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

项目概要/摘要 在本提案中，我们将使用现代机器学习技术来联合收割机和增强 计算机模拟预测。我们将克服物理缺陷， 固有的建模假设，包括地面实况临床测量，但反过来 提供的预测信息量更大（更高的空间和时间分辨率）， 原始临床测量。此外，我们将实现此框架作为代理 可以在真实的时间中使用的模型，并且可以以高得令人望而却步的 计算成本如果成功，拟议的研究将使实验室到床边部署， 大量的现有和未来的计算模型，它最终可能导致一个范式， 医疗保健工作流程的转变。 我们的首要假设是，计算之间的统计相关性 可以在概率数据融合框架中利用模型和临床测量 更准确的预测。我们的多保真度框架是基于自回归的 高斯过程（GP）方案。我们提出的方案是一个非参数贝叶斯机 学习技术，具有概率工作流程，并在不同时间估计不确定性。 以一种有原则的方式保持忠诚。 作为其他临床应用的模板，我们将开发此框架， 脑血流动力学灌注扫描在健康和中风人群中， 重要的健康应用。在目标1中，我们将模拟健康和 基于CT和MR血管造影（CTA和MRA）扫描的卒中人群。我们将模拟和 验证健康和中风性别平衡受试者的脑血流灌注。在目标2中， 通过结合计算结果构建特定于主题的多保真度模型， 灌注扫描我们建议利用多保真度模型来减少扫描时间， 通过将模拟灌注图与CT灌注扫描相结合进行辐射暴露。

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.