MRI-Based Radiation Therapy Treatment Planning

 描述（由申请人提供）：CT目前是放射治疗计划的金标准。MRI提供了许多优于CT的优点，包括提高靶勾画的准确性、减少辐射暴露和简化临床工作流程。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采用：（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，如果采用适当的MR图像采集、重建和分析方法，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，我们将通过最小化系统级和患者特定失真来提高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，我们将开发新的计算工具，以根据物理原理推导出详细的磁场失真图，用于纠正磁致伸缩引起的空间失真。在目标2中，我们将开发一个统一的贝叶斯方法定量电子密度映射，通过结合互补的强度和几何信息。通过利用多个患者图谱和具有差分对比度的全景多参数MRI，我们将应用机器学习技术将强度和几何形状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，它提供了连续尺度上的最佳电子密度。在目标3中，我们将根据3个主要终点临床评价MRI用于治疗计划的几何和剂量准确性：（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），使用CT作为基础事实。这些评价将通过多个疾病部位的患者研究进行，包括脑、头颈部和前列腺。该项目的成功将为无失真MRI提供可靠的定量电子密度信息。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MRI引导辐射输送系统的治疗工作流程。通过最小的修改，所提出的技术可以应用于PET/MR成像中基于MR的PET衰减校正。更广泛地说，统一贝叶斯形式主义可以用于通过整合各种各样的不同信息来改善当前的成像生物标志物，包括解剖和功能成像，例如灌注/扩散加权成像和MR光谱成像。它将有助于将多模态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。

 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.