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Enhanced Clinical Diagnosis through Imaging and Modeling: A Machine Learning Data Fusion Framework

通过成像和建模增强临床诊断：机器学习数据融合框架

基本信息

批准号：
10287669
负责人：
Hessam Babaee
金额：
$ 24.66万
依托单位：
UNIVERSITY OF PITTSBURGH AT PITTSBURGH
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-07 至 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10287669
关键词：
3-Dimensional 4D MRI Adoption Age Angiography Architecture Bayesian learning Blood Vessels Blood Volume Blood flow Brain Brain scan Cerebrovascular Circulation Cerebrum Certification Clinic Clinical Computational Technique Computer Models Computer Simulation Consequentialism Core-Binding Factor Correlation Studies Data Data Set Databases Doppler Ultrasound Future Gaussian model Gender Goals Healthcare Heterogeneity Image Ischemic Stroke Lead Machine Learning Maps Measurement Medical Imaging Medicine Methodology Modality Modeling Modernization Partner in relationship Patients Perfusion Physics Population Positioning Attribute Process Radiation exposure Research Resolution Scanning Scheme Source Stroke Techniques Technology Testing Time Training Uncertainty Validation base blood perfusion cerebral artery clinical Diagnosis clinical application clinical decision-making cost data fusion health application hemodynamics innovation interest novel perfusion imaging population based predictive modeling relating to nervous system sensor simulation spatiotemporal stroke patient temporal measurement

项目摘要

PROJECT SUMMARY/ABSTRACT In this proposal, we will use modern machine learning techniques to combine and enhance computational modeling predictions. We will overcome the physics deficiencies that are inherent in modeling assumptions by including ground-truth clinical measurements, but in turn provide predictions that are more informative (higher spatial and temporal resolution) than the original clinical measurements. Furthermore, we will implement this framework as a surrogate model that can be used in real time and can replace current models with prohibitively high computation cost. If successful, the proposed research will enable lab-to-bedside deployment of a vast array of existing and future computational models and it ultimately could lead to a paradigm shift in health care workflow. Our overarching hypothesis is that the statistical correlations between computational models and clinical measurements can be exploited in a probabilistic data-fusion framework for more accurate predictions. Our multi-fidelity framework is based on an autoregressive Gaussian Process (GP) scheme. Our proposed scheme is a non-parametric Bayesian machine learning technique that has a probabilistic workflow and estimates uncertainty at different levels of fidelity in a principled manner. As a template for other clinical applications, we will develop this framework for perfusion scanning of brain hemodynamics in healthy and stroke populations, which has a significant health application. In Aim 1, we will simulate cerebral perfusion in healthy and stroke populations based on CT and MR angiography (CTA and MRA) scans. We will simulate and validate cerebral blood perfusion in healthy and stroke gender-balanced subjects. In Aim 2, we construct subject-specific multi-fidelity models by combining computational results and perfusion scans. We propose to leverage the multi-fidelity model to reduce scan time and radiation exposure by incorporating simulated perfusion maps with CT perfusion scans.

项目总结/文摘