Ultrasound Image Filtering and Reconstruction Using DCT/IDCT Filter Structure

In this paper, a new recursive structure based on the convolution model of discrete cosine transform (DCT) for designing of a finite impulse response (FIR) digital filter is proposed. In our derivation, we start with the convolution model of DCT-II to use its Z-transform for the proposed filter structure perspective. Moreover, using the same algorithm, a filter base implementation of the inverse DCT (IDCT) for image reconstruction is developed. The computational time experiments of the proposed DCT/IDCT filter(s) demonstrate that the proposed filters achieve faster elapsed CPU time compared to the direct recursive structures and recursive algorithms for the DCT/IDCT with Arbitrary Length. Experimental results on clinical ultrasound images and comparisons with classical Wiener filter, non-local mean (NLM) filter and total variation (TV) algorithms are used to validate the improvements of the proposed approaches in both noise reduction and reconstruction performance for ultrasound images

DCT/IDCT filter design for ultrasound image filtering

In this paper, a new recursive structure based on the convolution model of discrete cosine transform (DCT) for designing of a finite impulse response (FIR) digital filter is proposed. In our derivation, we start with the convolution model of DCT-II to use its Z-transform for the proposed filter structure perspective. Moreover, using the same algorithm, a filter base implementation of the inverse DCT (IDCT) for image reconstruction is developed. The computational time experiments of the proposed DCT/IDCT filter(s) demonstrate that the proposed filters achieve faster elapsed CPU time compared to the others. The image filtering and reconstruction performance of the proposed approach on ultrasound images are presented to validate the theoretical framework

Characterization of carotid artery plaques using noninvasive vascular ultrasound elastography

