Electromagnetic models for ultrasound image processing

Speckle noise appears when coherent illumination is employed, as for example Laser, Synthetic Aperture Radar (SAR), Sonar, Magnetic Resonance, X-ray and Ultrasound imagery. Backscattered echoes from the randomly distributed scatterers in the microscopic structure of the medium are the origin of speckle phenomenon, which characterizes coherent imaging with a granular appearance. It can be shown that speckle noise is of multiplicative nature, strongly correlated and more importantly, with non-Gaussian statistics. These characteristics differ greatly from the traditional assumption of white additive Gaussian noise, often taken in image segmentation, filtering, and in general, image processing; which leads to reduction of the methods effectiveness for final image information extraction; therefore, this kind of noise severely impairs human and machine ability to image interpretation. Statistical modeling is of particular relevance when dealing with speckled data in order to obtain efficient image processing algorithms; but, additionally, clinical ultrasound imaging systems employ nonlinear signal processing to reduce the dynamic range of the input echo signal to match the smaller dynamic range of the display device and to emphasize objects with weak backscatter. This reduction in dynamic range is normally achieved through a logarithmic amplifier i.e. logarithmic compression, which selectively compresses large input signals. This kind of nonlinear compression totally changes the statistics of the input envelope signal; and, a closed form expression for the density function of the logarithmic transformed data is usually hard to derive. This thesis is concerned with the statistical distributions of the Log-compressed amplitude signal in coherent imagery, and its main objective is to develop a general statistical model for log-compressed ultrasound B-scan images. The developed model is adapted, making the pertinent physical analogies, from the multiplicative model in Synthetic Aperture Radar (SAR) context. It is shown that the proposed model can successfully describe log-compressed data generated from different models proposed in the specialized ultrasound image processing literature. Also, the model is successfully applied to model in-vivo echo-cardiographic (ultrasound) B-scan images. Necessary theorems are established to account for a rigorous mathematical proof of the validity and generality of the model. Additionally, a physical interpretation of the parameters is given, and the connections between the generalized central limit theorems, the multiplicative model and the compound representations approaches for the different models proposed up-to-date, are established. It is shown that the log-amplifier parameters are included as model parameters and all the model parameters are estimated using moments and maximum likelihood methods. Finally, three applications are developed: speckle noise identification and filtering; segmentation of in vivo echo-cardiographic (ultrasound) B-scan images and a novel approach for heart ejection fraction evaluationEl ruido Speckle aparece cuando se utilizan sistemas de iluminación coherente, como por ejemplo Láser, Radar de Apertura Sintética (SAR), Sonar, Resonancia Magnética, rayos X y ultrasonidos. Los ecos dispersados por los centros dispersores distribuidos al azar en la estructura microscópica del medio son el origen de este fenómeno, que caracteriza las imágenes coherentes con un aspecto granular. Se puede demostrar que el ruido Speckle es de carácter multiplicativo, fuertemente correlacionados y lo más importante, con estadística no Gaussiana. Estas características son muy diferentes de la suposición tradicional de ruido aditivo gaussiano blanco, a menudo asumida en la segmentación de imágenes, filtrado, y en general, en el procesamiento de imágenes; lo cual se traduce en la reducción de la eficacia de los métodos para la extracción de información de la imagen final. La modelización estadística es de particular relevancia cuando se trata con datos Speckle, a fin de obtener algoritmos de procesamiento de imágenes eficientes. Además, el procesamiento no lineal de señales empleado en sistemas clínicos de imágenes por ultrasonido para reducir el rango dinámico de la señal de eco de entrada de manera que coincida con el rango dinámico más pequeño del dispositivo de visualización y resaltar así los objetos con dispersión más débil, modifica radicalmente la estadística de los datos. Esta reducción en el rango dinámico se logra normalmente a través de un amplificador logarítmico es decir, la compresión logarítmica, que comprime selectivamente las señales de entrada y una forma analítica para la expresión de la función de densidad de los datos transformados logarítmicamente es por lo general difícil de derivar. Esta tesis se centra en las distribuciones estadísticas de la amplitud de la señal comprimida logarítmicamente en las imágenes coherentes, y su principal objetivo es el desarrollo de un modelo estadístico general para las imágenes por ultrasonido comprimidas logarítmicamente en modo-B. El modelo desarrollado se adaptó, realizando las analogías físicas relevantes, del modelo multiplicativo en radares de apertura sintética (SAR). El Modelo propuesto puede describir correctamente los datos comprimidos logarítmicamente a partir datos generados con los diferentes modelos propuestos en la literatura especializada en procesamiento de imágenes por ultrasonido. Además, el modelo se aplica con éxito para modelar ecocardiografías en vivo. Se enuncian y demuestran los teoremas necesarios para dar cuenta de una demostración matemática rigurosa de la validez y generalidad del modelo. Además, se da una interpretación física de los parámetros y se establecen las conexiones entre el teorema central del límite generalizado, el modelo multiplicativo y la composición de distribuciones para los diferentes modelos propuestos hasta a la fecha. Se demuestra además que los parámetros del amplificador logarítmico se incluyen dentro de los parámetros del modelo y se estiman usando los métodos estándar de momentos y máxima verosimilitud. Por último, tres aplicaciones se desarrollan: filtrado de ruido Speckle, segmentación de ecocardiografías y un nuevo enfoque para la evaluación de la fracción de eyección cardiaca.Postprint (published version

