Paper Info
Title
Longitudinal Analysis Using Personalised 3D Cardiac Models with Population-Based Priors: Application to Paediatric Cardiomyopathies.
Abstract
Personalised 3D modelling of the heart is of increasing interest in order to better characterise pathologies and predict evolution. The personalisation consists in estimating the parameter values of an electromechanical model in order to reproduce the observed cardiac motion. However, the number of parameters in these models can be high and their estimation may not be unique. This variability can be an obstacle to further analyse the estimated parameters and for their clinical interpretation. In this paper we present a method to perform consistent estimations of electromechanical parameters with prior probabilities on the estimated values, which we apply on a large database of 84 different heartbeats. We show that the use of priors reduces considerably the variance in the estimated parameters, enabling better conditioning of the parameters for further analysis of the cardiac function. This is demonstrated by the application to longitudinal data of paediatric cardiomyopathies, where the estimated parameters provide additional information on the pathology and its evolution.
Year
Venue
Field
2017
MICCAI
Population,Data mining,Computer science,Prior probability,Cardiac motion
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
2
15
Name
Order
Citations
PageRank
Roch Molléro172.70
Hervé Delingette22133207.11
Manasi Datar300.68
Tobias Heimann489352.62
Jakob A. Hauser500.68
Dilveer Panesar600.34
Alexander Jones701.01
Andrew Mayall Taylor800.34
Marcus Kelm921.73
titus kuehne1023.85
Marcello Chinali1140.96
Gabriele Rinelli1240.96
Nicholas Ayache13108041654.36
X. Pennec1451143.47
Maxime Sermesant151111122.97