L'athérosclérose est une maladie vasculaire complexe qui affecte la paroi des artères (par l'épaississement) et les lumières (par la formation de plaques). La rupture d'une plaque de l'artère carotide peut également provoquer un accident vasculaire cérébral ischémique et des complications. Bien que plusieurs modalités d'imagerie médicale soient actuellement utilisées pour évaluer la stabilité d'une plaque, elles présentent des limitations telles que l'irradiation, les propriétés invasives, une faible disponibilité clinique et un coût élevé. L'échographie est une méthode d'imagerie sûre qui permet une analyse en temps réel pour l'évaluation des tissus biologiques. Il est intéressant et prometteur d’appliquer une échographie vasculaire pour le dépistage et le diagnostic précoces des plaques d’artère carotide. Cependant, les ultrasons vasculaires actuels identifient uniquement la morphologie d'une plaque en termes de luminosité d'écho ou l’impact de cette plaque sur les caractéristiques de l’écoulement sanguin, ce qui peut ne pas être suffisant pour diagnostiquer l’importance de la plaque. La technique d’élastographie vasculaire non-intrusive (« noninvasive vascular elastography (NIVE) ») a montré le potentiel de détermination de la stabilité d'une plaque. NIVE peut déterminer le champ de déformation de la paroi vasculaire en mouvement d’une artère carotide provoqué par la pulsation cardiaque naturelle. En raison des différences de module de Young entre les différents tissus des vaisseaux, différents composants d’une plaque devraient présenter différentes déformations, caractérisant ainsi la stabilité de la plaque. Actuellement, les performances et l’efficacité numérique sous-optimales limitent l’acceptation clinique de NIVE en tant que méthode rapide et efficace pour le diagnostic précoce des plaques vulnérables. Par conséquent, il est nécessaire de développer NIVE en tant qu’outil d’imagerie non invasif, rapide et économique afin de mieux caractériser la vulnérabilité liée à la plaque. La procédure à suivre pour effectuer l’analyse NIVE consiste en des étapes de formation et de post-traitement d’images. Cette thèse vise à améliorer systématiquement la précision de ces deux aspects de NIVE afin de faciliter la prédiction de la vulnérabilité de la plaque carotidienne. Le premier effort de cette thèse a été dédié à la formation d'images (Chapitre 5). L'imagerie par oscillations transversales a été introduite dans NIVE. Les performances de l’imagerie par oscillations transversales couplées à deux estimateurs de contrainte fondés sur un modèle de déformation fine, soit l’ « affine phase-based estimator (APBE) » et le « Lagrangian speckle model estimator (LSME) », ont été évaluées. Pour toutes les études de simulation et in vitro de ce travail, le LSME sans imagerie par oscillation transversale a surperformé par rapport à l'APBE avec imagerie par oscillations transversales. Néanmoins, des estimations de contrainte principales comparables ou meilleures pourraient être obtenues avec le LSME en utilisant une imagerie par oscillations transversales dans le cas de structures tissulaires complexes et hétérogènes. Lors de l'acquisition de signaux ultrasonores pour la formation d'images, des mouvements hors du plan perpendiculaire au plan de balayage bidimensionnel (2-D) existent. Le deuxième objectif de cette thèse était d'évaluer l'influence des mouvements hors plan sur les performances du NIVE 2-D (Chapitre 6). À cette fin, nous avons conçu un dispositif expérimental in vitro permettant de simuler des mouvements hors plan de 1 mm, 2 mm et 3 mm. Les résultats in vitro ont montré plus d'artefacts d'estimation de contrainte pour le LSME avec des amplitudes croissantes de mouvements hors du plan principal de l’image. Malgré tout, nous avons néanmoins obtenu des estimations de déformations robustes avec un mouvement hors plan de 2.0 mm (coefficients de corrélation supérieurs à 0.85). Pour un jeu de données cliniques de 18 participants présentant une sténose de l'artère carotide, nous avons proposé d'utiliser deux jeux de données d'analyses sur la même plaque carotidienne, soit des images transversales et longitudinales, afin de déduire les mouvements hors plan (qui se sont avérés de 0.25 mm à 1.04 mm). Les résultats cliniques ont montré que les estimations de déformations restaient reproductibles pour toutes les amplitudes de mouvement, puisque les coefficients de corrélation inter-images étaient supérieurs à 0.70 et que les corrélations croisées normalisées entre les images radiofréquences étaient supérieures à 0.93, ce qui a permis de démontrer une plus grande confiance lors de l'analyse de jeu de données cliniques de plaques carotides à l'aide du LSME. Enfin, en ce qui concerne le post-traitement des images, les algorithmes NIVE doivent estimer les déformations des parois des vaisseaux à partir d’images reconstituées dans le but d’identifier les tissus mous et durs. Ainsi, le dernier objectif de cette thèse était de développer un algorithme d'estimation de contrainte avec une résolution de la taille d’un pixel ainsi qu'une efficacité de calcul élevée pour l'amélioration de la précision de NIVE (Chapitre 7). Nous avons proposé un estimateur de déformation de modèle fragmenté (SMSE) avec lequel le champ de déformation dense est paramétré avec des descriptions de transformées en cosinus discret, générant ainsi des