Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Personalized and uncertainty-aware coronary hemodynamics simulations: From Bayesian estimation to improved multi-fidelity uncertainty quantification

Karthik Menon, Andrea Zanoni, Owais Khan, Gianluca Geraci, Koen Nieman, Daniele E. Schiavazzi, Alison L. Marsden

TL;DR

This work presents an end-to-end uncertainty-aware pipeline to personalize coronary flow simulations by incorporating vessel-specific coronary flows as well as cardiac function and predicts clinical and biomechanical quantities of interest with improved precision, while accounting for uncertainty in the clinical data.

Abstract

Simulations of coronary hemodynamics have improved non-invasive clinical risk stratification and treatment outcomes for coronary artery disease, compared to relying on anatomical imaging alone. However, simulations typically use empirical approaches to distribute total coronary flow amongst the arteries in the coronary tree. This ignores patient variability, the presence of disease, and other clinical factors. Further, uncertainty in the clinical data often remains unaccounted for in the modeling pipeline. We present an end-to-end uncertainty-aware pipeline to (1) personalize coronary flow simulations by incorporating branch-specific coronary flows as well as cardiac function; and (2) predict clinical and biomechanical quantities of interest with improved precision, while accounting for uncertainty in the clinical data. We assimilate patient-specific measurements of myocardial blood flow from CT myocardial perfusion imaging to estimate branch-specific coronary flows. We use adaptive Markov Chain Monte Carlo sampling to estimate the joint posterior distributions of model parameters with simulated noise in the clinical data. Additionally, we determine the posterior predictive distribution for relevant quantities of interest using a new approach combining multi-fidelity Monte Carlo estimation with non-linear, data-driven dimensionality reduction. Our framework recapitulates clinically measured cardiac function as well as branch-specific coronary flows under measurement uncertainty. We substantially shrink the confidence intervals for estimated quantities of interest compared to single-fidelity and state-of-the-art multi-fidelity Monte Carlo methods. This is especially true for quantities that showed limited correlation between the low- and high-fidelity model predictions. Moreover, the proposed estimators are significantly cheaper to compute for a specified confidence level or variance.

Personalized and uncertainty-aware coronary hemodynamics simulations: From Bayesian estimation to improved multi-fidelity uncertainty quantification

TL;DR

This work presents an end-to-end uncertainty-aware pipeline to personalize coronary flow simulations by incorporating vessel-specific coronary flows as well as cardiac function and predicts clinical and biomechanical quantities of interest with improved precision, while accounting for uncertainty in the clinical data.

Abstract

Simulations of coronary hemodynamics have improved non-invasive clinical risk stratification and treatment outcomes for coronary artery disease, compared to relying on anatomical imaging alone. However, simulations typically use empirical approaches to distribute total coronary flow amongst the arteries in the coronary tree. This ignores patient variability, the presence of disease, and other clinical factors. Further, uncertainty in the clinical data often remains unaccounted for in the modeling pipeline. We present an end-to-end uncertainty-aware pipeline to (1) personalize coronary flow simulations by incorporating branch-specific coronary flows as well as cardiac function; and (2) predict clinical and biomechanical quantities of interest with improved precision, while accounting for uncertainty in the clinical data. We assimilate patient-specific measurements of myocardial blood flow from CT myocardial perfusion imaging to estimate branch-specific coronary flows. We use adaptive Markov Chain Monte Carlo sampling to estimate the joint posterior distributions of model parameters with simulated noise in the clinical data. Additionally, we determine the posterior predictive distribution for relevant quantities of interest using a new approach combining multi-fidelity Monte Carlo estimation with non-linear, data-driven dimensionality reduction. Our framework recapitulates clinically measured cardiac function as well as branch-specific coronary flows under measurement uncertainty. We substantially shrink the confidence intervals for estimated quantities of interest compared to single-fidelity and state-of-the-art multi-fidelity Monte Carlo methods. This is especially true for quantities that showed limited correlation between the low- and high-fidelity model predictions. Moreover, the proposed estimators are significantly cheaper to compute for a specified confidence level or variance.
Paper Structure (21 sections, 16 equations, 9 figures, 1 table)

This paper contains 21 sections, 16 equations, 9 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methods
  3. Clinical imaging and patient data
  4. Computational modeling
  5. Parameter estimation using Markov Chain Monte Carlo
  6. Multi-fidelity uncertainty propagation
  7. Multi-fidelity Monte Carlo
  8. Non-linear dimensionality reduction with autoencoders and normalizing flows
  9. Results
  10. Parameter estimation
  11. Uncertainty propagation
  12. Lumen wall shear stress
  13. Fractional flow reserve
  14. Computational budget
  15. Discussion
  16. ...and 6 more sections

Figures (9)

  • Figure 1: An overview of the pipeline developed in this work, from clinical image analysis, to Bayesian parameter estimation, and, finally, the computation of posterior predictive quantities of interest.
  • Figure 2: (a) Slices of the estimated posterior distributions of the distal resistance for all 14 coronary outlets. (b) Distributions of predicted coronary flows compared with clinically measured targets (vertical line) and noise distribution for each coronary artery.
  • Figure 3: Correlation between maximum OSI from 3D simulations and mean outlet flow from 0D simulations. Data is shown for the standard and MFMC-AE-reparameterized 0D models. The three panels show the correlations for the LAD, LCx and RCA branches.
  • Figure 4: Maximum OSI in (a)-(c) and minimum TAWSS in (d)-(f) for LAD, LCx and RCA branches estimated using one realization of MC, MFMC and MFMC-AE estimators. Solid and dashed lines show 95% and 99% confidence intervals, respectively.
  • Figure 5: FFR estimated with 0D FFR in (a)-(c), and 0D mean flow in (d)-(f), using one realization of MC, MFMC and MFMC-AE. The three panels show data for the LAD, LCx and RCA branches. Solid and dashed lines show 95% and 99% confidence intervals.
  • ...and 4 more figures