Comparing Optical-Flow Based Methods for Quantification of Myocardial Deformations on RT3D Ultrasound

This paper presents a new homogeneity measure for variational segmentation with multiple level set functions. We propose to modify the quadratic homogeneity measure to trade off the convexity of the function against a faster rate of convergence. We tested in two series of experiments the performance of this new homogeneity force at converging to appropriate partitioning of brain MRI data sets, over a large range of image spatial resolution and image quality, in terms of tissue homogeneity and contrast

Cardiac Motion Analysis Based on Optical Flow on Real-Time Three-Dimensional Ultrasound Data

With relatively high frame rates and the ability to acquire volume data sets with a stationary transducer, 3D ultrasound systems, based on matrix phased array transducers, provide valuable three-dimensional information, from which quantitative measures of cardiac function can be extracted. Such analyses require segmentation and visual tracking of the left ventricular endocardial border. Due to the large size of the volumetric data sets, manual tracing of the endocardial border is tedious and impractical for clinical applications. Therefore the development of automatic methods for tracking three-dimensional endocardial motion is essential. In this study, we evaluate a four-dimensional optical flow motion tracking algorithm to determine its capability to follow the endocardial border in three dimensional ultrasound data through time. The four-dimensional optical flow method was implemented using three-dimensional correlation. We tested the algorithm on an experimental open-chest dog data set and a clinical data set acquired with a Philips' iE33 three-dimensional ultrasound machine. Initialized with left ventricular endocardial data points obtained from manual tracing at end-diastole, the algorithm automatically tracked these points frame by frame through the whole cardiac cycle. Finite element surfaces were fitted through the data points obtained by both optical flow tracking and manual tracing by an experienced observer for quantitative comparison of the results. Parameterization of the finite element surfaces was performed and maps displaying relative differences between the manual and semi-automatic methods were compared. The results showed good consistency with less than 10% difference between manual tracing and optical flow estimation on 73% of the entire surface. In addition, the optical flow motion tracking algorithm greatly reduced processing time (about 94% reduction compared to human involvement per cardiac cycle) for analyzing cardiac function in three-dimensional ultrasound data sets. A displacement field was computed from the optical flow output, and a framework for computation of dynamic cardiac information is introduced. The method was applied to a clinical data set from a heart transplant patient and dynamic measurements agreed with known physiology as well as experimental results

Quantitative validation of optical flow based myocardial strain measures using sonomicrometry

Dynamic cardiac metrics, including myocardial strains and displacements, provide a quantitative approach to evaluate cardiac function. However, in current clinical diagnosis, largely 2D strain measures are used despite that cardiac motions are complex 3D volumes over time. Recent advances in 4D ultrasound enable the capability to capture such complex motion in a single image data set. In our previous work, a 4D optical flow based motion tracking algorithm was developed to extract full 4D dynamic cardiac metrics from such 4D ultrasound data. In order to quantitatively evaluate this tracking method, in-vivo coronary artery occlusion experiments at various locations were performed on three canine hearts. Each dog was screened with 4D ultrasound and sonomicrometry data was acquired during each occlusion study. The 4D ultrasound data from these experiments was then analyzed with the tracking method and estimated principal strain measures were directly compared to those recorded by sonomicrometry. Strong agreement was observed independently for the three canine hearts. This is the first validation study of optical flow based strain estimation for 4D ultrasound with a direct comparison with sonomicrometry using in-vivo data

Coronary Occlusion Detection with 4D Optical Flow Based Strain Estimation on 4D Ultrasound

Real-time three-dimensional echocardiography (RT3DE) offers an efficient way to obtain complete 3D images of the heart over an entire cardiac cycle in just a few seconds. The complex 3D wall motion and temporal information contained in these 4D data sequences has the potential to enhance and supplement other imaging modalities for clinical diagnoses based on cardiac motion analysis. In our previous work, a 4D optical flow based method was developed to estimate dynamic cardiac metrics, including strains and displacements, from 4D ultrasound. In this study, in order to evaluate the ability of our method in detecting ischemic regions, coronary artery occlusion experiments at various locations were performed on five dogs. 4D ultrasound data acquired during these experiments were analyzed with our proposed method. Ischemic regions predicted by the outcome of strain measurements were compared to predictions from cardiac physiology with strong agreement. This is the first direct validation study of our image analysis method for biomechanical prediction and in vivo experimental outcome

