Strain-based parameters for infarct localization: Evaluation via a learning algorithm on a synthetic database of pathological hearts

Gerardo Kenny Rumindo, Nicolas Duchateau, Pierre Croisille, Jacques Ohayon, Patrick Clarysse

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Localization of infarcted regions is essential to determine the most appropriate treatment for patients with cardiac ischemia. Myocardial strain partially reflects the location of infarcted regions, which demonstrated potential use in clinical practice. However, strain patterns are complex and simple thresholding is not sufficient to locate the infarcts. Besides, many strain-based parameters exist and their sensitivities to myocardial infarcts have not been directly investigated. In our study, we propose to evaluate nine strain-based parameters to locate infarcted regions. For this purpose, we designed a large database (n = 200) of synthetic pathological finite-element heart models from 5 real healthy left ventricle geometries. The infarcts were incorporated with random location, shape and degree of severity. In addition, we used a state-of-the-art learning algorithm to link deformation patterns and infarct location. Based on our evaluation, we propose to sort the strain-based parameters into three groups according to their performances in locating infarcts.

Original languageEnglish (US)
Title of host publicationFunctional Imaging and Modelling of the Heart - 9th International Conference, FIMH 2017, Proceedings
EditorsMihaela Pop, Graham A. Wright
PublisherSpringer-Verlag
Pages106-114
Number of pages9
ISBN (Print)9783319594477
DOIs
StatePublished - 2017
Event9th International Conference on Functional Imaging and Modelling of the Heart, FIMH 2017 - Toronto, Canada
Duration: Jun 11 2017Jun 13 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10263 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th International Conference on Functional Imaging and Modelling of the Heart, FIMH 2017
CountryCanada
CityToronto
Period6/11/176/13/17

Keywords

  • Finite-element model
  • Infarct diagnosis
  • Machine learning
  • Myocardial infarct
  • Myocardial strain

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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