AuthorsK. S. Mcleod, M. Sermesant, P. Beerbaum and X. Pennec
TitleDescriptive and Intuitive Population-Based Cardiac Motion Analysis via Sparsity Constrained Tensor Decomposition.
AfilliationCardiac Modeling, Center for Cardiological Innovation (SFI), Scientific Computing
Project(s)Center for Cardiological Innovation (SFI)
StatusPublished
Publication TypeProceedings, refereed
Year of Publication2015
Conference NameMedical Image Computing and Computer-Assisted Intervention-MICCAI 2015
Volume9351
Pagination419-426
Date Published10/2015
PublisherSpringer International Publishing
ISBN Number978-3-319-24574-4
ISSN Number0302-9743
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.

Notes

Poster Presentation by M. Sermesant and X. Pennec

URLhttp://link.springer.com/chapter/10.1007/978-3-319-24574-4_50
DOI10.1007/978-3-319-24574-4_50
Citation Key23823

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