Automatic analysis of left ventricle wall thickness using short-axis cine CMR images

Abstract

A new automatic framework for analyzing wall thickness and thickening function on short-axis cine cardiac magnetic resonance (CMR) images is proposed. Inner and outer wall borders (contours) are segmented in a CMR image with a level set deformable model. Its evolution is controlled by a stochastic speed function that accounts for an “object-background” Markov-Gibbs shape and appearance model. Found by solving a Laplace equation, point-to-point correspondences between the inner and outer borders provide initial estimates of the local wall thickness and thickening function index. Effects of segmentation errors are reduced and a 3-D continuity analysis of the left ventricle (LV) wall thickening values is performed by using the maximum a posteriori (MAP) estimates for a pairwise energy function of a generalized Gauss-Markov random field (GGMRF) probabilistic model. Experiments with in vivo CMR data confirm the robustness and accuracy of the proposed framework.