619 research outputs found
Fetal whole-heart 4D imaging using motion-corrected multi-planar real-time MRI
Purpose: To develop a MRI acquisition and reconstruction framework for
volumetric cine visualisation of the fetal heart and great vessels in the
presence of maternal and fetal motion.
Methods: Four-dimensional depiction was achieved using a highly-accelerated
multi-planar real-time balanced steady state free precession acquisition
combined with retrospective image-domain techniques for motion correction,
cardiac synchronisation and outlier rejection. The framework was evaluated and
optimised using a numerical phantom, and evaluated in a study of 20 mid- to
late-gestational age human fetal subjects. Reconstructed cine volumes were
evaluated by experienced cardiologists and compared with matched ultrasound. A
preliminary assessment of flow-sensitive reconstruction using the velocity
information encoded in the phase of dynamic images is included.
Results: Reconstructed cine volumes could be visualised in any 2D plane
without the need for highly-specific scan plane prescription prior to
acquisition or for maternal breath hold to minimise motion. Reconstruction was
fully automated aside from user-specified masks of the fetal heart and chest.
The framework proved robust when applied to fetal data and simulations
confirmed that spatial and temporal features could be reliably recovered.
Expert evaluation suggested the reconstructed volumes can be used for
comprehensive assessment of the fetal heart, either as an adjunct to ultrasound
or in combination with other MRI techniques.
Conclusion: The proposed methods show promise as a framework for
motion-compensated 4D assessment of the fetal heart and great vessels
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
Construction of a Statistical Atlas of the Whole Heart from a Large 4D CT Database
International audienceWe present in this work an efficient and robust framework for the construction of a high-resolution and spatio-temporal atlas of the whole heart from a database of 138 CT 4D images, the largest sample to be used for cardiac statistical modeling to date. The data is drawn from a variety of pathologies, which benefits its generalization to new subjects and physiological studies. In the proposed technique, spatial and temporal normalization based on non-rigid image registration are used to synthesize a population mean image from all CT image. With the resulting transformation, a detailed 3D mesh representation of the atlas is warped to fit all images in each subject and phase. The obtained level of anatomical detail (a total of 13 cardiac structures) and the extendability of the atlas present an advantage over most existing cardiac models published previously
Spatio-Temporal Tensor Decomposition of a Polyaffine Motion Model for a Better Analysis of Pathological Left Ventricular Dynamics
International audienceGiven that heart disease can cause abnormal motion dynamics over the cardiac cycle, which can then affect cardiac function, understanding and quantifying cardiac motion can provide insight for clinicians to aid in diagnosis, therapy planning, as well as to determine the prognosis for a given patient. The goal of this paper is to extract population-specific cardiac motion patterns from 3D displacements in order to firstly identify the mean motion behaviour in a population and secondly to describe pathology-specific motion patterns in terms of the spatial and temporal aspects of the motion. Since there are common motion patterns observed in patients suffering from the same condition, extracting these patterns can lead towards a better understanding of a disease. Quantifying cardiac motion at a population level is not a simple task since images can vary widely in terms of image quality, size, resolution and pose. To overcome this, we analyse the parameters obtained from a cardiac-specific Polyaffine motion tracking algorithm, which are aligned both spatially and temporally to a common reference space. Once all parameters are aligned, different subjects and different populations can be compared and analysed in the space of Polyaffine transformations by projecting the transformations to a reduced-order subspace in which dominant motion patterns in each population can be extracted and analysed. Using tensor decomposition allows the spatial and temporal aspects to be decoupled in order to study the different components individually. The proposed method was validated on healthy volunteers and Tetralogy of Fallot patients according to known spatial andtemporal behaviour for each population. A key advantage of the proposed method is the ability to regenerate motion sequences from the respective models, thus the models can be visualised in terms of the full motion, which allows for better understanding of the motion dynamics of different populations
Analysis of cardiac motion using MRI and nonrigid image registration
Imperial Users onl
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
Dynamic Image Processing for Guidance of Off-pump Beating Heart Mitral Valve Repair
Compared to conventional open heart procedures, minimally invasive off-pump beating heart mitral valve repair aims to deliver equivalent treatment for mitral regurgitation with reduced trauma and side effects. However, minimally invasive approaches are often limited by the lack of a direct view to surgical targets and/or tools, a challenge that is compounded by potential movement of the target during the cardiac cycle. For this reason, sophisticated image guidance systems are required in achieving procedural efficiency and therapeutic success. The development of such guidance systems is associated with many challenges. For example, the system should be able to provide high quality visualization of both cardiac anatomy and motion, as well as augmenting it with virtual models of tracked tools and targets. It should have the capability of integrating pre-operative images to the intra-operative scenario through registration techniques. The computation speed must be sufficiently fast to capture the rapid cardiac motion. Meanwhile, the system should be cost effective and easily integrated into standard clinical workflow.
