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

Gerardo Rumindo (1) , Nicolas Duchateau (1, 2) , Pierre Croisille (3) , Jacques Ohayon (4) , Patrick Clarysse (1)

1 MOTIVATE - Imagerie et modélisation Vasculaires, Thoraciques et Cérébrales
2 CREATIS - Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé
3 RMN et optique : De la mesure au biomarqueur
4 TIMC-IMAG-DyCTiM2 - Dynamiques Cellulaire et Tissulaire - Interdisciplinarité, Modélisation & Microscopie

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.

Mots clés: Finite-element model; myocardial infarct; infarct diagnosis; machine learning; myocardial strain

Domaines: Imagerie médicale

Gerardo Rumindo, Nicolas Duchateau, Pierre Croisille, Jacques Ohayon, Patrick Clarysse. Strain-based parameters for infarct localization: evaluation via a learning algorithm on a synthetic database of pathological hearts. 9th International Conference, Functional Imaging and Modeling of the Heart (FIMH), 2017, Toronto, Canada. pp.106-114. ⟨hal-01829278⟩