Recently, MR tagging techniques have been getting a lot of attention from researchers since they provide non-invasively suitable data sets for cardiac motion analysis. Several different techniques have been developed to analyze 3D cardiac wall motion from such data sets. Unfortunately, due to the lack of gold standard data sets to be tested on, most of the techniques are not fully evaluated. In this dissertation, we have developed a virtual MR tagging method which generates MR tagging images, in particular SPAMM (Spatial Modulation of Magnetization) images, on a virtual deformable model. At the initial undeformed state, the user lays out tagging planes, which are orthogonal to the image plane, by specifying their locations and spacing in-between to tag a grid on the cardiac wall. During the simulation of deforming the model, the grid tagged on the model move together to reflect the underlying tissue motion in 3D. On the image plane, the deformed grid is computed and displayed to give virtual SPAMM images. These images have the similar characteristics of SPAMM images that we would obtain using a real MR machine. The virtual images can be used to fit the model using a technique to be evaluated and to compare the results with the known, actual 3D deformation information.