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
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 localize 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 initialize
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.
In this paper, we present a novel approach for simultaneous motion tracking of left ventricle and coronary arteries
from cardiac Computed Tomography Angiography (CTA) images. We first use the multi-scale vesselness filter
proposed by Frangi <i>et al</i>.<sup>1</sup> to enhance vessels in the cardiac CTA images. The vessel centrelines are then extracted
as the minimal cost path from the enhanced images. The centrelines at end-diastolic (ED) are used as prior
input for the motion tracking. All other centrelines are used to evaluate the accuracy of the motion tracking. To
segment the left ventricle automatically, we perform three levels of registration using a cardiac atlas obtained
from MR images. The cardiac motion is derived from cardiac CTA sequences by using local-phase information
to derive a non-rigid registration algorithm. The CTA image at each time frame is registered to the ED frame
by maximising the proposed similarity function and following a serial registration scheme. Once the images have
been aligned, a dynamic motion model of the left ventricle can be obtained by applying the computed free-form
deformations to the segmented left ventricle at ED phase. A similar propagation method also applies to the
coronary arteries. To validate the accuracy of the motion model we compare the actual position of the coronaries
and left ventricle in each time frame with the predicted ones as estimated from the proposed tracking method.
We propose a new framework to propagate the labels in a heart atlas to the cardiac MRI images for ventricle segmentations based on image registrations. The method employs the anatomical information from the atlas as priors to constrain the initialisation between the atlas and the MRI images using region based registrations. After the initialisation which minimises the possibility of local misalignments, a fluid registration is applied to fine-tune the labelling in the atlas to the detail in the MRI images. The heart shape from the atlas does not have to be representative of that of the segmented MRI images in terms of morphological variations of the heart in this framework. In the experiments, a cadaver heart atlas and a normal heart atlas were used to register to <i>in-vivo</i> data for ventricle segmentation propagations. The results have shown that the segmentations based on the proposed method are visually acceptable, accurate (surface distance against manual segmentations is 1.0 ± 1.0 mm in healthy volunteer data, and 1.6 ± 1.8 mm in patient data), and reproducible (0.7 ± 1.0 mm) for <i>in-vivo</i> cardiac MRI images. The experiments also show that the new initialisation method can correct the local misalignments and help to avoid producing unrealistic deformations in the nonrigid registrations with 21% quantitative improvement of the segmentation accuracy.