Landmark detection and coupled patch registration for cardiac motion tracking

Increasing attention has been focused on the estimation of the deformation of the endocardium to aid the diagnosis of cardiac malfunction. Landmark tracking can provide sparse, anatomically relevant constraints to help establish correspondences between images being tracked or registered. However, landmarks on the endocardium are often characterized by ambiguous appearance in cardiac MR images which makes the extraction and tracking of these landmarks problematic. In this paper we propose an automatic framework to select and track a sparse set of distinctive landmarks in the presence of relatively large deformations in order to capture the endocardial motion in cardiac MR sequences. To achieve this a sparse set of the landmarks is identified using an entropy-based approach. In particular we use singular value decomposition (SVD) to reduce the search space and localise the landmarks with relatively large deformation across the cardiac cycle. The tracking of the sparse set of landmarks is performed simultaneously by optimizing a two-stage Markov Random Field (MRF) model. The tracking result is further used to initialise registration based dense motion tracking. We have applied this framework to extract a set of landmarks at the endocardial border of the left ventricle in MR image sequences from 51 subjects. Although the left ventricle undergoes a number of different deformations, we show how the radial, longitudinal motion and twisting of the endocardial surface can be captured by the proposed approach. Our experiments demonstrate that motion tracking using sparse landmarks can outperform conventional motion tracking by a substantial amount, with improvements in terms of tracking accuracy of 20:8% and 19:4% respectively.