This thesis develops image processing techniques to address these challenges, aiming to achieve a safe and efficient guidance system for off-pump beating heart mitral valve repair. These techniques can be divided into two categories, using 3D and 2D image data respectively. When 3D images are accessible, a rapid multi-modal registration approach is proposed to link the pre-operative CT images to the intra-operative ultrasound images. The ultrasound images are used to display the real time cardiac motion, enhanced by CT data serving as high quality 3D context with annotated features. I also developed a method to generate synthetic dynamic CT images, aiming to replace real dynamic CT data in such a guidance system to reduce the radiation dose applied to the patients. When only 2D images are available, an approach is developed to track the feature of interest, i.e. the mitral annulus, based on bi-plane ultrasound images and a magnetic tracking system. The concept of modern GPU-based parallel computing is employed in most of these approaches to accelerate the computation in order to capture the rapid cardiac motion with desired accuracy.
Validation experiments were performed on phantom, animal and human data. The overall accuracy of registration and feature tracking with respect to the mitral annulus was about 2-3mm with computation time of 60-400ms per frame, sufficient for one update per cardiac cycle. It was also demonstrated in the results that the synthetic CT images can provide very similar anatomical representations and registration accuracy compared to that of the real dynamic CT images. These results suggest that the approaches developed in the thesis have good potential for a safer and more effective guidance system for off-pump beating heart mitral valve repair
An image segmentation and registration approach to cardiac function analysis using MRI
Cardiovascular diseases (CVDs) are one of the major causes of death in the world. In recent
years, significant progress has been made in the care and treatment of patients with such
diseases. A crucial factor for this progress has been the development of magnetic resonance
(MR) imaging which makes it possible to diagnose and assess the cardiovascular function
of the patient. The ability to obtain high-resolution, cine volume images easily and safely
has made it the preferred method for diagnosis of CVDs. MRI is also unique in its ability
to introduce noninvasive markers directly into the tissue being imaged(MR tagging) during
the image acquisition process. With the development of advanced MR imaging acquisition
technologies, 3D MR imaging is more and more clinically feasible. This recent development has
allowed new potentially 3D image analysis technologies to be deployed. However, quantitative
analysis of cardiovascular system from the images remains a challenging topic.
The work presented in this thesis describes the development of segmentation and motion
analysis techniques for the study of the cardiac anatomy and function in cardiac magnetic
resonance (CMR) images. The first main contribution of the thesis is the development of a fully
automatic cardiac segmentation technique that integrates and combines a series of state-of-the-art
techniques. The proposed segmentation technique is capable of generating an accurate 3D
segmentation from multiple image sequences. The proposed segmentation technique is robust
even in the presence of pathological changes, large anatomical shape variations and locally
varying contrast in the images.
