1,489 research outputs found
Segmentation of the left ventricle of the heart in 3-D+t MRI data using an optimized nonrigid temporal model
Modern medical imaging modalities provide large amounts of information in both the spatial and temporal domains and the incorporation of this information in a coherent algorithmic framework is a significant challenge. In this paper, we present a novel and intuitive approach to combine 3-D spatial and temporal (3-D + time) magnetic resonance imaging (MRI) data in an integrated segmentation algorithm to extract the myocardium of the left ventricle. A novel level-set segmentation process is developed that simultaneously delineates and tracks the boundaries of the left ventricle muscle. By encoding prior knowledge about cardiac temporal evolution in a parametric framework, an expectation-maximization algorithm optimally tracks the myocardial deformation over the cardiac cycle. The expectation step deforms the level-set function while the maximization step updates the prior temporal model parameters to perform the segmentation in a nonrigid sense
Septal Flash Assessment on CRT Candidates Based on Statistical Atlases of Motion
International audienceIn this paper, we propose a complete framework for the automatic detection and quantification of abnormal heart motion patterns using Statistical Atlases of Motion built from healthy populations. The method is illustrated on CRT patients with identified cardiac dyssyn-chrony and abnormal septal motion on 2D ultrasound (US) sequences. The use of the 2D US modality guarantees that the temporal resolution of the image sequences is high enough to work under a small displacements hypothesis. Under this assumption, the computed displacement fields can be directly considered as cardiac velocities. Comparison of subjects acquired with different spatiotemporal resolutions implies the reorientation and temporal normalization of velocity fields in a common space of coordinates. Statistics are then performed on the reoriented vector fields. Results show the ability of the method to correctly detect abnormal motion patterns and quantify their distance to normality. The use of local p-values for quantifying abnormal motion patterns is believed to be a promising strategy for computing new markers of cardiac dyssynchrony for better characterizing CRT candidates
Deep learning cardiac motion analysis for human survival prediction
Motion analysis is used in computer vision to understand the behaviour of
moving objects in sequences of images. Optimising the interpretation of dynamic
biological systems requires accurate and precise motion tracking as well as
efficient representations of high-dimensional motion trajectories so that these
can be used for prediction tasks. Here we use image sequences of the heart,
acquired using cardiac magnetic resonance imaging, to create time-resolved
three-dimensional segmentations using a fully convolutional network trained on
anatomical shape priors. This dense motion model formed the input to a
supervised denoising autoencoder (4Dsurvival), which is a hybrid network
consisting of an autoencoder that learns a task-specific latent code
representation trained on observed outcome data, yielding a latent
representation optimised for survival prediction. To handle right-censored
survival outcomes, our network used a Cox partial likelihood loss function. In
a study of 302 patients the predictive accuracy (quantified by Harrell's
C-index) was significantly higher (p < .0001) for our model C=0.73 (95 CI:
0.68 - 0.78) than the human benchmark of C=0.59 (95 CI: 0.53 - 0.65). This
work demonstrates how a complex computer vision task using high-dimensional
medical image data can efficiently predict human survival
Atlas construction and image analysis using statistical cardiac models
International audienceThis paper presents a brief overview of current trends in the construction of population and multi-modal heart atlases in our group and their application to atlas-based cardiac image analysis. The technical challenges around the construction of these atlases are organized around two main axes: groupwise image registration of anatomical, motion and fiber images and construction of statistical shape models. Application-wise, this paper focuses on the extraction of atlas-based biomarkers for the detection of local shape or motion abnormalities, addressing several cardiac applications where the extracted information is used to study and grade different pathologies. The paper is concluded with a discussion about the role of statistical atlases in the integration of multiple information sources and the potential this can bring to in-silico simulations
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
Non-Rigid Groupwise Registration for Motion Estimation and Compensation in Compressed Sensing Reconstruc- tion of Breath-Hold Cardiac Cine MRI
Purpose: Compressed sensing methods with motion estimation and compensation techniques
have been proposed for the reconstruction of accelerated dynamic MRI. However, artifacts that
naturally arise in compressed sensing reconstruction procedures hinder the estimation of motion
from reconstructed images, especially at high acceleration factors. This work introduces a robust
groupwise non-rigid motion estimation technique applied to the compressed sensing reconstruction
of dynamic cardiac cine MRI sequences.
Theory and Methods: A spatio-temporal regularized, groupwise, non-rigid registration method
based on a B-splines deformation model and a least squares metric is used to estimate and to
compensate the movement of the heart in breath-hold cine acquisitions and to obtain a quasi-static
sequence with highly sparse representation in temporally transformed domains.
Results: Short axis in vivo datasets are used for validation, both original multi-coil as well as
DICOM data. Fully sampled data were retrospectively undersampled with various acceleration
factors and reconstructions were compared with the two well-known methods k-t FOCUSS and
MASTeR. The proposed method achieves higher signal to error ratio and structure similarity index
for medium to high acceleration factors.
Conclusions: Reconstruction methods based on groupwise registration show higher quality recon-
structions for cardiac cine images than the pairwise counterparts tested
Temporal Interpolation via Motion Field Prediction
Navigated 2D multi-slice dynamic Magnetic Resonance (MR) imaging enables high
contrast 4D MR imaging during free breathing and provides in-vivo observations
for treatment planning and guidance. Navigator slices are vital for
retrospective stacking of 2D data slices in this method. However, they also
prolong the acquisition sessions. Temporal interpolation of navigator slices an
be used to reduce the number of navigator acquisitions without degrading
specificity in stacking. In this work, we propose a convolutional neural
network (CNN) based method for temporal interpolation via motion field
prediction. The proposed formulation incorporates the prior knowledge that a
motion field underlies changes in the image intensities over time. Previous
approaches that interpolate directly in the intensity space are prone to
produce blurry images or even remove structures in the images. Our method
avoids such problems and faithfully preserves the information in the image.
Further, an important advantage of our formulation is that it provides an
unsupervised estimation of bi-directional motion fields. We show that these
motion fields can be used to halve the number of registrations required during
4D reconstruction, thus substantially reducing the reconstruction time.Comment: Submitted to 1st Conference on Medical Imaging with Deep Learning
(MIDL 2018), Amsterdam, The Netherland
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