SCH: Proactive Health Monitoring Using Individualized Analysis of Tissue Elastic*

描述（由申请人提供）：现有研究表明，组织弹性可能与癌症的侵袭性有关。基于可变形图像配准的结果，该项目研究了在外力和几何约束下跟踪器官运动的可能性，从而推导出患者特异性的组织弹性参数，用于主动健康监测。本探索性研究项目的目标是：(1)基于对大量癌症患者的广泛研究，开发一个计算框架，以便在一对医学图像（可能来自超声，乳房x线摄影，计算机断层扫描，磁共振成像或其他成像技术）上使用耦合生物力学模拟-优化框架准确估计患者特异性组织弹性；(2)研究不同区域的组织弹性与相应区域已知/诊断癌症侵袭性之间的潜在关联；(3)基于恢复的患者特异性组织弹性和其他解释变量，推导癌症分期/分级的预测模型；(4)针对“高危”人群设计基于组织弹性个性化分析的健康监测系统

DESCRIPTION (provided by applicant): Existing studies suggest that tissue elasticity is possibly correlated with the aggressiveness of cancers. Based on results from deformable image registration, the proposed project investigates the possibility of tracking the organs movement subject to external forces and geometric constraints, thereby deducing patient-specific tissue elasticity parameters for proactive health monitoring. The objectives of this exploratory research project are (1) to develop a computational framework based on extensive studies of a large cohort of cancer patients, in order to accurately estimate patent-specific tissu elasticity using a coupled biomechanical simulation-optimization framework on a pair of medical images (possibly from ultrasound, mammography, computed tomography scan, magnetic resonance imaging, or other imaging technologies); (2) to examine potential association between tissue elasticity in different regions with aggressiveness of know/diagnosed cancer in the corresponding regions; (3) to derive predictive models for cancer staging/grading based on recovered patient-specific tissue elasticity and other explanatory variables; (4) to design a health monitoring system based on individualized analysis of tissue elasticity for 'at-risk' groups who are more likely to develop cancers. This proposal describes a truly ambitious effort and a bold vision that is built upon the investigators' prior scientific accomplishments and strong credentials to potentially transform existing practice to more proactive, preventive, evidence-based health monitoring for individuals at risk of developing cancers. This research is expected to make several major scientific advances. These include new algorithms for non-invasive, image-based techniques for automatic extraction of tissue elasticity parameters without force applications and/or force sensing devices, novel regression models and inference procedures for survival analysis, new force sensing devices, novel regression models and inference procedures for survival analysis, new predictive models for cancer staging and grading based on patient-specific tissue elasticity parameters, and a health monitoring system for at-risk groups based on individual tissue elasticity along with other variables.