Method and System for Image-Based Estimation of Multi-Physics Parameters and Their Uncertainty for Patient-Specific Simulation of Organ Function

摘要

A method and system for estimating tissue parameters of a computational model of organ function and their uncertainty due to model assumptions, data noise and optimization limitations is disclosed. As applied to a cardiac use-case, a patient-specific anatomical heart model is generated from medical image data of a patient. A patient-specific computational heart model is generated based on the patient-specific anatomical heart model. Patient-specific parameters and corresponding uncertainty values are estimated for at least a subset of parameters of the patient-specific computational heart model. A surrogate model is estimated for a forward model of cardiac function, and the surrogate model is applied within Bayesian inference to estimate the posterior probability density function of the parameter space of the forward model. Cardiac function for the patient is simulated using the patient-specific computational heart model. The estimated parameters, their uncertainty, and the computed cardiac function are displayed to the user.

主权项

1. A method for generating a patient-specific computational model of organ function having patient-specific tissue parameters and corresponding uncertainty values, comprising:
estimating a plurality of forward model responses for the computational model of organ function corresponding to a plurality of parameter values for tissue parameters of the computational model of organ function; generating, for each of one or more noise levels, a respective set of samples of a posterior density of a parameter space of the tissue parameters of the computational model of organ function based on the estimated plurality of forward model responses; modeling a probability density function for each respective set of samples; selecting a most likely point in the model of the probability density function as an estimate of the patient-specific tissue parameters of the computational model of organ function; and determining a confidence region for the estimate of the patient-specific tissue parameters based on the model of the probability density function.