Uncertainty Quantification in Cardiac Model Personalisation from Ultrafast Ultrasound

2026-09-28

AI Summary

This study addresses the ill-posed parameter inversion and uncertainty quantification challenges in cardiac model personalization by proposing a posterior estimation framework based on simulation-based inference (SBI) and neural networks. Methodologically, the approach integrates a 0D cardiovascular model with shear wave elastography to efficiently estimate posterior distributions of cardiac mechanical parameters from ultrafast ultrasound data. Furthermore, prior knowledge is incorporated to evaluate model adequacy, revealing inter-parameter compensation mechanisms and sources of uncertainty. Results demonstrate an approximately 90% reduction in curve-fitting root mean square error (RMSE), achieving uncertainty-aware personalized modeling. These findings validate the effectiveness and clinical potential of the proposed methodology for robust cardiac model personalization.

Abstract

Cardiac model personalisation requires inferring mechanical parameters that are not directly measurable in vivo. Ultrafast ultrasound shear wave elastography (SWE) enables non-invasive tracking of myocardial stiffness dynamics over the cardiac cycle, providing a target for personalisation. However, mapping these observations to subject specific model parameters remains ill-posed, as multiple parameter sets can reproduce the same stiffness dynamics. We formulate SWE-informed personalisation as a statistical inference problem using simulation-based inference (SBI). Using a subject-adapted 0D cardiovascular model and neural posterior estimation, we estimate model-conditional posterior distributions over active stiffness scale k0, contraction rate kATP, and relaxation rate kSR, conditioned on SWE-derived curve features and subject specific context. Among six healthy volunteers, four passed objective prior-support diagnostics and were retained for quantitative posterior analysis. Curve-level RMSE against the observed SWE target decreased from 12.61 $\pm$ 5.55 kPa for the prior predictive median to 1.14 $\pm$ 0.38 kPa for the posterior predictive median, an 89.7 $\pm$ 4.2% reduction. Posterior analysis revealed parameter-specific uncertainty, k0-kATP compensation, weaker constraint of kSR, and the importance of prior-predictive diagnostics for assessing whether each subject is represented within the modelled SWE feature space. These results support SBI for uncertainty aware SWE-based personalisation, while identifying prior support and forward-model adequacy as key diagnostics.

Problem

• Uncertainty Quantification
• Cardiac Model Personalisation
• Shear Wave Elastography
• Ill-posed Inverse Problem

Innovation

• Simulation-based Inference
• Shear Wave Elastography
• Neural Posterior Estimation
• Uncertainty Quantification
• Cardiac Model Personalisation