composantes de déformations affines (déformations axiales et latérales et en cisaillement) sans opération mathématique de dérivées. En comparant avec le LSME, le SMSE a réduit les erreurs d'estimation lors des tests de simulations, ainsi que pour les mesures in vitro et in vivo. De plus, la faible mise en oeuvre de la méthode SMSE réduit de 4 à 25 fois le temps de traitement par rapport à la méthode LSME pour les simulations, les études in vitro et in vivo, ce qui pourrait permettre une implémentation possible de NIVE en temps réel.Atherosclerosis is a complex vascular disease that affects artery walls (by thickening) and lumens (by plaque formation). The rupture of a carotid artery plaque may also induce ischemic stroke and complications. Despite the use of several medical imaging modalities to evaluate the stability of a plaque, they present limitations such as irradiation, invasive property, low clinical availability and high cost. Ultrasound is a safe imaging method with a real time capability for assessment of biological tissues. It is clinically used for early screening and diagnosis of carotid artery plaques. However, current vascular ultrasound technologies only identify the morphology of a plaque in terms of echo brightness or the impact of the vessel narrowing on flow properties, which may not be sufficient for optimum diagnosis. Noninvasive vascular elastography (NIVE) has been shown of interest for determining the stability of a plaque. Specifically, NIVE can determine the strain field of the moving vessel wall of a carotid artery caused by the natural cardiac pulsation. Due to Young’s modulus differences among different vessel tissues, different components of a plaque can be detected as they present different strains thereby potentially helping in characterizing the plaque stability. Currently, sub-optimum performance and computational efficiency limit the clinical acceptance of NIVE as a fast and efficient method for the early diagnosis of vulnerable plaques. Therefore, there is a need to further develop NIVE as a non-invasive, fast and low computational cost imaging tool to better characterize the plaque vulnerability. The procedure to perform NIVE analysis consists in image formation and image post-processing steps. This thesis aimed to systematically improve the accuracy of these two aspects of NIVE to facilitate predicting carotid plaque vulnerability. The first effort of this thesis has been targeted on improving the image formation (Chapter 5). Transverse oscillation beamforming was introduced into NIVE. The performance of transverse oscillation imaging coupled with two model-based strain estimators, the affine phase-based estimator (APBE) and the Lagrangian speckle model estimator (LSME), were evaluated. For all simulations and in vitro studies, the LSME without transverse oscillation imaging outperformed the APBE with transverse oscillation imaging. Nonetheless, comparable or better principal strain estimates could be obtained with the LSME using transverse oscillation imaging in the case of complex and heterogeneous tissue structures. During the acquisition of ultrasound signals for image formation, out-of-plane motions which are perpendicular to the two-dimensional (2-D) scan plane are existing. The second objective of this thesis was to evaluate the influence of out-of-plane motions on the performance of 2-D NIVE (Chapter 6). For this purpose, we designed an in vitro experimental setup to simulate out-of-plane motions of 1 mm, 2 mm and 3 mm. The in vitro results showed more strain estimation artifacts for the LSME with increasing magnitudes of out-of-plane motions. Even so, robust strain estimations were nevertheless obtained with 2.0 mm out-of-plane motion (correlation coefficients higher than 0.85). For a clinical dataset of 18 participants with carotid artery stenosis, we proposed to use two datasets of scans on the same carotid plaque, one cross-sectional and the other in a longitudinal view, to deduce the out-of-plane motions (estimated to be ranging from 0.25 mm to 1.04 mm). Clinical results showed that strain estimations remained reproducible for all motion magnitudes since inter-frame correlation coefficients were higher than 0.70, and normalized cross-correlations between radiofrequency images were above 0.93, which indicated that confident motion estimations can be obtained when analyzing clinical dataset of carotid plaques using the LSME. Finally, regarding the image post-processing component of NIVE algorithms to estimate strains of vessel walls from reconstructed images with the objective of identifying soft and hard tissues, we developed a strain estimation method with a pixel-wise resolution as well as a high computation efficiency for improving NIVE (Chapter 7). We proposed a sparse model strain estimator (SMSE) for which the dense strain field is parameterized with Discrete Cosine Transform descriptions, thereby deriving affine strain components (axial and lateral strains and shears) without mathematical derivative operations. Compared with the LSME, the SMSE reduced estimation errors in simulations, in vitro and in vivo tests. Moreover, the sparse implementation of the SMSE reduced the processing time by a factor of 4 to 25 compared with the LSME based on simulations, in vitro and in vivo results, which is suggesting a possible implementation of NIVE in real time

