Developing an Accurate and Reliable Method for Tumor Motion Monitoring for Tumor

开发一种准确可靠的肿瘤运动监测方法

• 批准号: 8764998
• 负责人: Guang Li
• 金额: $ 13.26万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-26 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8764998
• 关键词: 4D Imaging; Accounting; Breathing; Bronchial Tree; Chest wall structure; Community Outreach; Computer Vision Systems; Data; Education and Outreach; Environmental air flow; Fluoroscopy; Four-dimensional; Image; Institutional Review Boards; Laws; Location; Lung; Lung Neoplasms; Machine Learning; Measurement; Memorial Sloan-Kettering Cancer Center; Methods; Modeling; Monitor; Motion; Optics; Paper; Patients; Periodicity; Pilot Projects; Positioning Attribute; Protocols documentation; Publishing; Radiation therapy; Research Training; Respiration; Respiratory Diaphragm; Spirometry; Surface; Tidal Volume; X-Ray Computed Tomography; anticancer research; base; cancer radiation therapy; improved; innovation; novel strategies; simulation; tumor; volunteer

This project aims to develop an innovative physical approach to accurately predict the location of a moving lung tumor during streotactic body radiotherapy (SBRT). The strategy involves using four-dimensional computed tomography (4DCT) to calibrate a tumor base-model at simulation and using video-based optical surface imaging (4DSI) to provide motion adaptation to infer tumor position during treatment. This tumor motion model accounts for breathing irregularities and baseline drift, which cannot be handled by any existing correlation-based motion surrogate. Therefore, the proposed method potentially provides an accurate, reliable, and non-radiological means of monitoring a moving tumor for motion-tracking SBRT. Previously, Dr. Li discovered the physical relationships of torso-volume change with dynamic tidal volume and mean diaphragm motion. He has collaborated with Dr. Wei for 1.5 years and published 2 papers on lung motion characterization and breathing periodicity, facilitated by Dr. Wei's expertise in computer vision and machine learning. Preliminary data have been generated, including accuracy assessment of 4DSI in volume measurement, framework for a tumor motion perturbation model, and characterization of lung ventilation and motion. We therefore hypothesize that this physical law-based method will provide an accurate estimation of tumor location, thereby potentially improving radiation therapy for cancer. In particular, we propose to establish a tumor motion perturbation model using both 4DCT and 4DSI. We will characterize the physical relationship between a moving tumor and the bronchial tree, chest wall, diaphragm, and torso surface (see Specific Aim 1). We will build a motion base model, apply 4DSI for spatial and temporal inputs, and finalize the motion perturbation model. We also propose to establish 4DSI-based spirometry and clinically validate the proposed method, through both volunteer and patient studies (see Specific Aim 2). We will establish an IRB protocol for patient studies using concurrent 4DSI and fluoroscopy to validate the tumor motion model. We will also study volunteer respiration using 4DMR and spirometry. This pilot project will demonstrate the proof of principle that this novel approach can accurately target lung tumors in 4D image-guided SBRT.

该项目旨在开发一种创新的物理方法，以准确预测链球菌放射疗法（SBRT）期间移动的肺肿瘤的位置。该策略涉及使用四维计算机断层扫描（4DCT）在模拟时校准肿瘤碱模型，并使用基于视频的光学表面成像（4DSI）提供运动适应以推断治疗期间的肿瘤位置。该肿瘤运动模型解释了呼吸不规则和基线漂移，这无法通过任何基于相关的运动替代物来处理。因此，所提出的方法有可能提供一种准确，可靠和非放射学的方法，用于监测运动跟踪SBRT的移动肿瘤。以前，Li博士发现了躯干体积变化的物理关系，并通过动态潮汐体积和平均膜片运动。他已经与WEI博士合作了1。5年，并发表了2篇有关肺运动表征和呼吸周期性的论文，这是WEI博士在计算机视觉和机器学习方面的专业知识促进的。已经生成了初步数据，包括在体积测量中对4DSI的准确评估，肿瘤运动扰动模型的框架以及肺通风和运动的表征。因此，我们假设这种基于物理定律的方法将对肿瘤位置进行准确的估计，从而有可能改善癌症的放射治疗。特别是，我们建议使用4DCT和4DSI建立肿瘤运动扰动模型。我们将表征移动肿瘤与支气管树，胸壁，隔膜和躯干表面之间的物理关系（请参阅特定目标1）。我们将构建一个运动基础模型，将4DSI应用于空间和时间输入，并最终确定运动扰动模型。我们还建议通过志愿者和患者研究来建立基于4DSI的肺活量测定法，并在临床上验证所提出的方法（请参阅特定目标2）。我们将使用并发4DSI和荧光镜检查建立IRB方案，以验证肿瘤运动模型。我们还将使用4DMR和肺活量测定法研究志愿者呼吸。该试点项目将证明原理的证据，即这种新型方法可以准确靶向4D图像引导的SBRT中的肺肿瘤。