Determining velocity displacement field from cardiac image sequence

Estimation of left ventricle motion and deformation from series of images has been an area of attention in the medical image analysis and still remains an open and challenging problem. Left ventricle contractile abnormalities can be an important manifestation of coronary artery disease. The proper motion tracking of left ventricle wall can contribute to isolate the location and extent of ischemic or infarcted myocardium and constitutes a fundamental goal of image modalities, such as Nuclear Medicine. This work describes a method to automatically estimate the velocity vector field for a beating heart based on the study of variation in frequency content in a series of 2D images as time varies. The frequency analysis is performed by computing the Wigner-Ville and the Choi-Williams distributions to each image pixel, yielding the corresponding 3D-frequency spectrum. From this 3D spectrum the local velocity of each pixel is calculated by employing a multiple linear regression model. Experimental validation was carried out using synthetic phantoms that simulate translation and rotation between successive frames. Results obtained from gated SPECT perfusion studies are also presented.