654 research outputs found
Dense nonrigid motion tracking from a sequence of velocity fields
We have addressed the problem of tracking the non-rigid motion of the heart using a sequence of velocity fields and a sequence of contours. The information from both the contours and the dense velocity fields is integrated into a deforming mesh that is placed over the myocardium at one time frame and then tracked over the entire cardiac cycle. The deformation is guided by a smoothing filter that provides a compro-mise between (i) believing the dense field velocity and the contour data when it is crisp and coherent in a local spatial and temporal sense and (ii) employing a temporally smooth cyclic model of cardiac motion when contour and velocity data are not trustworthy. The method has been carefully evaluated with simu-lated data and phantom data. Experiments with in vivo data have also been conducted.
Multi-Scale 3D Scene Flow from Binocular Stereo Sequences
Scene flow methods estimate the three-dimensional motion field for points in the world, using multi-camera video data. Such methods combine multi-view reconstruction with motion estimation. This paper describes an alternative formulation for dense scene flow estimation that provides reliable results using only two cameras by fusing stereo and optical flow estimation into a single coherent framework. Internally, the proposed algorithm generates probability distributions for optical flow and disparity. Taking into account the uncertainty in the intermediate stages allows for more reliable estimation of the 3D scene flow than previous methods allow. To handle the aperture problems inherent in the estimation of optical flow and disparity, a multi-scale method along with a novel region-based technique is used within a regularized solution. This combined approach both preserves discontinuities and prevents over-regularization – two problems commonly associated with the basic multi-scale approaches. Experiments with synthetic and real test data demonstrate the strength of the proposed approach.National Science Foundation (CNS-0202067, IIS-0208876); Office of Naval Research (N00014-03-1-0108
Analysis of cardiac motion using MRI and nonrigid image registration
Imperial Users onl
Robust Cardiac Motion Estimation using Ultrafast Ultrasound Data: A Low-Rank-Topology-Preserving Approach
Cardiac motion estimation is an important diagnostic tool to detect heart
diseases and it has been explored with modalities such as MRI and conventional
ultrasound (US) sequences. US cardiac motion estimation still presents
challenges because of the complex motion patterns and the presence of noise. In
this work, we propose a novel approach to estimate the cardiac motion using
ultrafast ultrasound data. -- Our solution is based on a variational
formulation characterized by the L2-regularized class. The displacement is
represented by a lattice of b-splines and we ensure robustness by applying a
maximum likelihood type estimator. While this is an important part of our
solution, the main highlight of this paper is to combine a low-rank data
representation with topology preservation. Low-rank data representation
(achieved by finding the k-dominant singular values of a Casorati Matrix
arranged from the data sequence) speeds up the global solution and achieves
noise reduction. On the other hand, topology preservation (achieved by
monitoring the Jacobian determinant) allows to radically rule out distortions
while carefully controlling the size of allowed expansions and contractions.
Our variational approach is carried out on a realistic dataset as well as on a
simulated one. We demonstrate how our proposed variational solution deals with
complex deformations through careful numerical experiments. While maintaining
the accuracy of the solution, the low-rank preprocessing is shown to speed up
the convergence of the variational problem. Beyond cardiac motion estimation,
our approach is promising for the analysis of other organs that experience
motion.Comment: 15 pages, 10 figures, Physics in Medicine and Biology, 201
Biomechanics-informed Neural Networks for Myocardial Motion Tracking in MRI
Image registration is an ill-posed inverse problem which often requires
regularisation on the solution space. In contrast to most of the current
approaches which impose explicit regularisation terms such as smoothness, in
this paper we propose a novel method that can implicitly learn
biomechanics-informed regularisation. Such an approach can incorporate
application-specific prior knowledge into deep learning based registration.
Particularly, the proposed biomechanics-informed regularisation leverages a
variational autoencoder (VAE) to learn a manifold for biomechanically plausible
deformations and to implicitly capture their underlying properties via
reconstructing biomechanical simulations. The learnt VAE regulariser then can
be coupled with any deep learning based registration network to regularise the
solution space to be biomechanically plausible. The proposed method is
validated in the context of myocardial motion tracking on 2D stacks of cardiac
MRI data from two different datasets. The results show that it can achieve
better performance against other competing methods in terms of motion tracking
accuracy and has the ability to learn biomechanical properties such as
incompressibility and strains. The method has also been shown to have better
generalisability to unseen domains compared with commonly used L2
regularisation schemes.Comment: The paper is early accepted by MICCAI 202
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