Methods for Blind Estimation of Speckle Variance in SAR Images: Simulation Results and Verification for Real-Life Data

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Independent component analysis (ICA) applied to ultrasound image processing and tissue characterization

As a complicated ubiquitous phenomenon encountered in ultrasound imaging, speckle can be treated as either annoying noise that needs to be reduced or the source from which diagnostic information can be extracted to reveal the underlying properties of tissue. In this study, the application of Independent Component Analysis (ICA), a relatively new statistical signal processing tool appeared in recent years, to both the speckle texture analysis and despeckling problems of B-mode ultrasound images was investigated. It is believed that higher order statistics may provide extra information about the speckle texture beyond the information provided by first and second order statistics only. However, the higher order statistics of speckle texture is still not clearly understood and very difficult to model analytically. Any direct dealing with high order statistics is computationally forbidding. On the one hand, many conventional ultrasound speckle texture analysis algorithms use only first or second order statistics. On the other hand, many multichannel filtering approaches use pre-defined analytical filters which are not adaptive to the data. In this study, an ICA-based multichannel filtering texture analysis algorithm, which considers both higher order statistics and data adaptation, was proposed and tested on the numerically simulated homogeneous speckle textures. The ICA filters were learned directly from the training images. Histogram regularization was conducted to make the speckle images quasi-stationary in the wide sense so as to be adaptive to an ICA algorithm. Both Principal Component Analysis (PCA) and a greedy algorithm were used to reduce the dimension of feature space. Finally, Support Vector Machines (SVM) with Radial Basis Function (RBF) kernel were chosen as the classifier for achieving best classification accuracy. Several representative conventional methods, including both low and high order statistics based methods, and both filtering and non-filtering methods, have been chosen for comparison study. The numerical experiments have shown that the proposed ICA-based algorithm in many cases outperforms other algorithms for comparison. Two-component texture segmentation experiments were conducted and the proposed algorithm showed strong capability of segmenting two visually very similar yet different texture regions with rather fuzzy boundaries and almost the same mean and variance. Through simulating speckle with first order statistics approaching gradually to the Rayleigh model from different non-Rayleigh models, the experiments to some extent reveal how the behavior of higher order statistics changes with the underlying property of tissues. It has been demonstrated that when the speckle approaches the Rayleigh model, both the second and higher order statistics lose the texture differentiation capability. However, when the speckles tend to some non-Rayleigh models, methods based on higher order statistics show strong advantage over those solely based on first or second order statistics. The proposed algorithm may potentially find clinical application in the early detection of soft tissue disease, and also be helpful for better understanding ultrasound speckle phenomenon in the perspective of higher order statistics. For the despeckling problem, an algorithm was proposed which adapted the ICA Sparse Code Shrinkage (ICA-SCS) method for the ultrasound B-mode image despeckling problem by applying an appropriate preprocessing step proposed by other researchers. The preprocessing step makes the speckle noise much closer to the real white Gaussian noise (WGN) hence more amenable to a denoising algorithm such as ICS-SCS that has been strictly designed for additive WGN. A discussion is given on how to obtain the noise-free training image samples in various ways. The experimental results have shown that the proposed method outperforms several classical methods chosen for comparison, including first or second order statistics based methods (such as Wiener filter) and multichannel filtering methods (such as wavelet shrinkage), in the capability of both speckle reduction and edge preservation

Development of a Feasible Elastography Framework for Portable Ultrasound

Portable wireless ultrasound is emerging as a new ultrasound device due to the advantages such as small size, lightweight and affordable price. Its high portability allows practitioners to make diagnostic and therapeutic decisions in real-time without having to take the patients out of their environment. Recent portable ultrasound devices are equipped with sophisticated processors and image processing algorithms providing high image quality. Some of them are able to deliver multiple ultrasound modes including color Doppler, echocardiography, and endovaginal examination. Nevertheless, they are still lack of elastography functions due to the limitations in computational performance and data transfer speed via wireless communication. In order to implement the elastography function in the wireless portable ultrasound devices, this thesis proposes a new strain estimation method to significantly reduce the computation time and a compressive sensing framework to minimize the data transfer size. Firstly, a robust phase-based strain estimator (RPSE) is developed to overcome the limited hardware performance of portable ultrasound. The RPSE is not only computationally efficient but also robust to variations of the speed of sound, sampling frequency and pulse repetition. The RPSE has been compared with other representative strain estimators including time-delay, displacement-gradient, and conventional phase-based strain estimators (TSE, DSE and PSE, respectively). It has been shown that the RPSE is superior in several elastographic image quality measures, including signal-to-noise (SNRe) and contrast-to-noise (CNRe), and the computational efficiency. The study indicates that the RPSE method can deliver the acceptable level of elastography and fast computational speed for the ultrasound echo data sets from the numerical and experimental phantoms. According to the results from the numerical phantom experiment, RPSE can achieve highest values of SNRe and CNRe (around 5.22 and 47.62 dB) among all strain estimators tested, and almost 100 times higher computational efficiency than TSE and DSE (around 0.06 vs. 5.76 seconds per frame for RPSE and TSE, respectively). Secondly, as a means to reduce the large amount of ultrasound measurement data that has to be transmitted via wireless communication, the compressive sensing (CS) framework has been applied to elastography. The performance of CS is highly dependent on the selection of model basis to represent the sparse expansion as well as the reconstruction algorithm to recover the original data from the compressed signal. Therefore, it is essential to compose the optimal combination of model basis and reconstruction algorithm for CS framework to achieve the best CS performance in terms of image quality and the maximum data reduction. In this thesis, three model bases, discrete Fourier transform (FT), discrete cosine transform (DCT), and wave atoms (WA), along with two reconstruction algorithms, L1 minimization (L1) and Block sparse Bayesian learning (BSBL) are tested. Using B-mode and elastogram images of simulated numerical phantoms, the quality of CS reconstruction is assessed in terms of three image quality measures, mean absolute error (MAE), SNRe, and CNRe, at varying data reduction (subsampling) rates. The results illustrate that BSBL based CS frameworks can generally deliver much higher image quality and subsampling rate compared with L1-based ones. In particular, the CS frameworks adopting DCT and BSBL offer the best CS performance. The results also suggests that the maximum subsampling rates without causing image degradation are 40% for L1-based framework and 60% for BSBL-based framework, respectively. The contributions of this thesis help realize elastography functionality in portable ultrasound, thereby significantly expanding its utility. For example, the diagnosis of malignant lesions, even when a patient cannot be moved to hospital immediately, is possible with the portable ultrasound. Furthermore, the SPSE method and the CS framework can be individually employed for the conventional ultrasound device as well as other telemedicine applications, to enhance computational efficiency and image quality

