Improving Cardio-Mechanic Inference by Combining in Vivo Strain Data with Ex Vivo Volume–Pressure Data

Abstract

AbstractCardio-mechanic models show substantial promise for improving personalised diagnosis and disease risk prediction. However, estimating the constitutive parameters from strains extracted from in vivo cardiac magnetic resonance scans can be challenging. The reason is that circumferential strains, which are comparatively easy to extract, are not sufficiently informative to uniquely estimate all parameters, while longitudinal and radial strains are difficult to extract at high precision. In the present study, we show how cardio-mechanic parameter inference can be improved by incorporating prior knowledge from population-wide ex vivo volume–pressure data. Our work is based on an empirical law known as the Klotz curve. We propose and assess two alternative methodological frameworks for integrating ex vivo data via the Klotz curve into the inference framework, using both a non-empirical and empirical prior distribution.