Center for Virtual Imaging Trials and Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine, Durham, North Carolina, USA.
Department of Physics, Duke University, Durham, North Carolina, USA.
Med Phys. 2022 Feb;49(2):891-900. doi: 10.1002/mp.15400. Epub 2021 Dec 22.
Estimation of organ doses in digital tomosynthesis (DT) is challenging due to the lack of existing tools that accurately and flexibly model protocol- and view-specific collimations and motion trajectories of the source and detector for a variety of exam protocols, and the computational inefficiencies of conducting MC simulations. The purpose of this study was to overcome these limitations by developing and benchmarking a GPU-accelerated MC simulation framework compatible with patient-specific computational phantoms for individualized estimation of organ doses in DT.
The framework for individualized estimation of dose in DT was developed as a two-step workflow: (1) a custom MATLAB code that accepts a patient-specific computational phantom and exam description (organ markers for defining the extremities of the anatomical region of interest, tube voltage, source-to-image distance, angular sweep range, number of projection views, and the pivot point to image distance - PPID) to compute the field of views (FOVs) for a clinical DT system, and (2) a MC tool (developed using MC-GPU) modeling the configuration of a clinical DT system to estimate organ doses based on the computed FOVs. Using this framework, we estimated organ doses for 28 radiosensitive organs in an adult reference patient model (M; 30 years) imaged using a commercial DT system (VolumeRad, GE Healthcare, Waukesha, WI). The estimates were benchmarked against values from a comparable organ dose estimation framework (reference dataset developed by the Advanced Laboratory for Radiation Dosimetry Studies at University of Florida) for a posterior-anterior chest exam. The resulting differences were quantified as percent relative errors and analyzed to identify any potential sources of bias and uncertainties. The timing performance (run duration in seconds) of the framework was also quantified for the same simulation to gauge the feasibility of the workflow for time-constrained clinical applications.
The organ dose estimates from the developed framework showed a close agreement with the reference dataset, with percent relative errors ranging from -6.9% to 5.0% and a mean absolute percent difference of 1.7% over all radiosensitive organs, with the exception of testes and eye lens, for which the percent relative errors were higher at -18.9% and -27.6%, respectively, due to their relative positioning outside the primary irradiation field, leading to fewer photons depositing energy and consequently higher errors in estimated organ doses. The run duration for the same simulation was 916.3 s, representing a substantial improvement in performance over existing nonparallelized MC tools.
This study successfully developed and benchmarked a GPU-accelerated framework compatible with patient-specific anthropomorphic computational phantoms for accurate individualized estimation of organ doses in DT. By enabling patient-specific estimation of organ doses, this framework can aid clinicians and researchers by providing them with tools essential for tracking the radiation burden to patients for dose monitoring purposes and identifying the trends and relationships in organ doses for a patient population to optimize existing and develop new exam protocols.
由于缺乏能够准确灵活地模拟各种检查协议中源和探测器的协议和视场特定准直以及运动轨迹的现有工具，并且 MC 模拟的计算效率低下，因此数字断层融合（DT）中的器官剂量估计具有挑战性。本研究的目的是通过开发和基准测试与患者特定计算体模兼容的 GPU 加速 MC 模拟框架来克服这些限制，以实现 DT 中器官剂量的个体化估计。
用于 DT 中个体化剂量估计的框架是作为两步工作流程开发的：（1）接受患者特定计算体模和检查描述（用于定义感兴趣解剖区域的末端的器官标记、管电压、源到图像距离、角扫描范围、投影视图数量以及枢轴点到图像距离 - PPID）的自定义 MATLAB 代码，以计算临床 DT 系统的视场（FOV），（2）基于计算的 FOV 模拟临床 DT 系统配置的 MC 工具（使用 MC-GPU 开发），以估计器官剂量。使用该框架，我们使用商业 DT 系统（GE Healthcare，Waukesha，WI 的 VolumeRad）对成年参考患者模型（M；30 岁）进行了 28 个敏感器官的剂量估计。将估计值与佛罗里达大学先进辐射剂量研究实验室开发的可比器官剂量估计框架（参考数据集）的数值进行了基准测试，用于前后胸检查。将产生的差异量化为相对百分比误差，并进行分析以确定任何潜在的偏差和不确定性来源。还对相同模拟的框架的时间性能（以秒为单位的运行时间）进行了量化，以评估工作流程在时间受限的临床应用中的可行性。
开发的框架的器官剂量估计与参考数据集非常吻合，所有敏感器官的相对百分比误差在-6.9%至 5.0%之间，平均绝对百分比差异为 1.7%，除了睾丸和晶状体，其相对百分比误差分别为-18.9%和-27.6%，这是由于它们位于初级照射场之外的相对位置，导致沉积能量的光子较少，因此估计器官剂量的误差更高。对于相同的模拟，运行时间为 916.3 秒，与现有的非并行 MC 工具相比，性能有了显著提高。
本研究成功开发并基准测试了与患者特定的人体解剖计算体模兼容的 GPU 加速框架，用于在 DT 中准确进行个体化器官剂量估计。通过实现器官剂量的个体化估计，该框架可以为临床医生和研究人员提供必要的工具，帮助他们跟踪患者的辐射负担，以进行剂量监测，并识别患者群体中器官剂量的趋势和关系，以优化现有检查协议并开发新的检查协议。
