Conventional analysis of cardiac ventricular function from magnetic resonance images is typically relying on
short axis image information only. Usually, two cardiac phases of the cardiac cycle are analyzed- the end-diastole
and end-systole. Unfortunately, the short axis ventricular coverage is incomplete and inconsistent due to
the lack of image information about the ventricular apex and base. In routine clinical images, this information is
only available in long axis image planes. Additionally, the standard ventricular function indices such as ejection
fraction are only based on a limited temporal information and therefore do not fully describe the four-dimensional
(4D, 3D+time) nature of the heart's motion. We report a novel approach in which the long and short axis image
data are fused to correct for respiratory motion and form a spatio-temporal 4D data sequence with cubic voxels.
To automatically segment left and right cardiac ventricles, a 4D active appearance model was built. Applying
the method to cardiac segmentation of tetralogy of Fallot (TOF) and normal hearts, our method achieved mostly
subvoxel signed surface positioning errors of 0.2±1.1 voxels for normal left ventricle, 0.6±1.5 voxels for normal
right ventricle, 0.5±2.1 voxels for TOF left ventricle, and 1.3±2.6 voxels for TOF right ventricle. Using the
computer segmentation results, the cardiac shape and motion indices and volume-time curves were derived as
novel indices describing the ventricular function in 4D.