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A pose estimation method is proposed for measuring the position and orientation of a biopsy needle. The technique is to be used as a touchless needle guide system for guidance of percutaneous procedures with 4D ultrasound. A pair of uncalibrated, light-weight USB cameras are used as inputs. A database is prepared offline, using both the needle line estimated from camera-captured images and the true needle line recorded from an independent tracking device. A nonparametric learning algorithm determines the best fit model from the database. This model can then be used in real-time to estimate the true position of the needle with inputs from only the camera images. Simulation results confirm the feasibility of the method and show how a small, accurately made database can provide satisfactory results. In a series of tests with cameras, we achieved an average error of 2.4mm in position and 2.61° in orientation. The system is also extended to real ultrasound imaging, as the two miniature cameras capture images of the needle in air and the ultrasound system captures a volume as the needle moves through the workspace. A new database is created with the estimated 3D position of the needle from the ultrasound volume and the 2D position and orientation of the needle calculated from the camera images. This study achieved an average error of 0.94 mm in position and 3.93° in orientation.

A pose estimation method is proposed for measuring the position and orientation of a biopsy needle. The technique is to be used as a touchless needle guide system for guidance of percutaneous procedures with 4D ultrasound. A pair of uncalibrated, light-weight USB cameras are used as inputs. A database is prepared offline, using both the needle line estimated from camera-captured images and the true needle line recorded from an independent tracking device. A nonparametric learning algorithm determines the best fit model from the database. This model can then be used in real-time to estimate the true position of the needle with inputs from only the camera images. Simulation results confirm the feasibility of the method and show how a small, accurately made database can provide satisfactory results. In a series of tests with cameras, we achieved an average error of 2.4mm in position and 2.61° in orientation. The system is also extended to real ultrasound imaging, as the two miniature cameras capture images of the needle in air and the ultrasound system captures a volume as the needle moves through the workspace. A new database is created with the estimated 3D position of the needle from the ultrasound volume and the 2D position and orientation of the needle calculated from the camera images. This study achieved an average error of 0.94 mm in position and 3.93° in orientation.

A pose estimation method is proposed for measuring the position and orientation of a biopsy needle. The technique is to be used as a touchless needle guide system for guidance of percutaneous procedures with 4D ultrasound. A pair of uncalibrated, light-weight USB cameras are used as inputs. A database is prepared offline, using both the needle line estimated from camera-captured images and the true needle line recorded from an independent tracking device. A nonparametric learning algorithm determines the best fit model from the database. This model can then be used in real-time to estimate the true position of the needle with inputs from only the camera images. Simulation results confirm the feasibility of the method and show how a small, accurately made database can provide satisfactory results. In a series of tests with cameras, we achieved an average error of 2.4mm in position and 2.61° in orientation. The system is also extended to real ultrasound imaging, as the two miniature cameras capture images of the needle in air and the ultrasound system captures a volume as the needle moves through the workspace. A new database is created with the estimated 3D position of the needle from the ultrasound volume and the 2D position and orientation of the needle calculated from the camera images. This study achieved an average error of 0.94 mm in position and 3.93° in orientation. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate

The objective of this research is to develop large signal modeling and optimization methods for Capacitive Micromachined Ultrasonic Transducers (CMUTs), especially when they are used in an array configuration. General modeling and optimization methods that cover a large domain of CMUT designs are crucial, as many membrane and array geometry combinations are possible using existing microfabrication technologies. Currently, large signal modeling methods for CMUTs are not well established and nonlinear imaging techniques utilizing linear piezoelectric transducers are not applicable to CMUTs because of their strong nonlinearity. In this work, the nonlinear CMUT behavior is studied, and a feedback linearization method is proposed to reduce the CMUT nonlinearity. This method is shown to improve the CMUT performance for continuous wave applications, such as high-intensity focused ultrasound or harmonic imaging, where transducer linearity is crucial. In the second part of this dissertation, a large signal model is developed that is capable of transient modeling of CMUT arrays with arbitrary electrical terminations. The developed model is suitable for iterative design optimization of CMUTs and CMUT based imaging systems with arbitrary membrane and array geometries for a variety of applications. Finally, a novel multi-pulse method for nonlinear tissue and contrast agent imaging with CMUTs is presented. It is shown that the nonlinear content can be successfully extracted from echo signals in a CMUT based imaging system using a multiple pulse scheme. The proposed method is independent of the CMUT geometry and valid for large signal operation. Experimental results verifying the developed large signal CMUT array model, proposed gap feedback and multi-pulse techniques are also presented.

