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

本项目旨在开发一种创新的物理方法，以准确预测流定向放射治疗（SBRT）期间移动肺肿瘤的位置。该策略包括使用四维计算机断层扫描（4DCT）在模拟中校准肿瘤基础模型，并使用基于视频的光学表面成像（4DSI）提供运动适应，以推断治疗过程中的肿瘤位置。这个肿瘤运动模型解释了呼吸不规则和基线漂移，这是任何现有的基于相关性的运动替代品都无法处理的。因此，所提出的方法有可能为运动跟踪SBRT提供一种准确、可靠和非放射性的监测运动肿瘤的方法。在此之前，李博士发现了体容积变化与动态潮汐量和平均隔膜运动的物理关系。他与Wei博士合作了1.5年，并发表了2篇关于肺运动表征和呼吸周期性的论文，这得益于Wei博士在计算机视觉和机器学习方面的专业知识。初步数据已经生成，包括4DSI在体积测量中的准确性评估，肿瘤运动扰动模型的框架，以及肺通气和运动的表征。因此，我们假设这种基于物理定律的方法将提供对肿瘤位置的准确估计，从而有可能改善癌症的放射治疗。特别地，我们建议同时使用4DCT和4DSI建立肿瘤运动摄动模型。我们将描述移动肿瘤与支气管树、胸壁、横膈膜和躯干表面之间的物理关系（见具体目标1）。我们将建立一个运动基础模型，应用4DSI进行时空输入，最后完成运动摄动模型。我们还建议建立基于4dsi的肺活量测定法，并通过志愿者和患者研究对所提出的方法进行临床验证（见具体目标2）。我们将建立一个IRB方案，用于同时使用4DSI和透视检查的患者研究，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。该试点项目将证明这种新方法可以在4D图像引导的SBRT中准确靶向肺部肿瘤。

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.