319 research outputs found
Elevational Spatial Compounding for enhancing image quality in Echocardiography
INTRODUCTION: Echocardiography is commonly used in clinical practice for the real-time assessment of cardiac morphology and function. Nevertheless, due to the nature of the data acquisition, cardiac ultrasound images are often corrupted by a range of acoustic artefacts, including acoustic noise, speckle and shadowing. Spatial compounding techniques have long been recognised for their ability to suppress common ultrasound artefacts, enhancing the imaged cardiac structures. However, they require extended acquisition times as well as accurate spatio-temporal alignment of the compounded data. Elevational spatial compounding acquires and compounds adjacent partially decorrelated planes of the same cardiac structure. METHODS: This paper employs an anthropomorphic left ventricle phantom to examine the effect of acquisition parameters, such as inter-slice angular displacement and 3D sector angular range, on the elevational spatial compounding of cardiac ultrasound data. RESULTS AND CONCLUSION: Elevational spatial compounding can produce substantial noise and speckle suppression as well as visual enhancement of tissue structures even for small acquisition sector widths (2.5° to 6.5°). In addition, elevational spatial compounding eliminates the need for extended acquisition times as well as the need for temporal alignment of the compounded datasets. However, moderate spatial registration may still be required to reduce any tissue/chamber blurring side effects that may be introduced
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
Post formation processing of cardiac ultrasound data for enhancing image quality and diagnostic value
Cardiovascular diseases (CVDs) constitute a leading cause of death, including premature
death, in the developed world. The early diagnosis and treatment of CVDs is therefore of
great importance. Modern imaging modalities enable the quantification and analysis of the
cardiovascular system and provide researchers and clinicians with valuable tools for the
diagnosis and treatment of CVDs. In particular, echocardiography offers a number of
advantages, compared to other imaging modalities, making it a prevalent tool for assessing
cardiac morphology and function. However, cardiac ultrasound images can suffer from a
range of artifacts reducing their image quality and diagnostic value. As a result, there is great
interest in the development of processing techniques that address such limitations.
This thesis introduces and quantitatively evaluates four methods that enhance clinical cardiac
ultrasound data by utilising information which until now has been predominantly
disregarded. All methods introduced in this thesis utilise multiple partially uncorrelated
instances of a cardiac cycle in order to acquire the information required to suppress or
enhance certain image features. No filtering out of information is performed at any stage
throughout the processing. This constitutes the main differentiation to previous data
enhancement approaches which tend to filter out information based on some static or
adaptive selection criteria.
The first two image enhancement methods utilise spatial averaging of partially uncorrelated
data acquired through a single acoustic window. More precisely, Temporal Compounding
enhances cardiac ultrasound data by averaging partially uncorrelated instances of the imaged
structure acquired over a number of consecutive cardiac cycles. An extension to the notion of
spatial compounding of cardiac ultrasound data is 3D-to-2D Compounding, which presents a
novel image enhancement method by acquiring and compounding spatially adjacent (along
the elevation plane), partially uncorrelated, 2D slices of the heart extracted as a thin angular
sub-sector of a volumetric pyramid scan. Data enhancement introduced by both approaches
includes the substantial suppression of tissue speckle and cavity noise. Furthermore, by
averaging decorrelated instances of the same cardiac structure, both compounding methods
can enhance tissue structures, which are masked out by high levels of noise and shadowing,
increasing their corresponding tissue/cavity detectability.
The third novel data enhancement approach, referred as Dynamic Histogram Based Intensity
Mapping (DHBIM), investigates the temporal variations within image histograms of
consecutive frames in order to (i) identify any unutilised/underutilised intensity levels and
(ii) derive the tissue/cavity intensity threshold within the processed frame sequence.
Piecewise intensity mapping is then used to enhance cardiac ultrasound data. DHBIM
introduces cavity noise suppression, enhancement of tissue speckle information as well as
considerable increase in tissue/cavity contrast and detectability.
A data acquisition and analysis protocol for integrating the dynamic intensity mapping along
with spatial compounding methods is also investigated. The linear integration of DHBIM and
Temporal Compounding forms the fourth and final implemented method, which is also
quantitatively assessed. By taking advantage of the benefits and compensating for the
limitations of each individual method, the integrated method suppresses cavity noise and
tissue speckle while enhancing tissue/cavity contrast as well as the delineation of cardiac
tissue boundaries even when heavily corrupted by cardiac ultrasound artifacts.