Quantitative ultrasound imaging during shear wave propagation for application related to breast cancer diagnosis

Dans le contexte de la caractérisation des tissus mammaires, on peut se demander ce que l’examen d’un attribut en échographie quantitative (« quantitative ultrasound » - QUS) d’un milieu diffusant (tel un tissu biologique mou) pendant la propagation d’une onde de cisaillement ajoute à son pouvoir discriminant. Ce travail présente une étude du comportement variable temporel de trois paramètres statistiques (l’intensité moyenne, le paramètre de structure et le paramètre de regroupement des diffuseurs) d’un modèle général pour l’enveloppe écho de l’onde ultrasonore rétrodiffusée (c.-à-d., la K-distribution homodyne) sous la propagation des ondes de cisaillement. Des ondes de cisaillement transitoires ont été générés en utilisant la mèthode d’ imagerie de cisaillement supersonique ( «supersonic shear imaging » - SSI) dans trois fantômes in-vitro macroscopiquement homogènes imitant le sein avec des propriétés mécaniques différentes, et deux fantômes ex-vivo hétérogénes avec tumeurs de souris incluses dans un milieu environnant d’agargélatine. Une comparaison de l’étendue des trois paramètres de la K-distribution homodyne avec et sans propagation d’ondes de cisaillement a montré que les paramètres étaient significativement (p < 0,001) affectès par la propagation d’ondes de cisaillement dans les expériences in-vitro et ex-vivo. Les résultats ont également démontré que la plage dynamique des paramétres statistiques au cours de la propagation des ondes de cisaillement peut aider à discriminer (avec p < 0,001) les trois fantômes homogènes in-vitro les uns des autres, ainsi que les tumeurs de souris de leur milieu environnant dans les fantômes hétérogénes ex-vivo. De plus, un modéle de régression linéaire a été appliqué pour corréler la plage de l’intensité moyenne sous la propagation des ondes de cisaillement avec l’amplitude maximale de déplacement du « speckle » ultrasonore. La régression linéaire obtenue a été significative : fantômes in vitro : R2 = 0.98, p < 0,001 ; tumeurs ex-vivo : R2 = 0,56, p = 0,013 ; milieu environnant ex-vivo : R2 = 0,59, p = 0,009. En revanche, la régression linéaire n’a pas été aussi significative entre l’intensité moyenne sans propagation d’ondes de cisaillement et les propriétés mécaniques du milieu : fantômes in vitro : R2 = 0,07, p = 0,328, tumeurs ex-vivo : R2 = 0,55, p = 0,022 ; milieu environnant ex-vivo : R2 = 0,45, p = 0,047. Cette nouvelle approche peut fournir des informations supplémentaires à l’échographie quantitative statistique traditionnellement réalisée dans un cadre statique (c.-à-d., sans propagation d’ondes de cisaillement), par exemple, dans le contexte de l’imagerie ultrasonore en vue de la classification du cancer du sein.In the context of breast tissue characterization, one may wonder what the consideration of a quantitative ultrasound (QUS) feature of a scattering medium (such as a soft biological tissue) under propagation of a shear wave adds to its discriminant power. This work presents a study of the time varying behavior of three statistical parameters (the mean intensity, the structure parameter and the clustering parameter of scatterers) of a general model for the ultrasound backscattering echo envelope (i.e., the homodyned K-distribution) under shear wave propagation. Transient shear waves were generated using the supersonic shear imaging (SSI) method in three in-vitro macroscopically homogenous breast mimicking phantoms with different mechanical properties, and two ex-vivo heterogeneous phantoms with mice tumors included in an agar gelatin surrounding medium. A comparison of the range of the three homodyned K-distribution parameters with and without shear wave propagation showed that the parameters were significantly (p < 0.001) affected by shear wave propagation in the in-vitro and ex-vivo experiments. The results also demonstrated that the dynamic range of the statistical parameters during shear wave propagation may help discriminate (with p < 0.001) the three in-vitro homogenous phantoms from each other, and also the mice tumors from their surrounding medium in the ex-vivo heterogeneous phantoms. Furthermore, a linear regression model was applied to relate the range of the mean intensity under shear wave propagation with the maximum displacement amplitude of speckle. The linear regression was found to be significant : in-vitro phantoms : R2 = 0.98, p < 0.001 ; ex-vivo tumors : R2 = 0.56, p = 0.013 ; ex-vivo surrounding medium : R2 = 0.59, p = 0.009. In contrast, the linear regression was not as significant between the mean intensity without shear wave propagation and mechanical properties of the medium : in-vitro phantoms : R2 = 0.07, p = 0.328, ex-vivo tumors : R2 =0.55, p = 0.022 ; ex-vivo surrounding medium : R2 = 0.45, p = 0.047. This novel approach may provide additional information to statistical QUS traditionally performed in a static framework (i.e., without shear wave propagation), for instance, in the context of ultrasound imaging for breast cancer classification

