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

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

1 Citation (Scopus)

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 ((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.

Original languageEnglish
Title of host publicationStatistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges - 9th International Workshop, STACOM 2018, Held in Conjunction with MICCAI 2018, Revised Selected Papers
EditorsMaxime Sermesant, Jichao Zhao, Alistair Young, Kawal Rhode, Mihaela Pop, Kristin McLeod, Tommaso Mansi, Shuo Li
PublisherSpringer Verlag
Pages94-102
Number of pages9
ISBN (Print)9783030120283
DOIs
Publication statusPublished - 1 Jan 2019
Event9th International Workshop on Statistical Atlases and Computational Models of the Heart: Atrial Segmentation and LV Quantification Challenges, STACOM 2018, held in conjunction with Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018 - Granada, Spain
Duration: 16 Sept 201816 Sept 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11395 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th International Workshop on Statistical Atlases and Computational Models of the Heart: Atrial Segmentation and LV Quantification Challenges, STACOM 2018, held in conjunction with Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018
Country/TerritorySpain
CityGranada
Period16/09/201816/09/2018

Keywords

  • Cardiac motion atlas
  • Dimensionality reduction
  • Multivariate statistics
  • Non-imaging data

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