Palatalization refers to a type of coarticulation in which the place of articulation of some sound is closer to the palate than otherwise expected, very often triggered by adjacent palatal segments. It has been known as one of the most dynamic phonological phenomena in phonetic and phonological research, but the articulatory nature of palatalization still merits further investigation. This dissertation investigates the articulatory patterns of palatalization in Korean, English, and Scottish Gaelic (Gàidhlig), all of which are typologically distinct from one another and exhibit both language-universal and language-specific palatalization processes. The main question asked in this dissertation is which articulatory properties of palatalization are universal across languages, and specific to languages or to individuals. Three production experiments using ultrasound imaging technology were conducted to capture tongue gestures of speakers from three different language groups. The results from 30 speakers in the three language groups show that both phonemic and phonetic plain vs. palatalized differences manifest gesturally. Furthermore, the results show that there is a significant amount of articulatory variability across languages and speakers, yielding no clear universal "palatal" gesture, but some articulatory strategies seem to be shared by speakers from different languages. The theoretical and empirical implications of the findings are discussed.

Ultrasound (US) imaging has made significant progress over the past 20 years in relation to its role in inflammatory arthritis, and in particular, Rheumatoid Arthritis. Modern US machines provide crisp, detailed images of superficial anatomical structures which has facilitated the uptake of US imaging as an important assessment tool within the Rheumatology department. Diagnostic and prognostic information can now assist clinicians decisions with the goal of improving patient treatment and subsequent outcome. In addition, 3D US imaging has recently been suggested as an additional imaging modality with potential benefits in the assessment of in?ammatory arthritis. Recent work has focused on providing a reliable, responsive US joint count which can be assimilated into routine care as well as providing a platform for clinical research. Thus, my first aim was to show that a defined limited US data set, including 2D and 3D imaging, shows acceptable reliability. I demonstrate that both imaging modalities are reliable in terms of reading and image acquisition when restricted to a limited US data set. My second aim, was to demonstrate that a limited US data set is responsive. Using both a physiological and pharmacological trigger, I demonstrate that both 2D and 3D imaging are responsive and that combining US endpoints with DAS28 (Disease Activity Score - 28) increased the effect size and identifies treatment effects early. Despite notable advances in musculoskeletal US research, there is still need for better understanding of the pathophysiological correlates of ultrasound imaging. Therefore my final aim was to examine the relationship of Power Doppler Signal (PDS) and gray-scale synovial thickening with histological features of synovitis at a single joint level and with an extended joint US data set. I Firstly show that the harvesting of synovial tissue, using a minimally invasive US-guided biopsy technique, is safe and well tolerated by patients and that the quality of tissue and RNA extracted is good. Using this tissue collection method, I demonstrate a good correlation of US and histological parameters of synovitis (specifically CD68+ sub-lining macrophages) at a single joint level, in both an early and established RA cohort. This relationship is maintained if the US assessment is extended to a discrete US joint data set. Furthermore, within the knee joint I demonstrated that PDS correlates well with synovial tissue expression of inflammatory mediators of neoangiogenesis and histological assessment of synovial vascular area.

