Infarct localization from myocardial deformation: Prediction and uncertainty quantification by regression from a low-dimensional spaceReport as inadecuate




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1 ASCLEPIOS - Analysis and Simulation of Biomedical Images CRISAM - Inria Sophia Antipolis - Méditerranée 2 Medisys - MedisysResearch Lab 3 CHU Caen 4 Signalisation, électrophysiologie et imagerie des lésions d-ischémie-reperfusion myocardique Caen

Abstract : Diagnosing and localizing myocardial infarct is crucial for early patient management and therapy planning. We propose a new method for predicting the location of myocardial infarct from local wall deformation, which has value for risk stratification from routine examinations such as 3D echocardiography. The pipeline combines non-linear dimensionality reduction of deformation patterns and two multi-scale kernel regressions. Confidence in the diagnosis is assessed by a map of local uncertainties, which integrates plausible infarct locations generated from the space of reduced dimensionality. These concepts were tested on 500 synthetic cases generated from a realistic cardiac electromechanical model, and 108 pairs of 3D echocardiographic sequences and delayed-enhancement magnetic resonance images from real cases. Infarct prediction is made at a spatial resolution around 4 mm, more than 10 times smaller than the current diagnosis, made regionally. Our method is accurate, and significantly outperforms the clinically-used thresholding of the deformation patterns on real data: sensitivity - specificity of 0.828-0.804, area under the curve: 0.909 vs. 0.742 for the most predictive strain component. Uncertainty adds value to refine the diagnosis and eventually re-examine suspicious cases.

Keywords : biomechanical modeling delayed-enhancement pattern recognition & classification Index Terms—Myocardial infarct dimensionality reduction computer-aided diagnosis ultrasound





Author: Nicolas Duchateau - Mathieu De Craene - Pascal Allain - Eric Saloux - Maxime Sermesant -

Source: https://hal.archives-ouvertes.fr/



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