Data Compression in Ultrasound Computed Tomography

The large amount of data in the Karlsruhe 3D Ultrasound Computed Tomography (USCT) has to be reduced. For compression of ultrasound signals, cascading bit-wise run length method and adjacent A-scans or samples based method as new lossless methods were developed. Lossy compression methods are evaluated with an image quality based scheme using the newly designed optical flow based and committee model based estimators. Finally, an optimal method with a feasible compression ratio was suggested

Study of Computational Image Matching Techniques: Improving Our View of Biomedical Image Data

Image matching techniques are proven to be necessary in various fields of science and engineering, with many new methods and applications introduced over the years. In this PhD thesis, several computational image matching methods are introduced and investigated for improving the analysis of various biomedical image data. These improvements include the use of matching techniques for enhancing visualization of cross-sectional imaging modalities such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), denoising of retinal Optical Coherence Tomography (OCT), and high quality 3D reconstruction of surfaces from Scanning Electron Microscope (SEM) images. This work greatly improves the process of data interpretation of image data with far reaching consequences for basic sciences research. The thesis starts with a general notion of the problem of image matching followed by an overview of the topics covered in the thesis. This is followed by introduction and investigation of several applications of image matching/registration in biomdecial image processing: a) registration-based slice interpolation, b) fast mesh-based deformable image registration and c) use of simultaneous rigid registration and Robust Principal Component Analysis (RPCA) for speckle noise reduction of retinal OCT images. Moving towards a different notion of image matching/correspondence, the problem of view synthesis and 3D reconstruction, with a focus on 3D reconstruction of microscopic samples from 2D images captured by SEM, is considered next. Starting from sparse feature-based matching techniques, an extensive analysis is provided for using several well-known feature detector/descriptor techniques, namely ORB, BRIEF, SURF and SIFT, for the problem of multi-view 3D reconstruction. This chapter contains qualitative and quantitative comparisons in order to reveal the shortcomings of the sparse feature-based techniques. This is followed by introduction of a novel framework using sparse-dense matching/correspondence for high quality 3D reconstruction of SEM images. As will be shown, the proposed framework results in better reconstructions when compared with state-of-the-art sparse-feature based techniques. Even though the proposed framework produces satisfactory results, there is room for improvements. These improvements become more necessary when dealing with higher complexity microscopic samples imaged by SEM as well as in cases with large displacements between corresponding points in micrographs. Therefore, based on the proposed framework, a new approach is proposed for high quality 3D reconstruction of microscopic samples. While in case of having simpler microscopic samples the performance of the two proposed techniques are comparable, the new technique results in more truthful reconstruction of highly complex samples. The thesis is concluded with an overview of the thesis and also pointers regarding future directions of the research using both multi-view and photometric techniques for 3D reconstruction of SEM images

Évaluation de la biomécanique cardiovasculaire par élastographie ultrasonore non-invasive

