Paper Info
Title
Descriptive and Intuitive Population-Based Cardiac Motion Analysis via Sparsity Constrained Tensor Decomposition.
Abstract
Analysing and understanding population-specific cardiac function is a challenging task due to the complex dynamics observed in both healthy and diseased subjects and the difficulty in quantitatively comparing the motion in different subjects. Affine parameters extracted from a Polyaffine motion model for a group of subjects can be used to represent the 3D motion regionally over time for a group of subjects. We propose to construct from these parameters a 4-way tensor of the rotation, stretch, shear, and translation components of each affine matrix defined in an intuitive coordinate system, stacked per region, for each affine component, over time, and for all subjects. From this tensor, Tucker decomposition can be applied with a constraint of sparsity on the core tensor in order to extract a few key, easily interpretable modes for each subject. Using this construction of a data tensor, the tensors of multiple groups can be stacked and collectively decomposed in order to compare and discriminate the motion by analysing the different loadings of each combination of modes for each group. The proposed method was applied to study and compare left ventricular dynamics for a group of healthy adult subjects and a group of adults with repaired Tetralogy of Fallot.
Year
DOI
Venue
2015
10.1007/978-3-319-24574-4_50
Lecture Notes in Computer Science
Field
DocType
Volume
Coordinate system,Affine transformation,Population,Complex dynamics,Tensor,Pattern recognition,Artificial intelligence,Tucker decomposition,Cardiac motion,Mathematics,Tensor decomposition
Conference
9351
ISSN
Citations 
PageRank 
0302-9743
2
0.40
References 
Authors
7
4
Name
Order
Citations
PageRank
Kristin McLeod19710.68
Maxime Sermesant21111122.97
Philipp Beerbaum3141.78
Xavier Pennec45021357.08