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:
  • 金额:
    $ 24.66万
  • 依托单位:
  • 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

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

项目成果

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Hessam Babaee其他文献

Hessam Babaee的其他文献

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{{ truncateString('Hessam Babaee', 18)}}的其他基金

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
  • 批准号:
    10483126
  • 财政年份:
    2021
  • 资助金额:
    $ 24.66万
  • 项目类别:
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
  • 批准号:
    10676278
  • 财政年份:
    2021
  • 资助金额:
    $ 24.66万
  • 项目类别:
Noninvasive Real-time Estimation of Cerebral Blood Flow for Personalized Stroke Assessment
用于个性化中风评估的脑血流量无创实时估计
  • 批准号:
    9898495
  • 财政年份:
    2019
  • 资助金额:
    $ 24.66万
  • 项目类别:

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