SEMIAUTOMATIC TRACKING OF MYOCARDIAL MOTION IN MR TAGGED IMAGES

Tissue tagging using magnetic resonance (MR) imaging has enabled quant itative noninvasive analysis of motion and deformation in vivo. One me thod for MR tissue tagging is Spatial Modulation of Magnetization (SPA MM), Manual detection and tracking of tissue tags by visual inspection remains a time-consuming and tedious process. We have developed an in teractively guided semi-automated method of detecting and tracking tag intersections in cardiac MR images, A template matching approach comb ined with a novel adaptation of active contour modeling permits rapid analysis of MR images. We have validated our technique using MR SPAMM images of a silicone gel phantom with controlled deformations. Average discrepancy between theoretically predicted and semi-automatically se lected tag intersections was 0.30 mm +/- 0.17 [mean +/- SD, NS (P < 0. 05)]. Cardiac SPAMM images of normal volunteers and diseased patients also have been evaluated using our technique.