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

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.