Shape analysis of hypertrophic and hypertensive heart disease using MRI-based 3D surface models of left ventricular geometry

Siamak Ardekani, Saurabh Jain, Alianna Sanzi, Celia P. Corona-Villalobos, Theodore Abraham, Roselle Abraham, Stefan L. Zimmerman, Katherine C. Wu, Raimond L. Winslow, Michael I. Miller, Laurent Younes

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


The focus of this study was to develop advanced mathematical tools to construct high-resolution 3D models of left-ventricular (LV) geometry to evaluate focal geometric differences between patients with hypertrophic cardiomyopathy (HCM) and hypertensive heart disease (HHD) using cardiac magnetic resonance (MR) cross-sectional images. A limiting factor in 3D analysis of cardiac MR cross-sections is the low out-of-plane resolution of the acquired images. To overcome this problem, we have developed a mathematical framework to construct a population-based high-resolution 3D LV triangulated surface (template) in which an iterative matching algorithm maps a surface mesh of a normal heart to a set of cross-sectional contours that were extracted from short-axis cine cardiac MR images of patients who were diagnosed with either HCM or HHD. A statistical analysis was conducted on deformations that were estimated at each surface node to identify shape differences at end-diastole (ED), end-systole (ES), and motion-related shape variation from ED to ES. Some significant shape difference in radial thickness was detected at ES. Differences of LV 3D surface geometry were identified focally on the basal anterior septum wall. Further research is needed to relate these findings to the HCM morphological substrate and to design a classifier to discriminate among different etiologies of LV hypertrophy.

Original languageEnglish (US)
Pages (from-to)12-23
Number of pages12
JournalMedical image analysis
StatePublished - Apr 1 2016


  • 3D left-ventricular geometry
  • Cardiac MRI
  • Cardiomyopathy
  • Surface-to-contour mapping

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design


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