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A Statistical Model for Quantification and Prediction of Cardiac Remodelling: Application to Tetralogy of Fallot | IEEE Journals & Magazine | IEEE Xplore

A Statistical Model for Quantification and Prediction of Cardiac Remodelling: Application to Tetralogy of Fallot


Abstract:

Cardiac remodelling plays a crucial role in heart diseases. Analyzing how the heart grows and remodels over time can provide precious insights into pathological mechanism...Show More

Abstract:

Cardiac remodelling plays a crucial role in heart diseases. Analyzing how the heart grows and remodels over time can provide precious insights into pathological mechanisms, eventually resulting in quantitative metrics for disease evaluation and therapy planning. This study aims to quantify the regional impacts of valve regurgitation and heart growth upon the end-diastolic right ventricle (RV) in patients with tetralogy of Fallot, a severe congenital heart defect. The ultimate goal is to determine, among clinical variables, predictors for the RV shape from which a statistical model that predicts RV remodelling is built. Our approach relies on a forward model based on currents and a diffeomorphic surface registration algorithm to estimate an unbiased template. Local effects of RV regurgitation upon the RV shape were assessed with Principal Component Analysis (PCA) and cross-sectional multivariate design. A generative 3-D model of RV growth was then estimated using partial least squares (PLS) and canonical correlation analysis (CCA). Applied on a retrospective population of 49 patients, cross-effects between growth and pathology could be identified. Qualitatively, the statistical findings were found realistic by cardiologists. 10-fold cross-validation demonstrated a promising generalization and stability of the growth model. Compared to PCA regression, PLS was more compact, more precise and provided better predictions.
Published in: IEEE Transactions on Medical Imaging ( Volume: 30, Issue: 9, September 2011)
Page(s): 1605 - 1616
Date of Publication: 05 April 2011

ISSN Information:

PubMed ID: 21880565

References

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