Markerless Four-Dimensional-Cone Beam Computed Tomography Projection-Phase Sorting Using Prior Knowledge and Patient Motion Modeling: A Feasibility Study

Lei Zhang, Yawei Zhang, You Zhang, W. Harris, F. Yin, Jing Cai, L. Ren
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引用次数: 2

Abstract

Aim: During cancer radiotherapy treatment, on-board four-dimensional-cone beam computed tomography (4D-CBCT) provides important patient 4D volumetric information for tumor target verification. Reconstruction of 4D-CBCT images requires sorting of acquired projections into different respiratory phases. Traditional phase sorting methods are either based on external surrogates, which might miscorrelate with internal structures; or on 2D internal structures, which require specific organ presence or slow gantry rotations. The aim of this study is to investigate the feasibility of a 3D motion modeling-based method for markerless 4D-CBCT projection-phase sorting. Methods: Patient 4D-CT images acquired during simulation are used as prior images. Principal component analysis (PCA) is used to extract three major respiratory deformation patterns. On-board patient image volume is considered as a deformation of the prior CT at the end-expiration phase. Coefficients of the principal deformation patterns are solved for each on-board projection by matching it with the digitally reconstructed radiograph (DRR) of the deformed prior CT. The primary PCA coefficients are used for the projection-phase sorting. Results: PCA coefficients solved in nine digital phantoms (XCATs) showed the same pattern as the breathing motions in both the anteroposterior and superoinferior directions. The mean phase sorting differences were below 2% and percentages of phase difference < 10% were 100% for all the nine XCAT phantoms. Five lung cancer patient results showed mean phase difference ranging from 1.62% to 2.23%. The percentage of projections within 10% phase difference ranged from 98.4% to 100% and those within 5% phase difference ranged from 88.9% to 99.8%. Conclusion: The study demonstrated the feasibility of using PCA coefficients for 4D-CBCT projection-phase sorting. High sorting accuracy in both digital phantoms and patient cases was achieved. This method provides an accurate and robust tool for automatic 4D-CBCT projection sorting using 3D motion modeling without the need of external surrogate or internal markers.
基于先验知识和患者运动建模的无标记四维锥束计算机断层扫描投影相位排序的可行性研究
目的:在肿瘤放疗过程中,车载四维锥束计算机断层扫描(4D- cbct)为肿瘤靶标验证提供了重要的患者四维体积信息。重建4D-CBCT图像需要将获得的投影分类到不同的呼吸期。传统的相位分选方法要么是基于可能与内部结构不相关的外部替代物;或二维内部结构,这需要特定的器官存在或缓慢的龙门旋转。本研究的目的是探讨一种基于三维运动建模的无标记4D-CBCT投影相位排序方法的可行性。方法:将模拟过程中获取的患者4D-CT图像作为先验图像。主成分分析(PCA)用于提取三种主要的呼吸变形模式。机载患者图像体积被认为是先前CT在终末阶段的变形。通过与变形前CT的数字重建x线照片(DRR)匹配,求解每个机载投影的主变形模式系数。主PCA系数用于投影阶段排序。结果:9个数字幻影(XCATs)的PCA系数解算结果与呼吸运动在正反方向和上下方向呈现相同的模式。9个XCAT幻影的平均相位分选差均小于2%,相位差< 10%的百分比为100%。5例肺癌患者的平均相位差为1.62% ~ 2.23%。相位差在10%以内的预测比例为98.4% ~ 100%,相位差在5%以内的预测比例为88.9% ~ 99.8%。结论:本研究验证了PCA系数用于4D-CBCT投影相位分选的可行性。在数字幻影和患者病例中都达到了很高的分类精度。该方法在不需要外部替代或内部标记的情况下,为3D运动建模的4D-CBCT投影自动分类提供了一种准确、稳健的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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