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2019 | OriginalPaper | Chapter

Learning Associations Between Clinical Information and Motion-Based Descriptors Using a Large Scale MR-derived Cardiac Motion Atlas

Authors: Esther Puyol-Antón, Bram Ruijsink, Hélène Langet, Mathieu De Craene, Paolo Piro, Julia A. Schnabel, Andrew P. King

Published in: Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges

Publisher: Springer International Publishing

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Abstract

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 (\(\approx \) 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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Metadata
Title
Learning Associations Between Clinical Information and Motion-Based Descriptors Using a Large Scale MR-derived Cardiac Motion Atlas
Authors
Esther Puyol-Antón
Bram Ruijsink
Hélène Langet
Mathieu De Craene
Paolo Piro
Julia A. Schnabel
Andrew P. King
Copyright Year
2019
DOI
https://doi.org/10.1007/978-3-030-12029-0_11

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