Paper Info
Title
Spatio-temporal dimension reduction of cardiac motion for group-wise analysis and statistical testing.
Abstract
Given the observed abnormal motion dynamics of patients with heart conditions, quantifying cardiac motion in both normal and pathological cases can provide useful insights for therapy planning. In order to be able to analyse the motion over multiple subjects in a robust manner, it is desirable to represent the motion by a low number of parameters. We propose a reduced order cardiac motion model, reduced in space through a polyaffine model, and reduced in time by statistical model order reduction. The method is applied to a data-set of synthetic cases with known ground truth to validate the accuracy of the left ventricular motion tracking, and to validate a patient-specific reduced-order motion model. Population-based statistics are computed on a set of 15 healthy volunteers to obtain separate spatial and temporal bases. Results demonstrate that the reduced model can efficiently detect abnormal motion patterns and even allowed to retrospectively reveal abnormal unnoticed motion within the control subjects.
Year
DOI
Venue
2013
10.1007/978-3-642-40763-5_62
Lecture Notes in Computer Science
Field
DocType
Volume
Population,Computer vision,Singular value decomposition,Dimensionality reduction,Pattern recognition,Computer science,Ground truth,Statistical model,Artificial intelligence,Cardiac motion,Match moving,Statistical hypothesis testing
Conference
8150
Issue
ISSN
Citations 
Pt 2
0302-9743
5
PageRank 
References 
Authors
0.44
10
4
Name
Order
Citations
PageRank
Kristin McLeod19710.68
Christof Seiler2605.80
Maxime Sermesant31111122.97
Xavier Pennec45021357.08