Reconstruction of four-dimensional computed tomography imagesduring treatment time using electronic portal imaging device images based on a dynamic 2D/3D registration
Reconstruction of four-dimensional computed tomography imagesduring treatment time using electronic portal imaging device images based on a dynamic 2D/3D registration
复制标题
使用基于动态 2D/3D 配准的电子射野成像设备图像在治疗期间重建四维计算机断层扫描图像
DOI：
10.1117/12.2254000
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Sasaki T
中科院分区：
文献类型：
--
作者：
Nakamoto T;Arimura H;Hirose TA;Ohga S;Umezu Y;Nakamura Y;Honda H;Sasaki T
本研究的目的是开发一个计算框架，用于在治疗期间使用基于动态2D/3D配准的电子门静脉成像设备（EPID）图像重建四维计算机断层扫描（4D-CT）图像。治疗期间的4D-CT图像（“治疗”4D-CT图像）通过基于仿射变换的动态临床门静脉剂量图像（PDIs）与规划CT图像之间的动态2D/3D配准进行重建，该动态临床门静脉剂量图像来自EPID图像，通过规划所有帧的PDIs。采用Levenberg-Marquardt （LM）算法对仿射变换矩阵的元素（变换参数）进行优化，使规划的pdi与所有帧的动态临床pdi相似。为了在LM算法中找到最优的变换参数，需要确定每一帧的初始变换参数。本研究采用一帧内的最优变换参数作为初始变换参数，对连续帧内的变换参数进行优化。计算Gamma通过率（3mm /3%），用于评估所有帧的动态临床pdi和“治疗”pdi之间剂量分布的相似性，该pdi是根据“治疗”4D-CT图像计算的。该框架应用于8名接受立体定向全身放射治疗（SBRT）的肺癌患者。8例患者的动态临床pdi与“治疗”pdi的平均伽马通过率为98.3±1.2%。总之，所提出的框架使得在治疗期间动态监测患者的运动成为可能。
The goal of our study was to develop a computational framework for reconstruction of four-dimensional computed tomography (4D-CT) images during treatment time using electronic portal imaging device (EPID) images based on a dynamic 2D/3D registration. The 4D-CT images during treatment time (“treatment” 4D-CT images) were reconstructed by performing an affine transformation-based dynamic 2D/3D registration between dynamic clinical portal dose images (PDIs) derived from the EPID images with planning CT images through planning PDIs for all frames. Elements of the affine transformation matrices (transformation parameters) were optimized using a Levenberg-Marquardt (LM) algorithm so that the planning PDIs could be similar to the dynamic clinical PDIs for all frames. Initial transformation parameters in each frame should be determined for finding optimum transformation parameters in the LM algorithm. In this study, the optimum transformation parameters in a frame employed as the initial transformation parameters for optimizing the transformation parameter in the consecutive frame. Gamma pass rates (3 mm/3%) were calculated for evaluating a similarity of the dose distributions between the dynamic clinical PDIs and “treatment” PDIs, which were calculated from “treatment” 4D-CT images, for all frames. The framework was applied to eight lung cancer patients who were treated with stereotactic body radiation therapy (SBRT). A mean of the average gamma pass rates between the dynamic clinical PDIs and the “treatment” PDIs for all frames was 98.3±1.2% for eight cases. In conclusion, the proposed framework makes it possible to dynamically monitor patients’ movement during treatment time.