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Inter-Fraction Variations in Motion Modeling using Patient 4D-Cone Beam CT Images

机译:使用患者4D锥梁CT图像的运动建模帧间分型

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The purpose of this paper is to quantify the variability of patient-specific motion models derived from 4D cone beam CT (4DCBCT) images captured at different radiation therapy treatment fractions. Motion models are derived from 4DCBCT images by 1) applying deformable image registration on each 4DCBCT image with respect to a reference image, resulting in a set of displacement vector fields (DVFs), and 2) performing dimensionality reduction, principal component analysis (PCA) in this work, on the DVFs to derive a motion model. PCA-based motion models are composed of a few (up to five) eigenvectors associated with eigenvalues sorted in descending order. To quantify inter-fraction variability in motion models, the motion model from the first treatment fraction is compared to motion models from each successive treatment fraction. The eigenvectors are compared using two criteria; the absolute variation and the directional similarity. Three clinical patient treatments were obtained retrospectively and used in this study. Results showed that the first two eigenvectors have smaller absolute variation (1.8 × 10~(-4)) and larger directional similarity (0.47) than the last three eigenvectors (absolute variation: 2.5 ×10~(-4), directional similarity: 0.04). The study showed that motion models derived from different treatment fractions have variations but the eigenvectors associated with the greatest eigenvalues were shown to be more stable across treatment fractions than others. Further studies are planned to determine whether the inter-fraction variations may lead to significant changes in motion reconstruction.
机译:本文的目的是量化在不同放射治疗处理分数捕获的4D锥形光束CT(4DCBCT)图像的患者特定运动模型的可变性。运动模型从4dcbct图像导出1)在每个4dcbct图像上施加可变形的图像配准相对于参考图像,导致一组位移矢量字段(DVFs)和2)执行维度降低,主成分分析(PCA)在这项工作中,在DVF上导出运动模型。基于PCA的运动模型由与下降顺序排序的特征值相关的少数(最多五个)的特征向量组成。为了量化运动模型中的帧间分型变异,将来自第一处理级分的运动模型与来自每个连续治疗级分的运动模型进行比较。使用两个标准进行比较特征向量;绝对变化和方向相似度。回顾性并在本研究中获得了三种临床患者治疗方法。结果表明,前两个特征向量具有较小的绝对变化(1.8×10〜(-4))和比最后三个特征向量(0.47)更大的定向相似度(绝对变化:2.5×10〜(-4),方向相似度:0.04 )。该研究表明,来自不同治疗级分的运动模型具有变化,但与最大特征值相关的特征向量被证明横跨治疗部分比其它更稳定。计划进一步研究以确定间隔帧间变化是否可能导致运动重建的显着变化。

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