Lv volume quantification via spatiotemporal analysis of real-time 3-d echocardiography

Abstract—This paper presents a method of four-dimensional (4-D) (3-D + Time) space–frequency analysis for directional denoising and enhancement of real-time three-dimensional (RT3D) ultrasound and quantitative measures in diagnostic cardiac ultrasound. Expansion of echocardiographic volumes is performed with complex exponential wavelet-like basis functions called brushlets. These functions offer good localization in time and frequency and decompose a signal into distinct patterns of oriented harmonics, which are invariant to intensity and contrast range. Deformable-model segmentation is carried out on denoised data after thresholding of transform coefficients. This process attenuates speckle noise while preserving cardiac structure location. The superiority of 4-D over 3-D analysis for decorrelating additive white noise and multiplicative speckle noise on a 4-D phantom volume expanding in time is demonstrated. Quantitative validation, computed for contours and volumes, is performed on in vitro balloon phantoms. Clinical applications of this spaciotemporal analysis tool are reported for six patient cases providing measures of left ventricular volumes and ejection fraction. Index Terms—Echocardiography, LV volume, spaciotemporal analysis, speckle denoising. I

Analysis of strain in the human left ventricle using real-time 3D echocardiography and optical flow

Cardiovascular disease (CVD) consistently ranks among the leading causes of death in the United States. The most common subtype of CVD, ischemic heart disease, is a frequent precursor of myocardial infarction and heart failure, most commonly affecting the left ventricle (LV). Today, echocardiography is regarded as the gold standard in screening, diagnosis, and monitoring of LV dysfunction. But while global assessment of LV function tends to be quantitative, cardiologists with specific expertise still perform many regional evaluations subjectively. However, a more objective and quantitative measure of regional function – myocardial strain – has been developed and widely studied using 2D echocardiography. With recent developments in real-time 3D echocardiography (RT3DE), it has become possible to measure strain in its native 3D orientation as well. Our laboratory’s earlier work introduced the Optical Flow (OF) method of strain analysis, which was validated on simulated echocardiograms as well as through animal studies. The principal goal of this thesis is to translate this OF-based method of strain estimation from the research setting to the patient’s bedside. We have performed a series of studies to evaluate the feasibility, accuracy, and reproducibility of OF-based myocardial strain estimation in a routine clinical setting. The first investigation focused on the optimization of RT3DE acquisition and the OF processing pipeline for use in human subjects. Subsequently, we evaluated the capacity of this technique to distinguish abnormal strain patterns in patients with CVD and varying degrees of LV dysfunction. Our analysis revealed that segmental strain measures obtained by OF may have better sensitivity and specificity than the more commonly used global LV strains. Our third validation study examined the reproducibility of these strain measures in both healthy and diseased populations. We established that OF-based strain measures demonstrate repeatability comparable to that achieved by the latest commercial software commonly used in clinical research to estimate 2D or 3D strain. These studies were driven in large part by the absence of a ground truth or accepted gold standard of 3D strain measurements in the human LV. However, cardiac magnetic resonance imaging has had considerable success in measuring some forms of strain in the human LV. We therefore began to develop an image-processing pipeline to derive strain estimates from a new pulse sequence called 3D-DENSE. We further sought to improve the OF pipeline by automating the process of tracking the LV border. To this end, we developed a level-set based technique which tracks the LV endocardium. Our evaluation of its performance on RT3DE data confirmed that this method performs within the limits of inter-observer variability. Overall, our pilot studies of OF-based strain estimation demonstrate that the technique possesses several promising features for improving cardiologists’ ability to quantify and interpret the complex three-dimensional deformations of the human LV

LV Volume Quantification via Spatiotemporal Analysis of Real-Time 3-D Echocardiography

