Analysis of four-dimensional cardiac ventricular magnetic resonance images using statistical models of ventricular shape and cardiac motion

Honghai Zhang; Nicholas Walker; Steven C. Mitchell; Matthew Thomas; Andreas Wahle; Thomas Scholz; Milan Sonka

doi:10.1117/12.653621

13 March 2006 Analysis of four-dimensional cardiac ventricular magnetic resonance images using statistical models of ventricular shape and cardiac motion

Honghai Zhang, Nicholas Walker, Steven C. Mitchell, Matthew Thomas, Andreas Wahle, Thomas Scholz, Milan Sonka

Author Affiliations +

Proceedings Volume 6143, Medical Imaging 2006: Physiology, Function, and Structure from Medical Images; 614307 (2006) https://doi.org/10.1117/12.653621
Event: Medical Imaging, 2006, San Diego, California, United States

Abstract

Conventional analysis of cardiac ventricular magnetic resonance images is performed using short axis images and does not guarantee completeness and consistency of the ventricle coverage. In this paper, a four-dimensional (4D, 3D+time) left and right ventricle statistical shape model was generated from the combination of the long axis and short axis images. Iterative mutual intensity registration and interpolation were used to merge the long axis and short axis images into isotropic 4D images and simultaneously correct existing breathing artifact. Distance-based shape interpolation and approximation were used to generate complete ventricle shapes from the long axis and short axis manual segmentations. Landmarks were automatically generated and propagated to 4D data samples using rigid alignment, distance-based merging, and B-spline transform. Principal component analysis (PCA) was used in model creation and analysis. The two strongest modes of the shape model captured the most important shape feature of Tetralogy of Fallot (TOF) patients, right ventricle enlargement. Classification of cardiac images into classes of normal and TOF subjects performed on 3D and 4D models showed 100% classification correctness rates for both normal and TOF subjects using k-Nearest Neighbor (k=1 or 3) classifier and the two strongest shape modes.

Citation Download Citation

Honghai Zhang, Nicholas Walker, Steven C. Mitchell, Matthew Thomas, Andreas Wahle, Thomas Scholz, and Milan Sonka "Analysis of four-dimensional cardiac ventricular magnetic resonance images using statistical models of ventricular shape and cardiac motion", Proc. SPIE 6143, Medical Imaging 2006: Physiology, Function, and Structure from Medical Images, 614307 (13 March 2006); https://doi.org/10.1117/12.653621

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available