Comparison of Different Strain-Based Parameters to Identify Human Left Ventricular Myocardial Infarct During Diastole: A 3D Finite-Element Study

Rumindo, Gerardo Kenny; Ohayon, Jacques; Viallon, Magalie; Stuber, Mathias; Croisille, Pierre; Clarysse, Patrick

doi:10.1007/978-3-319-59764-5_18

Cited by 1 publication

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“…The diastolic simulation for the healthy LV model follows the principles detailed in [6,11]. Rule-based fiber orientation was incorporated into the LV geometry by first defining a pseudoprolate spheroidal coordinate system that consists of radial (orthogonal to the LV surface), circumferential and longitudinal (tangential to the LV surface) directions.…”

Section: Database Of Pathological Casesmentioning

confidence: 99%

Strain-Based Parameters for Infarct Localization: Evaluation via a Learning Algorithm on a Synthetic Database of Pathological Hearts

Rumindo

Duchateau

Croisille

et al. 2017

Functional Imaging and Modelling of the Heart

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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 su cient 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-theart 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.

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Section: Database Of Pathological Casesmentioning

confidence: 99%