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Statistical modeling of interfractional tissue deformation and its application in radiation therapy planning.

机译:间质组织变形的统计模型及其在放射治疗计划中的应用。

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摘要

In radiation therapy, interfraction organ motion introduces a level of geometric uncertainty into the planning process. Plans, which are typically based upon a single instance of anatomy, must be robust against daily anatomical variations. For this problem, a model of the magnitude, direction, and likelihood of deformation is useful. In this thesis, principal component analysis (PCA) is used to statistically model the 3D organ motion for 19 prostate cancer patients, each with 8-13 fractional computed tomography (CT) images. Deformable image registration and the resultant displacement vector fields (DVFs) are used to quantify the interfraction systematic and random motion. By applying the PCA technique to the random DVFs, principal modes of random tissue deformation were determined for each patient, and a method for sampling synthetic random DVFs was developed.;The PCA model was then extended to describe the principal modes of systematic and random organ motion for the population of patients. A leave-one-out study tested both the systematic and random motion model's ability to represent PCA training set DVFs. The random and systematic DVF PCA models allowed the reconstruction of these data with absolute mean errors between 0.5-0.9 mm and 1-2 mm, respectively. To the best of the author's knowledge, this study is the first successful effort to build a fully 3D statistical PCA model of systematic tissue deformation in a population of patients.;By sampling synthetic systematic and random errors, organ occupancy maps were created for bony and prostate-centroid patient setup processes. By thresholding these maps, PCA-based planning target volume (PTV) was created and tested against conventional margin recipes (van Herk for bony alignment and 5 mm fixed [3 mm posterior] margin for centroid alignment) in a virtual clinical trial for low-risk prostate cancer. Deformably accumulated delivered dose served as a surrogate for clinical outcome. For the bony landmark setup subtrial, the PCA PTV significantly (p<0.05) reduced D30, D20, and D5 to bladder and D50 to rectum, while increasing rectal D20 and D5. For the centroid-aligned setup, the PCA PTV significantly reduced all bladder DVH metrics and trended to lower rectal toxicity metrics. All PTVs covered the prostate with the prescription dose.
机译:在放射治疗中,介入器官的运动会在规划过程中引入一定程度的几何不确定性。通常基于单个解剖实例的计划必须对日常的解剖变化具有鲁棒性。对于此问题,使用大小,方向和变形可能性的模型是有用的。在本文中,主要成分分析(PCA)用于对19位前列腺癌患者的3D器官运动进行统计学建模,每位患者均使用8-13分数阶计算机断层扫描(CT)图像。可变形的图像配准和合成的位移矢量场(DVF)用于量化干涉系统和随机运动。通过将PCA技术应用于随机DVF,确定了每个患者的随机组织变形的主要模式,并开发了一种采样合成随机DVF的方法。;然后扩展了PCA模型以描述系统性器官和随机器官的主要模式为患者群体运动。一劳永逸的研究测试了系统运动模型和随机运动模型代表PCA训练集DVF的能力。随机的和系统的DVF PCA模型允许以绝对平均误差分别在0.5-0.9 mm和1-2 mm之间重建这些数据。就作者所知,该研究是建立完整的3D统计PCA模型的系统化方法,用于在一组患者中进行系统性组织变形。这是首次成功的尝试。通过对综合性系统误差和随机误差进行抽样,创建了骨骼和器官的器官占用图。前列腺质心患者设置过程。通过对这些图进行阈值处理,可以创建基于PCA的计划目标体积(PTV),并针对常规边缘裕度配方(van Herk进行骨对齐和5 mm固定[3 mm后缘]进行质心对齐)进行测试,以进行低水平的虚拟临床试验患前列腺癌。变形累积的输送剂量可作为临床结果的替代指标。对于骨标志性设置亚试验,PCA PTV显着(p <0.05)减少了膀胱的D30,D20和D5,直肠的D50,同时增加了直肠D20和D5。对于重心对齐设置,PCA PTV显着降低了所有膀胱DVH指标,并倾向于降低直肠毒性指标。所有PTV均以处方剂量覆盖前列腺。

著录项

  • 作者

    Vile, Douglas J.;

  • 作者单位

    Virginia Commonwealth University.;

  • 授予单位 Virginia Commonwealth University.;
  • 学科 Medicine.;Physics.
  • 学位 Ph.D.
  • 年度 2015
  • 页码 147 p.
  • 总页数 147
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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