Another main contribution of this thesis is the development of motion tracking techniques that
can integrate motion information from different sources. For example, the radial motion of
the myocardium can be tracked easily in untagged MR imaging since the epi- and endocardial
surfaces are clearly visible. On the other hand, tagged MR imaging allows easy tracking of
both longitudinal and circumferential motion. We propose a novel technique based on non-rigid
image registration for the myocardial motion estimation using both untagged and 3D tagged MR
images. The novel aspect of our technique is its simultaneous use of complementary information
from both untagged and 3D tagged MR imaging. The similarity measure is spatially weighted
to maximise the utility of information from both images.
The thesis also proposes a sparse representation for free-form deformations (FFDs) using the principles of compressed sensing. The sparse free-form deformation (SFFD) model can
capture fine local details such as motion discontinuities without sacrificing robustness. We
demonstrate the capabilities of the proposed framework to accurately estimate smooth as well
as discontinuous deformations in 2D and 3D CMR image sequences. Compared to the standard
FFD approach, a significant increase in registration accuracy can be observed in datasets with
discontinuous motion patterns.
Both the segmentation and motion tracking techniques presented in this thesis have been
applied to clinical studies. We focus on two important clinical applications that can be
addressed by the techniques proposed in this thesis. The first clinical application aims
at measuring longitudinal changes in cardiac morphology and function during the cardiac
remodelling process. The second clinical application aims at selecting patients that positively
respond to cardiac resynchronization therapy (CRT).
The final chapter of this thesis summarises the main conclusions that can be drawn from the
work presented here and also discusses possible avenues for future research
A Spatiotemporal Volumetric Interpolation Network for 4D Dynamic Medical Image
Dynamic medical imaging is usually limited in application due to the large
radiation doses and longer image scanning and reconstruction times. Existing
methods attempt to reduce the dynamic sequence by interpolating the volumes
between the acquired image volumes. However, these methods are limited to
either 2D images and/or are unable to support large variations in the motion
between the image volume sequences. In this paper, we present a spatiotemporal
volumetric interpolation network (SVIN) designed for 4D dynamic medical images.
SVIN introduces dual networks: first is the spatiotemporal motion network that
leverages the 3D convolutional neural network (CNN) for unsupervised parametric
volumetric registration to derive spatiotemporal motion field from two-image
volumes; the second is the sequential volumetric interpolation network, which
uses the derived motion field to interpolate image volumes, together with a new
regression-based module to characterize the periodic motion cycles in
functional organ structures. We also introduce an adaptive multi-scale
architecture to capture the volumetric large anatomy motions. Experimental
results demonstrated that our SVIN outperformed state-of-the-art temporal
medical interpolation methods and natural video interpolation methods that have
been extended to support volumetric images. Our ablation study further
exemplified that our motion network was able to better represent the large
functional motion compared with the state-of-the-art unsupervised medical
registration methods.Comment: 10 pages, 8 figures, Conference on Computer Vision and Pattern
Recognition (CVPR) 202
Increasing the field-of-view of dynamic cardiac OCT via post-acquisition mosaicing without affecting frame-rate or spatial resolution
Optical coherence tomography (OCT) allows imaging dynamic structures and fluid flow within scattering tissue, such as the beating heart and blood flow in murine embryos. For any given system, the frame rate, spatial resolution, field-of-view (FOV), and signal-to-noise ratio (SNR) are interconnected: favoring one aspect limits at least one of the others due to optical, instrumentation, and software constraints. Here we describe a spatio-temporal mosaicing technique to reconstruct high-speed, high spatial-resolution, and large-field-of-view OCT sequences. The technique is applicable to imaging any cyclically moving structure and operates on multiple, spatially overlapping tiled image sequences (each sequence acquired sequentially at a given spatial location) and effectively decouples the (rigid) spatial alignment and (non-rigid) temporal registration problems. Using this approach we reconstructed full-frame OCT sequences of the beating embryonic rat heart (11.5 days post coitus) and compared it to direct imaging on the same system, demonstrating a six-fold improvement of the frame rate without compromising spatial resolution, FOV, or SNR
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