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Deformable registration for image-guided radiotherapy

图像引导放射治疗的变形配准

基本信息

批准号：
7499634
负责人：
Ron Alterovitz
金额：
$ 1.72万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
依托单位国家：
美国
项目类别：
财政年份：
2007
资助国家：
美国
起止时间：
2007-08-29 至 2008-12-31
项目状态：
已结题

项目摘要

DESCRIPTION (provided by applicant): The objective of this project is to improve the accuracy and precision of dose targeting in image-guided radiation treatment by accurately and automatically registering deformable tissues in diagnostic and treatment planning images. Recent advances in medical imaging and radiation therapy have the potential to improve patient care by noninvasively identifying the location and extent of cancer and by allowing physicians to escalate and target radiation dose to cancerous lesions while sparing surrounding healthy tissues. To compensate for significant changes that occur between diagnostic and treatment phases due to imaging requirements such as endorectal probes, patient position differences, weight change, and other factors, a new computational tool for deformable image registration will be developed and validated. Given a segmented reference image and an image obtained during treatment, the algorithm will generate a 3D representation of the tissues, estimate tissue displacements and deformations using a finite element model, account for uncertainty due to unknown model parameters, and output a mapping between the images. Low computation time is critical for clinical viability. The new method will be applied to register prostate CT or MRI/MRSI reference images to treatment images obtained using Megavoltage Cone-Beam GT (MV CBCT), a 3D imaging modality being developed to image the patient on the treatment table for Intensity-Modulated Radiation Therapy (IMRT). Validation of the method will be performed using bone outlines and by measuring registration errors for marker seeds that have been implanted inside the prostate in a subset of patients. The new software tool, targeted at prostate cancer treatment, aims to significantly improve patient care by enabling clinicians to improve conformality of dose to tissue types during radiation therapy. These improvements could significantly enhance public health by lowering recovery times, recurrence rates, and treatment costs for cancer patients.

描述（由申请人提供）：该项目的目标是通过在诊断和治疗计划图像中准确、自动地配准变形组织来提高图像引导放射治疗中剂量靶向的准确性和精确度。医学成像和放射治疗的最新进展有可能通过无创地识别癌症的位置和范围，以及允许医生在不伤害周围健康组织的情况下逐步增加和瞄准癌性病变的放射剂量，从而改善患者护理。为了补偿由于直肠内探头、患者位置差异、体重变化和其他因素等成像要求而在诊断和治疗阶段之间发生的显着变化，将开发和验证用于可变形图像配准的新计算工具。给定分割的参考图像和治疗期间获得的图像，该算法将生成组织的 3D 表示，使用有限元模型估计组织位移和变形，考虑由于未知模型参数导致的不确定性，并输出图像之间的映射。低计算时间对于临床可行性至关重要。新方法将用于将前列腺 CT 或 MRI/MRSI 参考图像与使用兆伏锥束 GT (MV CBCT) 获得的治疗图像进行配准，MV CBCT 是一种正在开发的 3D 成像模式，用于对调强放射治疗 (IMRT) 治疗台上的患者进行成像。该方法的验证将使用骨骼轮廓并通过测量已植入一部分患者前列腺内的标记种子的配准误差来进行。该新软件工具针对前列腺癌治疗，旨在通过使临床医生能够改善放射治疗期间剂量与组织类型的适形性来显着改善患者护理。这些改进可以通过降低癌症患者的康复时间、复发率和治疗成本来显着增强公众健康。