Imaging-Based Assessments of Response

项目总结（见说明）： 基于图像的分析作为修改放射治疗的一种手段，以根据个体患者的预后或对治疗的早期反应来定制治疗，是该计划项目成功的关键研究领域。项目3研究假设，鉴于肿瘤和正常器官的潜在异质性及其对治疗的反应，解剖，代谢和生理图像（如在该程序的临床项目中获得的）通过稳健地识别比个体体素或整个肿瘤/正常器官更有预测性的子体积作为W/孔而最好地用于个体化治疗，并且提供这些子体积作为用于辐射剂量再分布（例如焦点增强）的空间引导。目的1将改善生理图像的重建。目标2是研究识别预测反应的子体积的方法。目标3将提高将子体积映射到患者的准确性，以指导放射治疗剂量分布的修改。将开发模式识别技术，用于子体积识别，并与结果（例如局部故障部位）相关联。 将研究来自不同成像方法（例如，胶质瘤中的11 C甲硫氨酸PET和DCE MRI）的用于局部缓解预测的补充和冗余信息，以及成像信号和提取子体积（来自重测数据）的再现性。改善生理成像和分析的方法，包括通过模式识别直接分析药代动力学，以及稀疏采样并行MR！将研究使用压缩感测来重建以解决器官运动并提高时间分辨率和/或体积覆盖。为了指导治疗个体化，需要将子体积几何映射到所治疗的患者，由于图像配准精度的限制而具有不确定性。有限元模型将作为图谱进行研究，以规范基于强度的 用于将这些子体积放置在治疗计划CT扫描空间中的可变形对准方法，所述CT扫描是为了计划和/或修改治疗而采集的。

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.