L’élastographie est une technique d’imagerie qui vise à cartographier in vivo les propriétés mécaniques des tissus biologiques dans le but de fournir des informations diagnostiques additionnelles. Depuis son introduction en imagerie ultrasonore dans les années 1990, l’élastographie a trouvé de nombreuses applications. Cette modalité a notamment été utilisée pour l’étude du sein, du foie, de la prostate et des artères par imagerie ultrasonore, par résonance magnétique ou en tomographie par cohérence optique. Dans le contexte des maladies cardiovasculaires, cette modalité a un fort potentiel diagnostique puisque l’athérosclérose modifie la structure des tissus biologiques et leurs propriétés mécaniques bien avant l’apparition de tout symptôme. Quelle que soit la modalité d’imagerie utilisée, l’élastographie repose sur : l’excitation mécanique du tissu (statique ou dynamique), la mesure de déplacements et de déformations induites, et l’inversion qui permet de recouvrir les propriétés mécaniques des tissus sous-jacents. Cette thèse présente un ensemble de travaux d’élastographie dédiés à l’évaluation des tissus de l’appareil cardiovasculaire. Elle est scindée en deux parties. La première partie intitulée « Élastographie vasculaire » s’intéresse aux pathologies affectant les artères périphériques. La seconde, intitulée « Élastographie cardiaque », s’adresse aux pathologies du muscle cardiaque. Dans le contexte vasculaire, l’athérosclérose modifie la physiologie de la paroi artérielle et, de ce fait, ses propriétés biomécaniques. La première partie de cette thèse a pour objectif principal le développement d’un outil de segmentation et de caractérisation mécanique des composantes tissulaires (coeur lipidique, tissus fibreux et inclusions calciques) de la paroi artérielle, en imagerie ultrasonore non invasive, afin de prédire la vulnérabilité des plaques. Dans une première étude (Chapitre 5), nous présentons un nouvel estimateur de déformations, associé à de l’imagerie ultrarapide par ondes planes. Cette nouvelle méthode d’imagerie permet d’augmenter les performances de l’élastographie non invasive. Dans la continuité de cette étude, on propose une nouvelle méthode d’inversion mécanique dédiée à l’identification et à la quantification des propriétés mécaniques des tissus de la paroi (Chapitre 6). Ces deux méthodes sont validées in silico et in vitro sur des fantômes d’artères en polymère. Dans le contexte cardiaque, les ischémies et les infarctus causés par l’athérosclérose altèrent la contractilité du myocarde et, de ce fait, sa capacité à pomper le sang dans le corps (fonction myocardique). En échocardiographie conventionnelle, on évalue généralement la fonction myocardique en analysant la dynamique des mouvements ventriculaires (vitesses et déformations du myocarde). L’abscence de contraintes physiologiques agissant sur le myocarde (contrairement à la pression sanguine qui contraint la paroi vasculaire) ne permet pas de résoudre le problème inverse et de retrouver les propriétés mécaniques du tissu. Le terme d’élastographie fait donc ici référence à l’évaluation de la dynamique des mouvements et des déformations et non à l’évaluation des propriétés mécanique du tissu. La seconde partie de cette thèse a pour principal objectif le développement de nouveaux outils d’imagerie ultrarapide permettant une meilleure évaluation de la dynamique du myocarde. Dans une première étude (Chapitre 7), nous proposons une nouvelle approche d’échocardiographie ultrarapide et de haute résolution, par ondes divergentes, couplée à de l'imagerie Doppler tissulaire. Cette combinaison, validée in vitro et in vivo, permet d’optimiser le contraste des images mode B ainsi que l’estimation des vitesses Doppler tissulaires. Dans la continuité de cette première étude, nous proposons une nouvelle méthode d’imagerie des vecteurs de vitesses tissulaires (Chapitre 8). Cette approche, validée in vitro et in vivo, associe les informations de vitesses Doppler tissulaires et le mode B ultrarapide de l’étude précédente pour estimer l’ensemble du champ des vitesses 2D à l’intérieur du myocarde.Elastography is an imaging technique that aims to map the in vivo mechanical properties of biological tissues in order to provide additional diagnostic information. Since its introduction in ultrasound imaging in the 1990s, elastography has found many applications. This method has been used for the study of the breast, liver, prostate and arteries by ultrasound imaging, magnetic resonance imaging (MRI) or optical coherence tomography (OCT). In the context of cardiovascular diseases (CVD), this modality has a high diagnostic potential as atherosclerosis, a common pathology causing cardiovascular diseases, changes the structure of biological tissues and their mechanical properties well before any symptoms appear. Whatever the imaging modality, elastography is based on: the mechanical excitation of the tissue (static or dynamic), the measurement of induced displacements and strains, and the inverse problem allowing the quantification of the mechanical properties of underlying tissues. This thesis presents a series of works in elastography for the evaluation of cardiovascular tissues. It is divided into two parts. The first part, entitled « Vascular elastography » focuses on diseases affecting peripheral arteries. The second, entitled « Cardiac elastography » targets heart muscle pathologies. In the vascular context, atherosclerosis changes the physiology of the arterial wall and thereby its biomechanical properties. The main objective of the first part of this thesis is to develop a tool that enables the segmentation and the mechanical characterization of tissues (necrotic core, fibrous tissues and calcium inclusions) in the vascular wall of the peripheral arteries, to predict the vulnerability of plaques. In a first study (Chapter 5), we propose a new strain estimator, associated with ultrafast plane wave imaging. This new imaging technique can increase the performance of the noninvasive elastography. Building on this first study, we propose a new inverse problem method dedicated to the identification and quantification of the mechanical properties of the vascular wall tissues (Chapter 6). These two methods are validated in silico and in vitro on polymer phantom mimicking arteries. In the cardiac context, myocardial infarctions and ischemia caused by atherosclerosis alter myocardial contractility. In conventional echocardiography, the myocardial function is generally evaluated by analyzing the dynamics of ventricular motions (myocardial velocities and deformations). The abscence of physiological stress acting on the myocardium (as opposed to the blood pressure which acts the vascular wall) do not allow the solving the inverse problem and to find the mechanical properties of the fabric. Elastography thus here refers to the assessment of motion dynamics and deformations and not to the evaluation of mechanical properties of the tissue. The main objective of the second part of this thesis is to develop new ultrafast imaging tools for a better evaluation of the myocardial dynamics. In a first study (Chapter 7), we propose a new approach for ultrafast and high-resolution echocardiography using diverging waves and tissue Doppler. This combination, validated in vitro and in vivo, optimize the contrast in B-mode images and the estimation of myocardial velocities with tissue Doppler. Building on this study, we propose a new velocity vector imaging method (Chapter 8). This approach combines tissue Doppler and ultrafast B-mode of the previous study to estimate 2D velocity fields within the myocardium. This original method was validated in vitro and in vivo on six healthy volunteers

