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Puyol-Antón, E.* ; Ruijsink, B.* ; Langet, H.* ; De Craene, M.* ; Piro, P.* ; Schnabel, J.A.* ; King, A.P.*

Learning associations between clinical information and motion-based descriptors using a large scale MR-derived cardiac motion atlas.

In: (International Workshop on Statistical Atlases and Computational Models of the Heart). Berlin [u.a.]: Springer, 2019. 94-102 (Lect. Notes Comput. Sc. ; 11395 LNCS)
DOI
Open Access Green as soon as Postprint is submitted to ZB.
The availability of large scale databases containing imaging and non-imaging data, such as the UK Biobank, represents an opportunity to improve our understanding of healthy and diseased bodily function. Cardiac motion atlases provide a space of reference in which the motion fields of a cohort of subjects can be directly compared. In this work, a cardiac motion atlas is built from cine MR data from the UK Biobank ((Formula Presented) 6000 subjects). Two automated quality control strategies are proposed to reject subjects with insufficient image quality. Based on the atlas, three dimensionality reduction algorithms are evaluated to learn data-driven cardiac motion descriptors, and statistical methods used to study the association between these descriptors and non-imaging data. Results show a positive correlation between the atlas motion descriptors and body fat percentage, basal metabolic rate, hypertension, smoking status and alcohol intake frequency. The proposed method outperforms the ability to identify changes in cardiac function due to these known cardiovascular risk factors compared to ejection fraction, the most commonly used descriptor of cardiac function. In conclusion, this work represents a framework for further investigation of the factors influencing cardiac health.
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Publication type Article: Conference contribution
Corresponding Author
Keywords Cardiac Motion Atlas ; Dimensionality Reduction ; Multivariate Statistics ; Non-imaging Data
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Conference Title International Workshop on Statistical Atlases and Computational Models of the Heart
Journal Lecture Notes in Computer Science
Quellenangaben Volume: 11395 LNCS, Issue: , Pages: 94-102 Article Number: , Supplement: ,
Publisher Springer
Publishing Place Berlin [u.a.]
Non-patent literature Publications
Institute(s) Institute for Machine Learning in Biomed Imaging (IML)