This paper presents a method of four-dimensional (4-D) (3-D+Time) space-frequency analysis for directional denoising and enhancement of real-time three-dimensional (RT3D) ultrasound and quantitative measures in diagnostic cardiac ultrasound. Expansion of echocardiographic volumes is performed with complex exponential wavelet-like basis functions called brushlets. These functions offer good localization in time and frequency and decompose a signal into distinct patterns of oriented harmonics, which are invariant to intensity and contrast range. Deformable-model segmentation is carried out on denoised data after thresholding of transform coefficients. This process attenuates speckle noise while preserving cardiac structure location. The superiority of 4-D over 3-D analysis for decorrelating additive white noise and multiplicative speckle noise on a 4-D phantom volume expanding in time is demonstrated. Quantitative validation, computed for contours and volumes, is performed on in vitro balloon phantoms. Clinical applications of this spatiotemporal analysis tool are reported for six patient cases providing measures of left ventricular volumes and ejection fraction

Echocardiography

The book "Echocardiography - New Techniques" brings worldwide contributions from highly acclaimed clinical and imaging science investigators, and representatives from academic medical centers. Each chapter is designed and written to be accessible to those with a basic knowledge of echocardiography. Additionally, the chapters are meant to be stimulating and educational to the experts and investigators in the field of echocardiography. This book is aimed primarily at cardiology fellows on their basic echocardiography rotation, fellows in general internal medicine, radiology and emergency medicine, and experts in the arena of echocardiography. Over the last few decades, the rate of technological advancements has developed dramatically, resulting in new techniques and improved echocardiographic imaging. The authors of this book focused on presenting the most advanced techniques useful in today's research and in daily clinical practice. These advanced techniques are utilized in the detection of different cardiac pathologies in patients, in contributing to their clinical decision, as well as follow-up and outcome predictions. In addition to the advanced techniques covered, this book expounds upon several special pathologies with respect to the functions of echocardiography

Cardiac motion estimation in ultrasound images using a sparse representation and dictionary learning