Holographic Fourier domain diffuse correlation spectroscopy

Diffuse correlation spectroscopy (DCS) is a non-invasive optical modality which can be used to measure cerebral blood flow (CBF) in real-time. It has important potential applications in clinical monitoring, as well as in neuroscience and the development of a non-invasive brain-computer interface. However, a trade-off exists between the signal-to-noise ratio (SNR) and imaging depth, and thus CBF sensitivity, of this technique. Additionally, as DCS is a diffuse optical technique, it is limited by a lack of inherent depth discrimination within the illuminated region of each source-detector pair, and the CBF signal is therefore also prone to contamination by the extracerebral tissues which the light traverses. Placing a particular emphasis on scalability, affordability, and robustness to ambient light, in this work I demonstrate a novel approach which fuses the fields of digital holography and DCS: holographic Fourier domain DCS (FD-DCS). The mathematical formalism of FD-DCS is derived and validated, followed by the construction and validation (for both in vitro and in vivo experiments) of a holographic FD-DCS instrument. By undertaking a systematic SNR performance assessment and developing a novel multispeckle denoising algorithm, I demonstrate the highest SNR gain reported in the DCS literature to date, achieved using scalable and low-cost camera-based detection. With a view to generating a forward model for holographic FD-DCS, in this thesis I propose a novel framework to simulate statistically accurate time-integrated dynamic speckle patterns in biomedical optics. The solution that I propose to this previously unsolved problem is based on the Karhunen-Loève expansion of the electric field, and I validate this technique against novel expressions for speckle contrast for different forms of homogeneous field. I also show that this method can readily be extended to cases with spatially varying sample properties, and that it can also be used to model optical and acoustic parameters