Paper Info
Title
Prediction of Infarct Localization from Myocardial Deformation.
Abstract
We propose a novel framework to predict the location of a myocardial infarct from local wall deformation data. Non-linear dimensionality reduction is used to estimate the Euclidean space of coordinates encoding deformation patterns. The infarct location of a new subject is inferred by two consecutive interpolations, formulated as multiscale kernel regressions. They consist in i finding the low-dimensional coordinates associated to the measured deformation pattern, and ii estimating the possible infarct location associated to these coordinates. These concepts were tested on a database of 500 synthetic cases generated from a realistic electromechanical model of the two ventricles. The database consisted of infarcts of random extent, shape, and location overlapping the whole left-anterior-descending coronary territory. We demonstrate that our method is accurate and significantly overcomes the limitations of the clinically-used thresholding of the deformation patterns average area under the ROC curve of 0.992$$\\pm $$0.011 vs. 0.812$$\\pm $$0.124, p$$<$$0.001.
Year
DOI
Venue
2015
10.1007/978-3-319-28712-6_6
STACOM@MICCAI
DocType
Citations 
PageRank 
Conference
2
0.37
References 
Authors
7
2
Name
Order
Citations
PageRank
N Duchateau119920.53
Maxime Sermesant21111122.97