To quantify deformable image registration errors in IGRT

描述(申请人提供)：这项研究的目的是开发一种新的和系统的方法来识别图像引导放射治疗中的可变形图像配准(DIR)错误和相关的剂量学后果。DIR和剂量重建过程的准确性是决定提供给患者的剂量是否与计划的剂量分布一致的关键。随着我们开始减少计划范围，基于日常成像所提供的保证，以及引入实时靶向设备(例如电磁信标)，肿瘤(和周围器官)变形的影响可能成为精确靶向肿瘤的限制因素。在这种情况下，无论是在线还是离线应用自适应校正，图像配准和剂量重建过程的准确性在评估向肿瘤和周围健康组织传递的实际剂量时变得至关重要。这项应用的长期目标是确保每个患者的治疗计划得到适当调整，以应对DIR移位和与剂量相关的错误，提高靶向准确性，并为健康组织提供最佳的保护。为了实现这一建议的目标，我们将：(1a)开发一种新的基于弹性的模型，基于不平衡力和能量的概念，量化可变形图像配准中的位移矢量场(DVF)误差，并将结果与可变形体模中的测量结果进行验证；(1b)将该方法应用于以前使用每日CBCT成像治疗的大量前列腺癌和肺癌患者的图像数据集；(2)使用三线性剂量内插和基于蒙特卡洛的能量映射进行剂量重建，并在患者图像数据集上量化由此产生的剂量学误差；(3)开发补偿基于DVF的位移误差引起的剂量误差的方法；(3a)开发剂量重建系统，使用优化过程，通过结合基于量化的DVF的剂量误差的反馈，将剂量误差降至最低；(3b)将剂量学误差量化为计划裕度的函数，并根据回顾治疗的一大组前列腺癌和肺癌患者的每日锥束CT(CBCT)图像和模拟CT图像的登记，制定用于解释DVF相关剂量误差的边际配方。

DESCRIPTION (provided by applicant): The purpose of this study is to develop a novel and systematic method to identify deformable image registration (DIR) errors and related dosimetric consequences in image-guided radiotherapy. The accuracy of the DIR and dose reconstruction process is central to determining whether or not the dose delivered to the patient is in agreement with the planned dose distribution. As we begin to reduce planning margins, based on the assurance afforded by daily imaging, and the introduction of real-time targeting devices (e.g. electromagnetic beacons), the impact of tumor (and surrounding organ) deformation may become a limiting factor in accurate targeting of the tumor. Under such circumstances, and regardless of whether on-line or off- line, adaptive corrections are applied, the accuracy of the image registration and dose reconstruction process becomes critical in evaluating the actual dose delivered to the tumor and surrounding healthy tissues. The long-term objective of this application is to ensure that each patient's treatment plan is properly adapted to account for DIR displacement and dose-related errors, to improve targeting accuracy and provide optimal sparing of healthy tissues. To accomplish the goals of this proposal, we will: (1a) Develop a novel elasticity-based model, founded on the concepts of unbalanced forces and energies, to quantify displacement vector field (DVF) errors in deformable image registration, and verify the results against measurements in a deformable phantom; (1b) Apply the method to a large number image datasets of prostate and lung cancer patients previously treated using daily CBCT imaging; (2) Perform dose reconstruction using tri-linear dose interpolation and Monte Carlo-based energy mapping and quantify the resulting dosimetric errors on the patient image datasets; (3) Develop methods to compensate for dose errors from DVF-based displacement errors; (3a) Develop a dose reconstruction system using an optimization process to minimize errors in the dose by incorporating feedback based on the quantified DVF-based dose errors; (3b) Quantify the dosimetric errors as a function of planning margin and develop a margin recipe to account for DVF-related dose errors based on registrations of daily cone-beam CT (CBCT) images with simulation CT images for a large group of prostate and lung cancer patients treated retrospectively.