A MAP framework for tag line detection in SPAMM data using Markov random fields on the B-spline solid

Magnetic resonance (MR) tagging is a technique for measuring heart deformations through creation of a stripe grid pattern on cardiac images. Typically, sets of tag surfaces are encoded in the tissue appearing as dark lines on 2D images. The B-spline solid model for tagged MRI has the advantage of tracking myocardial tissue with material coordinates. This makes it an effective model in the analysis of heart deformation. In this paper, we present a Maximum A Posteriori (MAP) framework for detecting tags with a Markov random field (MRF) defined on a sampled B-spline solid model. We formulate the tag tracking problem as MAP estimation, finding the optimal solid for the tag features present in the current image set given an initial solid for the previous frame. The framework also allows the parameters of the solid model, number of knots, and spline order to be adjusted. In this approach, fitting can start with a solid with less knots and lower spline order, and proceed to one with more knots and/or higher order to achieve more accuracy. The approach has been validated on two sets of in-vivo heart data.