A statistical model for quantification and prediction of cardiac remodelling: Application to tetralogy of fallot

T. Mansi, I. Voigt, B. Leonardi, X. Pennec, S. Durrleman, M. Sermesant, H. Delingette, A. M. Taylor, Y. Boudjemline, G. Pongiglione, N. Ayache

Research output: Contribution to journalArticle

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.

Original languageEnglish
Article number5741734
Pages (from-to)1605-1616
Number of pages12
JournalIEEE Transactions on Medical Imaging
Volume30
Issue number9
DOIs
Publication statusPublished - Sep 2011

Keywords

  • Canonical correlation analysis
  • cardiac remodelling
  • currents shape representation
  • partial least squares
  • statistical shape analysis
  • tetralogy of Fallot

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Radiological and Ultrasound Technology
  • Software

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    Mansi, T., Voigt, I., Leonardi, B., Pennec, X., Durrleman, S., Sermesant, M., Delingette, H., Taylor, A. M., Boudjemline, Y., Pongiglione, G., & Ayache, N. (2011). A statistical model for quantification and prediction of cardiac remodelling: Application to tetralogy of fallot. IEEE Transactions on Medical Imaging, 30(9), 1605-1616. [5741734]. https://doi.org/10.1109/TMI.2011.2135375