Long-Axis cardiac MRI contour detection with adaptive virtual exploring robot

  • Authors:

  • Mark Blok;Mikhail G. Danilouchkine;Cor J. Veenman;Faiza Admiraal-Behloul;Emile A. Hendriks;Johan H. C. Reiber;Boudewijn P. F. Lelieveldt

  • Affiliations:

  • Leiden University Medical Center, Leiden, The Netherlands;Leiden University Medical Center, Leiden, The Netherlands;Delft University of Technology, Delft, The Netherlands;Leiden University Medical Center, Leiden, The Netherlands;Delft University of Technology, Delft, The Netherlands;Leiden University Medical Center, Leiden, The Netherlands;Leiden University Medical Center, Leiden, The Netherlands

  • Venue:
  • FIMH'05 Proceedings of the Third international conference on Functional Imaging and Modeling of the Heart
  • Year:
  • 2005

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Abstract

This paper describes a method for automatic contour detection in long-axis cardiac MRI using an adaptive virtual exploring robot. The robot is a simulated trained virtual autonomous tri-cycle that is initially positioned in a binary representation of the left ventricle (LV) and finds the contours during navigation through the ventricle. The method incorporates global and local prior shape knowledge of the LV in order to adapt the navigational parameters. Together with kinematic constraints, the robot is able to avoid concave regions such as papillary muscles and navigate through narrow corridors such as the apex. Validation was performed on in-vivo multiphase long-axis cardiac MRI images of 11 subjects. Results showed good correlation between the quantitative parameters, computed from manual and automatic segmentation: for end-diastolic volume (EDV) r=0.91, for end-systolic volume (ESV) r=0.93, ejection fraction (EF) r=0.77, and LV mass (LVM) r=0.80.