Lazarus, A., Gao, H. , Luo, X. and Husmeier, D. (2022) Improving cardio-mechanic inference by combining in vivo strain data with ex vivo volume–pressure data. Journal of the Royal Statistical Society: Series C (Applied Statistics), 71(4), pp. 906-931. (doi: 10.1111/rssc.12560)
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Abstract
Cardio-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.
| Item Type: | Articles |
|---|---|
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Luo, Professor Xiaoyu and Gao, Dr Hao and Husmeier, Professor Dirk and Lazarus, Dr Alan |
| Authors: | Lazarus, A., Gao, H., Luo, X., and Husmeier, D. |
| College/School: | College of Science and Engineering > School of Mathematics and Statistics College of Science and Engineering > School of Mathematics and Statistics > Mathematics College of Science and Engineering > School of Mathematics and Statistics > Statistics |
| Journal Name: | Journal of the Royal Statistical Society: Series C (Applied Statistics) |
| Publisher: | Wiley |
| ISSN: | 0035-9254 |
| ISSN (Online): | 1467-9876 |
| Published Online: | 22 April 2022 |
| Copyright Holders: | Copyright © 2022 The Authors |
| First Published: | First published in Journal of the Royal Statistical Society: Series C (Applied Statistics) 71(4):906-931 |
| Publisher Policy: | Reproduced under a Creative Commons License |
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