Finally, a novel protocol for the quantitative assessment of the effect of each data
enhancement method on image quality and diagnostic value is employed. This enables the
quantitative evaluation of each method as well as the comparison between individual
methods using clinical data from 32 patients. Image quality is assessed using a range of
quantitative measures such as signal-to-noise ratio, tissue/cavity contrast and detectability
index. Diagnostic value is assessed through variations in the repeatability level of routine
clinical measurements performed on patient cardiac ultrasound scans by two experienced
echocardiographers. Commonly used clinical measures such as the wall thickness of the
Interventricular Septum (IVS) and the Left Ventricle Posterior Wall (LVPW) as well as the
cavity diameter of the Left Ventricle (LVID) and Left Atrium (LAD) are employed for
assessing diagnostic value
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
Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review
In recent years, cardiovascular diseases (CVDs) have become one of the
leading causes of mortality globally. CVDs appear with minor symptoms and
progressively get worse. The majority of people experience symptoms such as
exhaustion, shortness of breath, ankle swelling, fluid retention, and other
symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia,
cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina
are the most common CVDs. Clinical methods such as blood tests,
electrocardiography (ECG) signals, and medical imaging are the most effective
methods used for the detection of CVDs. Among the diagnostic methods, cardiac
magnetic resonance imaging (CMR) is increasingly used to diagnose, monitor the
disease, plan treatment and predict CVDs. Coupled with all the advantages of
CMR data, CVDs diagnosis is challenging for physicians due to many slices of
data, low contrast, etc. To address these issues, deep learning (DL) techniques
have been employed to the diagnosis of CVDs using CMR data, and much research
is currently being conducted in this field. This review provides an overview of
the studies performed in CVDs detection using CMR images and DL techniques. The
introduction section examined CVDs types, diagnostic methods, and the most
important medical imaging techniques. In the following, investigations to
detect CVDs using CMR images and the most significant DL methods are presented.
Another section discussed the challenges in diagnosing CVDs from CMR data.
Next, the discussion section discusses the results of this review, and future
work in CVDs diagnosis from CMR images and DL techniques are outlined. The most
important findings of this study are presented in the conclusion section
SPM to the heart: mapping of 4D continuous velocities for motion abnormality quantification
International audienceThis paper proposes to apply parallel transport and statistical atlas techniques to quantify 4D myocardial motion abnormalities. We take advantage of our previous work on cardiac motion , which provided a continuous spatiotemporal representation of velocities, to interpolate and reorient cardiac motion fields to an unbiased reference space. Abnormal motion is quantified using SPM analysis on the velocity fields, which includes a correction based on random field theory to compensate for the spatial smoothness of the velocity fields. This paper first introduces the imaging pipeline for constructing a continuous 4D velocity atlas. This atlas is then applied to quantify abnormal motion patterns in heart failure patients
Image based approach for early assessment of heart failure.
In diagnosing heart diseases, the estimation of cardiac performance indices requires accurate segmentation of the left ventricle (LV) wall from cine cardiac magnetic resonance (CMR) images. MR imaging is noninvasive and generates clear images; however, it is impractical to manually process the huge number of images generated to calculate the performance indices. In this dissertation, we introduce a novel, fast, robust, bi-directional coupled parametric deformable models that are capable of segmenting the LV wall borders using first- and second-order visual appearance features. These features are embedded in a new stochastic external force that preserves the topology of the LV wall to track the evolution of the parametric deformable models control points. We tested the proposed segmentation approach on 15 data sets in 6 infarction patients using the Dice similarity coefficient (DSC) and the average distance (AD) between the ground truth and automated segmentation contours. Our approach achieves a mean DSC value of 0.926±0.022 and mean AD value of 2.16±0.60 mm compared to two other level set methods that achieve mean DSC values of 0.904±0.033 and 0.885±0.02; and mean AD values of 2.86±1.35 mm and 5.72±4.70 mm, respectively. Also, a novel framework for assessing both 3D functional strain and wall thickening from 4D cine cardiac magnetic resonance imaging (CCMR) is introduced. The introduced approach is primarily based on using geometrical features to track the LV wall during the cardiac cycle. The 4D tracking approach consists of the following two main steps: (i) Initially, the surface points on the LV wall are tracked by solving a 3D Laplace equation between two subsequent LV surfaces; and (ii) Secondly, the locations of the tracked LV surface points are iteratively adjusted through an energy minimization cost function using a generalized Gauss-Markov random field (GGMRF) image model in order to remove inconsistencies and preserve the anatomy of the heart wall during the tracking process. Then the circumferential strains are straight forward calculated from the location of the tracked LV surface points. In addition, myocardial wall thickening is estimated by co-allocation of the corresponding points, or matches between the endocardium and epicardium surfaces of the LV wall using the solution of the 3D laplace equation. Experimental results on in vivo data confirm the accuracy and robustness of our method. Moreover, the comparison results demonstrate that our approach outperforms 2D wall thickening estimation approaches
- …