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

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