Les maladies cardiovasculaires sont de nos jours un problème de santé majeur. L'amélioration des méthodes liées au diagnostic de ces maladies représente donc un réel enjeu en cardiologie. Le coeur étant un organe en perpétuel mouvement, l'analyse du mouvement cardiaque est un élément clé pour le diagnostic. Par conséquent, les méthodes dédiées à l'estimation du mouvement cardiaque à partir d'images médicales, plus particulièrement en échocardiographie, font l'objet de nombreux travaux de recherches. Cependant, plusieurs difficultés liées à la complexité du mouvement du coeur ainsi qu'à la qualité des images échographiques restent à surmonter afin d'améliorer la qualité et la précision des estimations. Dans le domaine du traitement d'images, les méthodes basées sur l'apprentissage suscitent de plus en plus d'intérêt. Plus particulièrement, les représentations parcimonieuses et l'apprentissage de dictionnaires ont démontré leur efficacité pour la régularisation de divers problèmes inverses. Cette thèse a ainsi pour but d'explorer l'apport de ces méthodes, qui allient parcimonie et apprentissage, pour l'estimation du mouvement cardiaque. Trois principales contributions sont présentées, chacune traitant différents aspects et problématiques rencontrées dans le cadre de l'estimation du mouvement en échocardiographie. Dans un premier temps, une méthode d'estimation du mouvement cardiaque se basant sur une régularisation parcimonieuse est proposée. Le problème d'estimation du mouvement est formulé dans le cadre d'une minimisation d'énergie, dont le terme d'attache aux données est construit avec l'hypothèse d'un bruit de Rayleigh multiplicatif. Une étape d'apprentissage de dictionnaire permet une régularisation exploitant les propriétés parcimonieuses du mouvement cardiaque, combinée à un terme classique de lissage spatial. Dans un second temps, une méthode robuste de flux optique est présentée. L'objectif de cette approche est de robustifier la méthode d'estimation développée au premier chapitre de manière à la rendre moins sensible aux éléments aberrants. Deux régularisations sont mises en oeuvre, imposant d'une part un lissage spatial et de l'autre la parcimonie des champs de mouvements dans un dictionnaire approprié. Afin d'assurer la robustesse de la méthode vis-à-vis des anomalies, une stratégie de minimisation récursivement pondérée est proposée. Plus précisément, les fonctions employées pour cette pondération sont basées sur la théorie des M-estimateurs. Le dernier travail présenté dans cette thèse, explore une méthode d'estimation du mouvement cardiaque exploitant une régularisation parcimonieuse combinée à un lissage à la fois dans les domaines spatial et temporel. Le problème est formulé dans un cadre général d'estimation de flux optique. La régularisation temporelle proposée impose des trajectoires de mouvement lisses entre images consécutives. De plus, une méthode itérative d'estimation permet d'incorporer les trois termes de régularisations, tout en rendant possible le traitement simultané d'un ensemble d'images. Dans cette thèse, les contributions proposées sont validées en employant des images synthétiques et des simulations réalistes d'images ultrasonores. Ces données avec vérité terrain permettent d'évaluer la précision des approches considérées, et de souligner leur compétitivité par rapport à des méthodes de l'état-del'art. Pour démontrer la faisabilité clinique, des images in vivo de patients sains ou atteints de pathologies sont également considérées pour les deux premières méthodes. Pour la dernière contribution de cette thèse, i.e., exploitant un lissage temporel, une étude préliminaire est menée en utilisant des données de simulation.Cardiovascular diseases have become a major healthcare issue. Improving the diagnosis and analysis of these diseases have thus become a primary concern in cardiology. The heart is a moving organ that undergoes complex deformations. Therefore, the quantification of cardiac motion from medical images, particularly ultrasound, is a key part of the techniques used for diagnosis in clinical practice. Thus, significant research efforts have been directed toward developing new cardiac motion estimation methods. These methods aim at improving the quality and accuracy of the estimated motions. However, they are still facing many challenges due to the complexity of cardiac motion and the quality of ultrasound images. Recently, learning-based techniques have received a growing interest in the field of image processing. More specifically, sparse representations and dictionary learning strategies have shown their efficiency in regularizing different ill-posed inverse problems. This thesis investigates the benefits that such sparsity and learning-based techniques can bring to cardiac motion estimation. Three main contributions are presented, investigating different aspects and challenges that arise in echocardiography. Firstly, a method for cardiac motion estimation using a sparsity-based regularization is introduced. The motion estimation problem is formulated as an energy minimization, whose data fidelity term is built using the assumption that the images are corrupted by multiplicative Rayleigh noise. In addition to a classical spatial smoothness constraint, the proposed method exploits the sparse properties of the cardiac motion to regularize the solution via an appropriate dictionary learning step. Secondly, a fully robust optical flow method is proposed. The aim of this work is to take into account the limitations of ultrasound imaging and the violations of the regularization constraints. In this work, two regularization terms imposing spatial smoothness and sparsity of the motion field in an appropriate cardiac motion dictionary are also exploited. In order to ensure robustness to outliers, an iteratively re-weighted minimization strategy is proposed using weighting functions based on M-estimators. As a last contribution, we investigate a cardiac motion estimation method using a combination of sparse, spatial and temporal regularizations. The problem is formulated within a general optical flow framework. The proposed temporal regularization enforces smoothness of the motion trajectories between consecutive images. Furthermore, an iterative groupewise motion estimation allows us to incorporate the three regularization terms, while enabling the processing of the image sequence as a whole. Throughout this thesis, the proposed contributions are validated using synthetic and realistic simulated cardiac ultrasound images. These datasets with available groundtruth are used to evaluate the accuracy of the proposed approaches and show their competitiveness with state-of-the-art algorithms. In order to demonstrate clinical feasibility, in vivo sequences of healthy and pathological subjects are considered for the first two methods. A preliminary investigation is conducted for the last contribution, i.e., exploiting temporal smoothness, using simulated data

Myocardial strain analysis with high temporal resolution MRI tagging: extended 3D motion tracking in normal and LBBB hearts

Tese de doutoramento em Biofísica, apresentada à Universidade de Lisboa através da Faculdade de Ciências, 200