Outlining of the prostate boundary in ultrasound images is a very useful procedure performed and subsequently used by clinicians. The contribution of the resulting segmentation is twofold. First of all, the segmentation of the prostate glands can be used to analyze the size, geometry, and volume of the gland. Such analysis is useful as it is known that the former quantities used in conjunction with a PSA blood test can be used as an indicator of malignancy in the gland itself. The second purpose of accurate segmentation is for treatment planning purposes. In brachetherapy, commonly used to treat localized prostate cancer, the accurate location of the prostate must be found so that the radioactive seeds can be placed precisely in the malignant regions. Unfortunately, the current method of segmentation of ultrasound images is performed manually by expert radiologists. Due to the abundance of ultrasound data, the process of manual segmentation can be extremely time consuming and inefficient. A much more desirable way to perform the segmentation process is through automatic procedures, which should be able to accurately and efficiently extract the boundary of the prostate gland with minimal user intervention. This is the ultimate goal of the proposed approach. The proposed segmentation algorithm uses a probability distribution tracking framework to accurately and efficiently perform the task at hand. The basis for this methodology is to extract image and shape features from available manually segmented ultrasound images for which the actual prostate region is known. Then, the segmentation algorithm seeks a region in new ultrasound images whose features closely mirror the learned features of known prostate regions. Promising results were achieved using this method in a series of in silico and in vivo experiments.

Outlining of the prostate boundary in ultrasound images is a very useful procedure performed and subsequently used by clinicians. The contribution of the resulting segmentation is twofold. First of all, the segmentation of the prostate glands can be used to analyze the size, geometry, and volume of the gland. Such analysis is useful as it is known that the former quantities used in conjunction with a PSA blood test can be used as an indicator of malignancy in the gland itself. The second purpose of accurate segmentation is for treatment planning purposes. In brachetherapy, commonly used to treat localized prostate cancer, the accurate location of the prostate must be found so that the radioactive seeds can be placed precisely in the malignant regions. Unfortunately, the current method of segmentation of ultrasound images is performed manually by expert radiologists. Due to the abundance of ultrasound data, the process of manual segmentation can be extremely time consuming and inefficient. A much more desirable way to perform the segmentation process is through automatic procedures, which should be able to accurately and efficiently extract the boundary of the prostate gland with minimal user intervention. This is the ultimate goal of the proposed approach. The proposed segmentation algorithm uses a probability distribution tracking framework to accurately and efficiently perform the task at hand. The basis for this methodology is to extract image and shape features from available manually segmented ultrasound images for which the actual prostate region is known. Then, the segmentation algorithm seeks a region in new ultrasound images whose features closely mirror the learned features of known prostate regions. Promising results were achieved using this method in a series of in silico and in vivo experiments.

Ultrasound imaging plays an important role in biomedical diagnostics due its safety, noninvasive nature, and low cost. Conventional ultrasound systems typically form an image frame by scanning the region of interest line-by-line, using a focused beam during transmission and dynamic focusing during reception. Alternatively, the region of interest can be insonified at once using a plane wave, which allows for ultrafast data acquisition rates but reduces the resulting image quality. The latter can be improved by means of coherent plane-wave compounding (CPWC), whereby multiple plane waves are emitted at different angles to obtain multiple image datasets that are subsequently combined to enhance the final compounded image. We present two novel Fourier-domain techniques for CPWC image reconstruction from raw linear-array sensor data. In particular, we show how to modify two classic algorithms used for geophysical data processing, namely Stolt's and slant-stack depth migration under zero-offset constant-velocity assumptions, so that their new versions become applicable to plane-wave ultrasound data processing. To demonstrate the merits and limitations of our approach, we provide qualitative and quantitative comparisons with other Fourier-domain methods reported in the ultrasound literature. Our evaluation results are based on the image resolution, contrast, and similarity metrics obtained for several public-domain experimental benchmark datasets. We also describe another novel Fourier-domain method for CPWC image reconstruction that can be used in situations where the speed of sound varies with depth in a layered propagation medium. Our technique builds on Gazdag's phase-shift migration algorithm that has been modified to handle plane-wave ultrasound data processing. Our simulation results show that the proposed method is capable of accurately imaging point targets in a three-layer medium, mimicking tissue-bone-tissue ultrasound propagation. / Graduate