input fluence provided that the input fluence is above a minimal value that avoids biasing the estimate. The authors then propose to further lower the input fluence so that each event creates a point-spread function rather than a flood field. The authors use this finding as the foundation for a novel algorithm in which the characteristic MTF(f), NPS(f), and DQE(f) curves are simultaneously generated from the results of a single run. The authors also investigate lowering the number of optical photons used in a scintillator simulation to further increase efficiency. Simulation results are compared with measurements performed on a Varian AS1000 portal imager, and with a previously published simulation performed using clinical fluence levels.Results: On the order of only 10-100 gamma photons per flood image were required to be detected to avoid biasing the NPS estimate. This allowed for a factor of 10(7) reduction in fluence compared to clinical levels with no loss of accuracy. An optimal signal-to-noise ratio (SNR) was achieved by increasing the number of flood images from a typical value of 100 up to 500, thereby illustrating the importance of flood image quantity over the number of gammas per flood. For the point-spread ensemble technique, an additional 2x reduction in the number of incident gammas was realized. As a result, when modeling gamma transport in a thick pixelated array, the simulation time was reduced from 2.5 x 10(6) CPU min if using clinical fluence levels to 3.1 CPU min if using optimized fluence levels while also producing a higher SNR. The AS1000 DQE(f) simulation entailing both optical and radiative transport matched experimental results to within 11%, and required 14.5 min to complete on a single CPU.Conclusions: The authors demonstrate the feasibility of accurately modeling x-ray detector DQE(f) with completion times on the order of several minutes using a single CPU. Convenience of simulation can be achieved using GEANT4 which offers both gamma and optical photon transport capabilities. (c) 2014 American Association of Physicists in Medicine.