Tissue tagging using magnetic resonance (MR) imaging has enabled quantitative noninvasive analysis of motion and deformation in vivo. One method for MR tissue tagging is Spatial Modulation of Magnetization (SPAMM). Manual detection and tracking of tissue tags by visual inspection remains a time-consuming and tedious process. We have developed an interactively guided semi-automated method of detecting and tracking tag intersections in cardiac MR images. A template matching approach combined 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 selected 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.
ASJC Scopus subject areas
- Radiological and Ultrasound Technology
- Computer Science Applications
- Electrical and Electronic Engineering