Techniques for estimation of model parameters in computational hemodynamics

A major challenge in constructing three dimensional patient specific hemodynamic models is the calibration of model parameters to match patient data on flow, pressure, wall motion, etc. acquired in the clinic. This work summarizes several formulations for model parameter and flow estimations in cardiovascular applications, using MRI, CT, and angiography.

In the context of MRI and CT data, we have developed a CFD parameter estimation framework that relies on the following fundamental contributions [1]. (i) A Reduced-Order Unscented Kalman Filter (ROUKF) model for data assimilation for wall material and simple lumped parameter network (LPN) boundary condition model parameters. (ii) A constrained least squares augmentation (ROUKF-CLS) for more complex LPNs. (iii) A “Netlist” implementation, supporting easy filtering of parameters in such complex LPNs. The ROUKF algorithm is demonstrated using non-invasive patient-specific data on anatomy, flow and pressure from a healthy volunteer. The ROUKF-CLS algorithm is demonstrated using synthetic data on a coronary LPN. These methods have been implemented as part of the CRIMSON hemodynamics software package [2].

In the context of X-ray angiography data, we have recently developed a fully automatic method to segment arteries, through a convolutional neural network, AngioNet [3]. The main innovation in this network is the introduction of an Angiographic Processing Network (APN) which significantly improves segmentation performance on multiple network backbones, with the best performance using Deeplabv3+. This APN enabled us to learn the best possible pre-processing filters to improve segmentation, including when using dynamic series. This is a key step towards automated assessment of flow using X-ray angiography.