First-order statistical speckle models improve robustness and reproducibility of contrast-enhanced ultrasound perfusion estimates

Contrast-enhanced ultrasound (CEUS) permits the quantification and monitoring of adaptive tumor responses in the face of anti-angiogenic treatment, with the goal of informing targeted therapy. However, conventional CEUS image analysis relies on mean signal intensity as an estimate of tracer concentration in indicator-dilution modeling. This discounts additional information that may be available from the first-order speckle statistics in a CEUS image. Heterogeneous vascular networks, typical of tumor-induced angiogenesis, lead to heterogeneous contrast enhancement of the imaged tumor cross-section. To address this, a linear (B-mode) processing approach was developed to quantify the change in the first-order speckle statistics of B-mode cine loops due to the incursion of microbubbles. The technique, named the EDoF (effective degrees of freedom) method, was developed on tumor bearing mice (MDA-MB-231LN mammary fat pad inoculation) and evaluated using nonlinear (two-pulse amplitude modulated) contrast microbubble-specific images. To improve the potential clinical applicability of the technique, a second-generation compound probability density function for the statistics of two-pulse amplitude modulated contrast-enhanced ultrasound images was developed. The compound technique was tested in an antiangiogenic drug trial (bevacizumab) on tumor bearing mice (MDA-MB-231LN), and evaluated with gold-standard histology and contrast-enhanced X-ray computed tomography. The compound statistical model could more accurately discriminate anti-VEGF treated tumors from untreated tumors than conventional CEUS image. The technique was then applied to a rapid patient-derived xenograft (PDX) model of renal cell carcinoma (RCC) in the chorioallantoic membrane (CAM) of chicken embryos. The ultimate goal of the PDX model is to screen RCC patients for de novo sunitinib resistance. The analysis of the first-order speckle statistics of contrast-enhanced ultrasound cine loops provides more robust and reproducible estimates of tumor blood perfusion than conventional image analysis. Theoretically this form of analysis could quantify perfusion heterogeneity and provide estimates of vascular fractal dimension, but further work is required to determine what physiological features influence these measures. Treatment sensitivity matrices, which combine vascular measures from CEUS and power Doppler, may be suitable for screening of de novo sunitinib resistance in patients diagnosed with renal cell carcinoma. Further studies are required to assess whether this protocol can be predictive of patient outcome

Speckle Detection in Ultrasonic Images Using Unsupervised Clustering Techniques

Research for the improvement of the quality of clinical ultrasound images has been a topic of interest for researchers and physicians. One of the challenges is the presence of speckle artifacts. This dissertation reviews the speckle phenomena in such images, and develops algorithms to better identify this artifact in sonographic images. Speckle artifact is categorized into two groups: partially developed speckles and fully developed speckles (FDS). This concept has been used, along with the classification techniques, to segment the ultrasound images into patches and classify the patches in the image as FDS or non-FDS. The proposed algorithms and the results of the experiments have been validated using simulation, phantom and real data that were created for the purposes of this study or taken from other research groups. Current speckle detection methods do not optimize statistical features and they are not based on machine learning techniques. For the first time this work introduces a novel method for searching and extracting the best features for optimizing speckle detection rate using statistical machine learning and ensemble classification. Potential applications include strain imaging by tracking speckle displacement, elastography, speckle tracking and suppression applications, and needle-tracking applications.Ph.D., Biomedical Engineering -- Drexel University, 201

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

Deep Learning Methods for Estimation of Elasticity and Backscatter Quantitative Ultrasound

Ultrasound (US) imaging is increasingly attracting the attention of both academic and industrial researchers due to being a real-time and nonionizing imaging modality. It is also less expensive and more portable compared to other medical imaging techniques. However, the granular appearance hinders the interpretation of US images, hindering its wider adoption. This granular appearance (also referred to as speckles) arises from the backscattered echo from microstructural components smaller than the ultrasound wavelength, which are called scatterers. While significant effort has been undertaken to reduce the appearance of speckles, they contain scatterer properties that are highly correlated with the microstructure of the tissue that can be employed to diagnose different types of disease. There are many properties that can be extracted from speckles that are clinically valuable, such as the elasticity and organization of scatterers. Analyzing the motion of scatterers in the presence of an internal or external force can be used to obtain the elastic properties of the tissue. The technique is called elastography and has been widely used to characterize the tissue. Estimating the scatterer organization (scatterer number density and coherent to diffuse scattering power) is also crucial as it provides information about tissue microstructure and potentially aids in disease diagnosis and treatment monitoring. This thesis proposes several deep learning-based methods to facilitate and improve the estimation of speckle motion and scatterer properties, potentially simplifying the interpretation of US images. In particular, we propose new methods for displacement estimation in Chapters 2 to 6 and introduce novel techniques in Chapters 7 to 11 to quantify scatterers’ number density and organization