Quantifying the impact of shape uncertainty on predicted arrhythmias

• Published in: Computers in biology and medicine 2023-02, Vol.153, p.106528-106528, Article 106528
• Main Authors: Corrado, Cesare, Roney, Caroline H., Razeghi, Orod, Lemus, Josè Alonso Solís, Coveney, Sam, Sim, Iain, Williams, Steven E., O’Neill, Mark D., Wilkinson, Richard D., Clayton, Richard H., Niederer, Steven A.
• Format: Article
• Language: English
• Subjects: Ablation; Algorithms; Anatomy; Arrhythmia; Atria; Atrial Fibrillation; Atrial tachycardia; Atrium; Bayes Theorem; Bayesian analysis; Cardiac arrhythmia; Cardiac models; Catheter Ablation; Computer simulation; Contrast Media; Electrophysiology; Fibrosis; Gadolinium; Heart; Heart Atria; Humans; Magnetic resonance; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Material properties; Mathematical models; Mean; Medical image processing; Medical imaging; Noise prediction; Patients; Predictions; Probability distribution; Standard deviation; Substrates; Uncertainty; Uncertainty quantification
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2022.106528

Personalised computer models are increasingly used to diagnose cardiac arrhythmias and tailor treatment. Patient-specific models of the left atrium are often derived from pre-procedural imaging of anatomy and fibrosis. These images contain noise that can affect simulation predictions. There are few computationally tractable methods for propagating uncertainties from images to clinical predictions. We describe the left atrium anatomy using our Bayesian shape model that captures anatomical uncertainty in medical images and has been validated on 63 independent clinical images. This algorithm describes the left atrium anatomy using Nmodes=15 principal components, capturing 95% of the shape variance and calculated from 70 clinical cardiac magnetic resonance (CMR) images. Latent variables encode shape uncertainty: we evaluate their posterior distribution for each new anatomy. We assume a normally distributed prior. We use the unscented transform to sample from the posterior shape distribution. For each sample, we assign the local material properties of the tissue using the projection of late gadolinium enhancement CMR (LGE-CMR) onto the anatomy to estimate local fibrosis. To test which activation patterns an atrium can sustain, we perform an arrhythmia simulation for each sample. We consider 34 possible outcomes (31 macro-re-entries, functional re-entry, atrial fibrillation, and non-sustained arrhythmia). For each sample, we determine the outcome by comparing pre- and post-ablation activation patterns following a cross-field stimulus. We create patient-specific atrial electrophysiology models of ten patients. We validate the mean and standard deviation maps from the unscented transform with the same statistics obtained with 12,000 Monte Carlo (ground truth) samples. We found discrepancies