Cardiac Motion Evolution Model for Analysis of Functional Changes Using Tensor Decomposition and Cross-Sectional Data

• Published in: IEEE transactions on biomedical engineering Vol. 65; no. 12; pp. 2769 - 2780
• Main Authors: McLeod, Kristin, Tondel, Kristin, Calvet, Lilian, Sermesant, Maxime, Pennec, Xavier
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Abnormalities; Adolescent; Adult; Age; Algorithms; Analytical models; Cardiac disease; Cardiac function; Cardiac motion tracking; Child; Computer Science; Coronary artery disease; Data processing; Evolution; evolution modelling; Female; Heart; Heart - diagnostic imaging; Heart diseases; Heart Ventricles - diagnostic imaging; Humans; Image Processing, Computer-Assisted - methods; Image registration; Least squares method; Magnetic Resonance Imaging, Cine; Male; Mathematical models; Medical Imaging; Models, Cardiovascular; Motion tracking; Movement - physiology; N-way PLS; non-rigid image registration; Parameterization; Parameters; Population (statistical); population statistics; spatio-temporal alignment; Spatiotemporal phenomena; Statistical analysis; Tensile stress; tensor decomposition; Tensors; Tetralogy of Fallot; Tetralogy of Fallot - diagnostic imaging; Ventricle; Young Adult; Tetralogy of Fallot; evolution modelling; N-way PLS; atlas; spatio-temporal alignment; non-rigid image registration; population statistics; tensor decomposition; Cardiac motion tracking
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2018.2816519

Cardiac disease can reduce the ability of the ventricles to function well enough to sustain long-term pumping efficiency. Recent advances in cardiac motion tracking have led to improvements in the analysis of cardiac function. We propose a method to study cohort effects related to age with respect to cardiac function. The proposed approach makes use of a recent method for describing cardiac motion of a given subject using a polyaffine model, which gives a compact parameterization that reliably and accurately describes the cardiac motion across populations. Using this method, a data tensor of motion parameters is extracted for a given population. The partial least squares method for higher order arrays is used to build a model to describe the motion parameters with respect to age, from which a model of motion given age is derived. Based on the cross-sectional statistical analysis with the data tensor of each subject treated as an observation along time, the left ventricular motion over time of Tetralogy of Fallot patients is analysed to understand the temporal evolution of functional abnormalities in this population compared to healthy motion dynamics.