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 ± 5.55 kPa for the prior predictive median to 1.14 ± 0.38 kPa for the posterior predictive median, an 89.7 ± 4.2% reduction. Poste- rior 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.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/STACOM2026_033.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: https://openreview.net/profile?id=~Camilla_Ferrario1
BibTex
@InProceedings{FerCam_Uncertainty_MICCAISAT2026,
author = { Ferrario, Camilla AND Venet, Maelys AND Villemain, Olivier AND Sermesant, Maxime},
title = { { Uncertainty Quantification in Cardiac Model Personalisation from Ultrafast Ultrasound } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17267},
month = {pending},
page = {pending}
}