3,430 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
Ultrasound localization microscopy to image and assess microvasculature in a rat kidney.
The recent development of ultrasound localization microscopy, where individual microbubbles (contrast agents) are detected and tracked within the vasculature, provides new opportunities for imaging the vasculature of entire organs with a spatial resolution below the diffraction limit. In stationary tissue, recent studies have demonstrated a theoretical resolution on the order of microns. In this work, single microbubbles were localized in vivo in a rat kidney using a dedicated high frame rate imaging sequence. Organ motion was tracked by assuming rigid motion (translation and rotation) and appropriate correction was applied. In contrast to previous work, coherence-based non-linear phase inversion processing was used to reject tissue echoes while maintaining echoes from very slowly moving microbubbles. Blood velocity in the small vessels was estimated by tracking microbubbles, demonstrating the potential of this technique to improve vascular characterization. Previous optical studies of microbubbles in vessels of approximately 20 microns have shown that expansion is constrained, suggesting that microbubble echoes would be difficult to detect in such regions. We therefore utilized the echoes from individual MBs as microscopic sensors of slow flow associated with such vessels and demonstrate that highly correlated, wideband echoes are detected from individual microbubbles in vessels with flow rates below 2 mm/s
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Deep learning for cardiac image segmentation: A review
Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research
Coronary Artery Segmentation and Motion Modelling
Conventional coronary artery bypass surgery requires invasive sternotomy and the
use of a cardiopulmonary bypass, which leads to long recovery period and has high
infectious potential. Totally endoscopic coronary artery bypass (TECAB) surgery
based on image guided robotic surgical approaches have been developed to allow the
clinicians to conduct the bypass surgery off-pump with only three pin holes incisions
in the chest cavity, through which two robotic arms and one stereo endoscopic camera
are inserted. However, the restricted field of view of the stereo endoscopic images leads
to possible vessel misidentification and coronary artery mis-localization. This results
in 20-30% conversion rates from TECAB surgery to the conventional approach.
We have constructed patient-specific 3D + time coronary artery and left ventricle
motion models from preoperative 4D Computed Tomography Angiography (CTA)
scans. Through temporally and spatially aligning this model with the intraoperative
endoscopic views of the patient's beating heart, this work assists the surgeon to identify
and locate the correct coronaries during the TECAB precedures. Thus this work has
the prospect of reducing the conversion rate from TECAB to conventional coronary
bypass procedures.
This thesis mainly focus on designing segmentation and motion tracking methods
of the coronary arteries in order to build pre-operative patient-specific motion models.
Various vessel centreline extraction and lumen segmentation algorithms are presented,
including intensity based approaches, geometric model matching method and
morphology-based method. A probabilistic atlas of the coronary arteries is formed
from a group of subjects to facilitate the vascular segmentation and registration procedures.
Non-rigid registration framework based on a free-form deformation model
and multi-level multi-channel large deformation diffeomorphic metric mapping are
proposed to track the coronary motion. The methods are applied to 4D CTA images
acquired from various groups of patients and quantitatively evaluated
Respiratory organ motion in interventional MRI : tracking, guiding and modeling
Respiratory organ motion is one of the major challenges in interventional MRI, particularly in interventions with therapeutic ultrasound in the abdominal region. High-intensity focused ultrasound found an application in interventional MRI for noninvasive treatments of different abnormalities. In order to guide surgical and treatment interventions, organ motion imaging and modeling is commonly required before a treatment start. Accurate tracking of organ motion during various interventional MRI procedures is prerequisite for a successful outcome and safe therapy.
In this thesis, an attempt has been made to develop approaches using focused ultrasound which could be used in future clinically for the treatment of abdominal organs, such as the liver and the kidney. Two distinct methods have been presented with its ex vivo and in vivo treatment results. In the first method, an MR-based pencil-beam navigator has been used to track organ motion and provide the motion information for acoustic focal point steering, while in the second approach a hybrid imaging using both ultrasound and magnetic resonance imaging was combined for advanced guiding capabilities.
Organ motion modeling and four-dimensional imaging of organ motion is increasingly required before the surgical interventions. However, due to the current safety limitations and hardware restrictions, the MR acquisition of a time-resolved sequence of volumetric images is not possible with high temporal and spatial resolution. A novel multislice acquisition scheme that is based on a two-dimensional navigator, instead of a commonly used pencil-beam navigator, was devised to acquire the data slices and the corresponding navigator simultaneously using a CAIPIRINHA parallel imaging method. The acquisition duration for four-dimensional dataset sampling is reduced compared to the existing approaches, while the image contrast and quality are improved as well.
Tracking respiratory organ motion is required in interventional procedures and during MR imaging of moving organs. An MR-based navigator is commonly used, however, it is usually associated with image artifacts, such as signal voids. Spectrally selective navigators can come in handy in cases where the imaging organ is surrounding with an adipose tissue, because it can provide an indirect measure of organ motion. A novel spectrally selective navigator based on a crossed-pair navigator has been developed. Experiments show the advantages of the application of this novel navigator for the volumetric imaging of the liver in vivo, where this navigator was used to gate the gradient-recalled echo sequence
Cardiac Motion Estimation with Dictionary Learning and Robust Sparse Coding in Ultrasound Imaging
Cardiac motion estimation from ultrasound images is an ill-posed problem that needs regularization to stabilize the solution. In this work, regularization is achieved by exploiting the sparseness of cardiac motion fields when decomposed in an appropriate dictionary, as well as their smoothness through a classical total variation term. The main contribution of this work is to robustify the sparse coding step in order to handle anomalies, i.e., motion patterns that significantly deviate from the expected model. The proposed approach uses an ADMM-based optimization algorithm in order to simultaneously recover the sparse representations and the outlier components. It is evaluated using two realistic simulated datasets with available ground-truth, containing native outliers and corrupted by synthetic attenuation and clutter artefacts
Atlas-Based Quantification of Myocardial Motion Abnormalities: Added-Value for Understanding the Effect of Cardiac Resynchronization Therapy
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