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MRI-Based Radiation Therapy Treatment Planning

基于 MRI 的放射治疗治疗计划

基本信息

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
项目类别：
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

项目摘要

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.