Imaging-Based Assessments of Response

基于影像的反应评估

• 批准号: 9065667
• 负责人: JAMES M BALTER
• 金额: $ 37.64万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/9065667
• 关键词: Anatomy; Area; Atlases; Back; Biological; Breathing; Clinic; Clinical; Conformal Radiotherapy; Data; Disease; Dose; Drug Kinetics; Elements; Face; Failure; Functional Imaging; Glioma; Goals; Hybrids; Image; Image Analysis; Individual; Instruction; Magnetic Resonance Imaging; Maps; Measures; Metabolic; Methionine; Methodology; Methods; Modeling; Modification; Motion; Movement; Organ; Outcome; Patients; Pattern; Pattern Recognition; Physiological; Positron-Emission Tomography; Predictive Value; Radiation; Radiation therapy; Reproducibility; Research; Risk; Role; Sampling; Scanning; Signal Transduction; Site; Techniques; Testing; Time; Toxic effect; Uncertainty; X-Ray Computed Tomography; base; cancer imaging; image reconstruction; image registration; imaging modality; improved; individual patient; individualized medicine; outcome forecast; predicting response; programs; radiation-induced injury; reconstruction; response; single photon emission computed tomography; success; temporal measurement; tool; treatment planning; treatment response; tumor; tumor heterogeneity

PROJECT SUMMARY (See instructions): Image-based analyses as a means of modifying Radiation Therapy to customize treatment based on an individual patient's prognosis or early response to treatment is a research area crucial to the success of this program project. Project 3 investigates the hypothesis that, given the underlying heterogeneity of tumors and normal organs and their response to treatment, anatomic, metabolic and physiologic images (as acquired in the clinical projects of this program) are best utilized for individualizing therapy by robustly identifying subvolumes that are more predictive than individual voxels or the entire tumor/normal organ as a w/hole, and providing these subvolumes as a spatial guide for radiation dose redistributions such as focal boosting. Aim 1 will improve reconstruction of physiological images. Aim 2 is to investigate methods of Identifying subvolumes predictive of response. Aim 3 will improve the accuracy of mapping subvolumes to patients to guide modification of radiation therapy dose distributions. Pattern recognition techniques will be developed for subvolume identification, and correlated with outcomes (e.g. sites of local failure). Complementary and redundant information from different imaging methods (e.g. 11C Methionine PET and DCE MRI in gliomas) for local response prediction will be studied, as well as the reproducibility of imaging signals and extracted subvolumes (from test-retest data). Methods to improve physiological imaging and analysis, including direct analysis of pharmacokinetics via pattern recognition, and sparsely sampled parallel MR! reconstructed using compressed sensing to resolve organ movement and improve the temporal resolution and/or volumetric coverage, will be investigated. To guide therapy individualization, subvolumes need to be mapped geometrically to the treated patient, with uncertainties due to the limits of image registration accuracy. Finite element models will be investigated as atlases to regularize the intensity-based deformable alignment methods used to place these subvolumes in the space of treatment planning CT scans acquired for the purposes of planning and/or modifying treatment.

项目摘要（请参阅说明）： 基于图像的分析是修改放射疗法以根据患者的预后或对治疗的早期反应定制治疗的一种手段，对该计划项目的成功至关重要。 Project 3 investigates the hypothesis that, given the underlying heterogeneity of tumors and normal organs and their response to treatment, anatomic, metabolic and physiologic images (as acquired in the clinical projects of this program) are best utilized for individualizing therapy by robustly identifying subvolumes that are more predictive than individual voxels or the entire tumor/normal organ as a w/hole, and providing these subvolumes as a辐射剂量再分配的空间指南，例如焦点增强。 AIM 1将改善生理图像的重建。目的2是研究鉴定可预测反应的亚参数的方法。 AIM 3将提高对患者映射亚卷的准确性，以指导放射治疗剂量分布的修饰。将开发用于子体积识别的模式识别技术，并与结果相关（例如局部故障的位点）。 将研究来自不同成像方法的互补和冗余信息（例如，胶质膜中的11C甲硫氨酸PET和DCE MRI）进行局部响应预测，以及成像信号和提取的亚物种（从测试重新测试数据）的可重复性的可重复性。改善生理成像和分析的方法，包括通过模式识别对药代动力学的直接分析，以及稀疏采样的平行MR！将研究使用压缩传感来解决器官运动并改善时间分辨率和/或体积覆盖范围进行重建。为了指导治疗个性化，由于图像注册精度的限制，需要将亚参数几何映射到治疗患者中，并具有不确定性。有限元模型将作为地图酶进行研究以正规化基于强度 用于将这些亚参数放置在治疗计划的CT扫描的范围内的可变形比对方法，以计划和/或修改治疗。