438 research outputs found
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
Learning a Generative Motion Model from Image Sequences based on a Latent Motion Matrix
We propose to learn a probabilistic motion model from a sequence of images
for spatio-temporal registration. Our model encodes motion in a low-dimensional
probabilistic space - the motion matrix - which enables various motion analysis
tasks such as simulation and interpolation of realistic motion patterns
allowing for faster data acquisition and data augmentation. More precisely, the
motion matrix allows to transport the recovered motion from one subject to
another simulating for example a pathological motion in a healthy subject
without the need for inter-subject registration. The method is based on a
conditional latent variable model that is trained using amortized variational
inference. This unsupervised generative model follows a novel multivariate
Gaussian process prior and is applied within a temporal convolutional network
which leads to a diffeomorphic motion model. Temporal consistency and
generalizability is further improved by applying a temporal dropout training
scheme. Applied to cardiac cine-MRI sequences, we show improved registration
accuracy and spatio-temporally smoother deformations compared to three
state-of-the-art registration algorithms. Besides, we demonstrate the model's
applicability for motion analysis, simulation and super-resolution by an
improved motion reconstruction from sequences with missing frames compared to
linear and cubic interpolation.Comment: accepted at IEEE TM
Fast Predictive Multimodal Image Registration
We introduce a deep encoder-decoder architecture for image deformation
prediction from multimodal images. Specifically, we design an image-patch-based
deep network that jointly (i) learns an image similarity measure and (ii) the
relationship between image patches and deformation parameters. While our method
can be applied to general image registration formulations, we focus on the
Large Deformation Diffeomorphic Metric Mapping (LDDMM) registration model. By
predicting the initial momentum of the shooting formulation of LDDMM, we
preserve its mathematical properties and drastically reduce the computation
time, compared to optimization-based approaches. Furthermore, we create a
Bayesian probabilistic version of the network that allows evaluation of
registration uncertainty via sampling of the network at test time. We evaluate
our method on a 3D brain MRI dataset using both T1- and T2-weighted images. Our
experiments show that our method generates accurate predictions and that
learning the similarity measure leads to more consistent registrations than
relying on generic multimodal image similarity measures, such as mutual
information. Our approach is an order of magnitude faster than
optimization-based LDDMM.Comment: Accepted as a conference paper for ISBI 201
Information-Theoretic Registration with Explicit Reorientation of Diffusion-Weighted Images
We present an information-theoretic approach to the registration of images
with directional information, and especially for diffusion-Weighted Images
(DWI), with explicit optimization over the directional scale. We call it
Locally Orderless Registration with Directions (LORD). We focus on normalized
mutual information as a robust information-theoretic similarity measure for
DWI. The framework is an extension of the LOR-DWI density-based hierarchical
scale-space model that varies and optimizes the integration, spatial,
directional, and intensity scales. As affine transformations are insufficient
for inter-subject registration, we extend the model to non-rigid deformations.
We illustrate that the proposed model deforms orientation distribution
functions (ODFs) correctly and is capable of handling the classic complex
challenges in DWI-registrations, such as the registration of fiber-crossings
along with kissing, fanning, and interleaving fibers. Our experimental results
clearly illustrate a novel promising regularizing effect, that comes from the
nonlinear orientation-based cost function. We show the properties of the
different image scales and, we show that including orientational information in
our model makes the model better at retrieving deformations in contrast to
standard scalar-based registration.Comment: 16 pages, 19 figure
Group-wise 3D registration based templates to study the evolution of ant worker neuroanatomy
The evolutionary success of ants and other social insects is considered to be
intrinsically linked to division of labor and emergent collective intelligence.
The role of the brains of individual ants in generating these processes,
however, is poorly understood. One genus of ant of special interest is
Pheidole, which includes more than a thousand species, most of which are
dimorphic, i.e. their colonies contain two subcastes of workers: minors and
majors. Using confocal imaging and manual annotations, it has been demonstrated
that minor and major workers of different ages of three species of Pheidole
have distinct patterns of brain size and subregion scaling. However, these
studies require laborious effort to quantify brain region volumes and are
subject to potential bias. To address these issues, we propose a group-wise 3D
registration approach to build for the first time bias-free brain atlases of
intra- and inter-subcaste individuals and automatize the segmentation of new
individuals.Comment: 10 pages, 5 figures, preprint for conference (not reviewed
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