MRI-Based Radiation Therapy Treatment Planning

基于 MRI 的放射治疗治疗计划

• 批准号: 9197624
• 负责人: Ruijiang Li
• 金额: $ 35.94万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-02-01 至 2021-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9197624
• 关键词: Adopted; Adoption; Anatomy; Atlases; Bayesian Method; Bayesian Modeling; Brain; Calibration; Clinical; Data; Development; Diagnosis; Diffusion Magnetic Resonance Imaging; Disease; Dose; Evaluation; Functional Imaging; Geometry; Goals; Gold; Head and neck structure; Image; Image Analysis; Machine Learning; Magnetic Resonance Imaging; Malignant Neoplasms; Maps; Methods; Modality; Modeling; Modification; Organ; Patients; Perfusion; Photons; Positron-Emission Tomography; Predisposition; Probability; Procedures; Process; Prostate; Protons; Radiation; Radiation exposure; Radiation therapy; Site; Source; Staging; System; Techniques; anatomic imaging; attenuation; base; computerized tools; cost; density; electron density; image guided; imaging biomarker; imaging modality; improved; magnetic field; multimodality; novel; public health relevance; quality assurance; reconstruction; spectroscopic imaging; success; tool; treatment planning; treatment response

 DESCRIPTION (provided by applicant): CT is currently the gold standard in radiation therapy treatment planning. MRI provides a number of advantages over CT, including improved accuracy of target delineation, reduced radiation exposure, and simplified clinical workflow. There are two major technical hurdles that are impeding the clinical adoption of MRI-based radiation treatment planning: (1) geometric distortion, and (2) lack of electron density information. The goal of this project is to develop novel image analysis and computational tools to enable MRI-based radiation treatment planning. We hypothesize that accurate patient geometry and electron density information can be derived from MRI if the appropriate MR image acquisition, reconstruction, and analysis methods are applied. In Aim 1, we will improve the geometric accuracy of MRI by minimizing system-level and patient- specific distortions. To maintain sufficient system-level accuracy, we will perform comprehensive machine- specific calibrations and ongoing quality assurance procedures. To correct patient-induced distortions, we will develop novel computational tools to derive a detailed magnetic field distortion map based on physical principles, which is used to correct susceptibility-induced spatial distortions. In Aim 2, we will develop a unifying Bayesian method for quantitative electron density mapping, by combining the complementary intensity and geometry information. By utilizing multiple patient atlases and panoramic, multi-parametric MRI with differential contrast, we will apply machine learning techniques to encode the information given by intensity and geometry into two conditional probability density functions. These will be combined into one unifying posterior probability density function, which provides the optimal electron density on a continuous scale. In Aim 3, we will clinically evaluate the geometric and dosimetric accuracy of MRI for treatment planning in terms of 3 primary end points: (1) organ contours, (2) patient setup based on reference images, and (3) 3D dose distributions (both photon and proton), using CT as the ground truth. These evaluations will be conducted through patient studies at multiple disease sites, including brain, head and neck, and prostate. Success of the project will afford distortion-free MRI with reliable, quantitative electron density information. This will pave the way for MRI-based radiation treatment planning, leading to an improved accuracy in the overall radiation therapy process. It will streamline the treatment workflow for the MRI-guided radiation delivery systems under active development. With minimal modification, the proposed techniques can be applied to MR-based PET attenuation correction in PET/MR imaging. More broadly, the unifying Bayesian formalism can be used to improve current imaging biomarkers by integrating a wide variety of disparate information including anatomical and functional imaging such as perfusion/diffusion-weighted imaging and MR spectroscopic imaging. It will facilitate the incorporation of multimodality MRI into the entire process of cancer management: diagnosis, staging, radiation treatment planning, and treatment response assessment.

 描述（由申请人提供）：CT 目前是放射治疗计划的黄金标准。 MRI 与 CT 相比具有许多优点，包括提高靶区描绘的准确性、减少辐射暴露和简化临床工作流程。阻碍基于 MRI 的放射治疗计划的临床采用的因素包括：(1) 几何失真，以及 (2) 缺乏电子密度信息。该项目的目标是开发新型图像分析和计算工具，以实现基于 MRI 的放射。治疗计划。如果应用适当的 MR 图像采集、重建和分析方法，我们可以从 MRI 中获得准确的患者几何形状和电子密度信息。在目标 1 中，我们将通过最大限度地减少系统级和患者特定性来提高 MRI 的几何精度。为了保持足够的系统级精度，我们将执行全面的机器特定校准和持续的质量保证程序，为了纠正患者引起的失真，我们将开发新颖的计算工具，以根据物理原理得出详细的磁场失真图，这是用来纠正在目标 2 中，我们将通过结合互补的强度和几何信息，开发一种用于定量电子密度图的统一贝叶斯方法。应用机器学习技术将强度和几何给出的信息编码为两个条件概率密度函数，这些函数将组合成一个统一的后验概率密度函数，在目标 3 中提供连续尺度上的最佳电子密度。根据 3 个主要终点临床评估 MRI 用于治疗计划的几何和剂量精度：(1) 器官轮廓，(2) 基于参考图像的患者设置，以及 (3) 3D 剂量分布（光子和质子），这些评估将通过对多个疾病部位（包括大脑、头部和颈部以及前列腺）的患者研究进行。该项目的成功将提供可靠的定量电子密度信息。铺平道路基于 MRI 的放射治疗计划，可提高整个放射治疗过程的准确性，它将简化正在积极开发的 MRI 引导放射治疗系统的治疗工作流程，只需进行最少的修改，即可将所提出的技术应用于 MR-。更广泛地说，统一的贝叶斯形式可以通过整合各种不同的信息（包括解剖和功能成像，例如灌注/扩散加权成像和）来改善当前的成像生物标志物。磁共振波谱成像将有助于将多模态 MRI 纳入癌症管理的整个过程：诊断、分期、放射治疗计划和治疗反应评估。