Purpose

As the spectrum of X-ray procedures has increased both for diagnostic and for interventional cases, more attention is paid to X-ray dose management. While the medical benefit to the patient outweighs the risk of radiation injuries in almost all cases, reproducible studies on organ dose values help to plan preventive measures helping both patient as well as staff. Dose studies are either carried out retrospectively, experimentally using anthropomorphic phantoms, or computationally. When performed experimentally, it is helpful to combine them with simulations validating the measurements. In this paper, we show how such a dose simulation method, carried out together with actual X-ray experiments, can be realized to obtain reliable organ dose values efficiently.

Methods

A Monte Carlo simulation technique was developed combining down-sampling and super-resolution techniques for accelerated processing accompanying X-ray dose measurements. The target volume is down-sampled using the statistical mode first. The estimated dose distribution is then up-sampled using guided filtering and the high-resolution target volume as guidance image. Second, we present a comparison of dose estimates calculated with our Monte Carlo code experimentally obtained values for an anthropomorphic phantom using metal oxide semiconductor field effect transistor dosimeters.

Results

We reconstructed high-resolution dose distributions from coarse ones (down-sampling factor 2 to 16) with error rates ranging from 1.62 % to 4.91 %. Using down-sampled target volumes further reduced the computation time by 30 % to 60 %. Comparison of measured results to simulated dose values demonstrated high agreement with an average percentage error of under \(10 \%\) for all measurement points.

Conclusions

Our results indicate that Monte Carlo methods can be accelerated hardware-independently and still yield reliable results. This facilitates empirical dose studies that make use of online Monte Carlo simulations to easily cross-validate dose estimates on-site.

中文翻译：

---

XDose：用于在线交叉验证实验和计算X射线剂量估计

目的

随着诊断和介入性病例的X射线检查范围的增加，X射线剂量管理得到了更多的关注。尽管在几乎所有情况下，对患者的医疗益处都超过了放射线伤害的风险，但有关器官剂量值的可重复研究有助于规划预防措施，从而对患者和医护人员都有帮助。剂量研究既可以使用拟人化模型进行回顾性，实验性研究，也可以通过计算进行。在进行实验时，将它们与验证测量结果的模拟结合起来会很有帮助。在本文中，我们展示了如何将这种剂量模拟方法与实际的X射线实验一起实施，以有效地获得可靠的器官剂量值。

方法

开发了一种结合了下采样和超分辨率技术的蒙特卡洛模拟技术，用于伴随X射线剂量测量的加速处理。首先使用统计模式对目标体积进行下采样。然后使用引导滤波和高分辨率目标体积作为引导图像对估计的剂量分布进行上采样。其次，我们将使用金属氧化物半导体场效应晶体管剂量计，通过蒙特卡罗代码通过实验获得的拟人化体模值计算出的剂量估算值进行比较。

结果

我们从粗略的（向下采样系数2到16）重构了高分辨率的剂量分布，错误率从1.62％到4.91％。使用下采样目标体积进一步将计算时间减少了30％至60％。测量结果与模拟剂量值的比较表明，所有测量点的一致性均很高，平均百分比误差低于\（10 \％\）。

结论

我们的结果表明，蒙特卡洛方法可以独立于硬件进行加速，并且仍然可以获得可靠的结果。这有助于进行经验剂量研究，该研究利用在线蒙特卡洛模拟轻松地在现场交叉验证剂量估计。