MRI-Based Radiation Therapy Treatment Planning

基于 MRI 的放射治疗治疗计划

• 批准号: 9026075
• 负责人: Ruijiang Li
• 金额: $ 36.21万
• 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/9026075
• 关键词: Adopted; Adoption; 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 therapy; Site; Source; Staging; System; Techniques; 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(申请人提供)：CT是目前放射治疗计划的黄金标准。与CT相比，MRI提供了许多优势，包括提高了靶区描绘的准确性，减少了辐射暴露，简化了临床工作流程。有两个主要的技术障碍阻碍了基于MRI的放射治疗计划的临床应用：(1)几何失真，(2)缺乏电子密度信息。该项目的目标是开发新的图像分析和计算工具，以实现基于MRI的放射治疗计划。我们假设，如果应用适当的MR图像采集、重建和分析方法，可以从MRI获得准确的患者几何和电子密度信息。在目标1中，我们将通过最小化系统级和患者特定的失真来提高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专用校准和持续的质量保证程序。为了纠正患者引起的扭曲，我们将开发新的计算工具，根据物理原理得出详细的磁场扭曲图，用于纠正磁化率引起的空间扭曲。在目标2中，我们将发展一种统一的贝叶斯方法，通过结合互补强度和几何信息来定量绘制电子密度图。通过利用多个患者图谱和具有差异对比的全景、多参数MRI，我们将应用机器学习技术将由强度和几何给出的信息编码为两个条件概率密度函数。这些将被组合成一个统一的后验概率密度函数，它在连续的尺度上提供最佳的电子密度。在目标3中，我们将从三个主要终点对MRI用于治疗计划的几何和剂量学准确性进行临床评估：(1)器官轮廓，(2)基于参考图像的患者设置，(3)以CT为基本事实的三维剂量分布(光子和质子)。这些评估将通过对多个疾病部位的患者研究进行，包括大脑、头颈部和前列腺。该项目的成功将为无失真磁共振成像提供可靠的、定量的电子密度信息。这将为基于MRI的放射治疗计划铺平道路，从而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核磁共振引导放射传递系统的治疗工作流程。该方法只需最小的修改，即可应用于PET/MR成像中基于MR的PET衰减校正。更广泛地说，统一的贝叶斯公式可以通过集成各种不同的信息来改进当前的成像生物标记物，包括解剖和功能成像，如灌注/扩散加权成像和磁共振光谱成像。它将促进将多模式核磁共振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。