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

项目摘要/摘要 在这份提案中，我们将使用现代机器学习技术来结合和增强 计算模型预测。我们将克服物理上的缺陷 通过包括地面真实的临床测量来建模假设所固有的，但反过来 提供信息更丰富的预测(更高的空间和时间分辨率) 原始的临床测量结果。此外，我们将以代理的形式实现此框架 可以实时使用的模型，可以用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 大量现有的和未来的计算模型，它最终可能导致一种范式 医疗保健工作流程的转变。 我们的主要假设是，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 以获得更准确的预测。我们的多保真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器 一种学习技术，具有概率工作流，并在不同的 以一种原则性的方式保持一定程度的忠诚。 作为其他临床应用程序的模板，我们将为 健康人群和卒中人群脑血流动力学的灌注扫描 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血管成像(CTA和MRA)扫描的中风人群。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，我们 结合计算结果和数据构建特定对象的多保真模型 血流灌注扫描。我们建议利用多保真模型来减少扫描时间和 通过将模拟灌注图与CT灌注扫描相结合来进行辐射暴露。

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.