Exploring the application of ultrasonic phased arrays for industrial process analysis

This thesis was previously held under moratorium from 25/11/19 to 25/11/21Typical industrial process analysis techniques require an optical path to exist between the measurement sensor and the process to acquire data used to optimise and control an industrial process. Ultrasonic sensing is a well-established method to measure into optically opaque structures and highly focussed images can be generated using multiple element transducer arrays. In this Thesis, such arrays are explored as a real-time imaging tool for industrial process analysis. A novel methodology is proposed to characterise the variation between consecutive ultrasonic data sets deriving from the ultrasonic hardware. The pulse-echo response corresponding to a planar back wall acoustic interface is used to infer the bandwidth, pulse length and sensitivity of each array element. This led to the development of a calibration methodology to enhance the accuracy of experimentally generated ultrasonic images. An algorithm enabling non-invasive through-steel imaging of an industrial process is demonstrated using a simulated data set. Using principal component analysis, signals corresponding to reverberations in the steel vessel wall are identified and deselected from the ultrasonic data set prior to image construction. This facilitates the quantification of process information from the image. An image processing and object tracking algorithm are presented to quantify the bubble size distribution (BSD) and bubble velocity from ultrasonic images. When tested under controlled dynamic conditions, the mean value of the BSD was predicted within 50% at 100 mms-1 and the velocity could be predicted within 30% at 100 mms-1. However, these algorithms were sensitive to the quality of the input image to represent the true bubble shape. The consolidation of these techniques demonstrates successful application of ultrasonic phased array imaging, both invasively and noninvasively, to a dynamic process stream. Key to industrial uptake of the technology are data throughput and processing, which currently limit its applicability to real-time process analysis, and low sensitivity for some non-invasive applications.Typical industrial process analysis techniques require an optical path to exist between the measurement sensor and the process to acquire data used to optimise and control an industrial process. Ultrasonic sensing is a well-established method to measure into optically opaque structures and highly focussed images can be generated using multiple element transducer arrays. In this Thesis, such arrays are explored as a real-time imaging tool for industrial process analysis. A novel methodology is proposed to characterise the variation between consecutive ultrasonic data sets deriving from the ultrasonic hardware. The pulse-echo response corresponding to a planar back wall acoustic interface is used to infer the bandwidth, pulse length and sensitivity of each array element. This led to the development of a calibration methodology to enhance the accuracy of experimentally generated ultrasonic images. An algorithm enabling non-invasive through-steel imaging of an industrial process is demonstrated using a simulated data set. Using principal component analysis, signals corresponding to reverberations in the steel vessel wall are identified and deselected from the ultrasonic data set prior to image construction. This facilitates the quantification of process information from the image. An image processing and object tracking algorithm are presented to quantify the bubble size distribution (BSD) and bubble velocity from ultrasonic images. When tested under controlled dynamic conditions, the mean value of the BSD was predicted within 50% at 100 mms-1 and the velocity could be predicted within 30% at 100 mms-1. However, these algorithms were sensitive to the quality of the input image to represent the true bubble shape. The consolidation of these techniques demonstrates successful application of ultrasonic phased array imaging, both invasively and noninvasively, to a dynamic process stream. Key to industrial uptake of the technology are data throughput and processing, which currently limit its applicability to real-time process analysis, and low sensitivity for some non-invasive applications

Ultrasound in reverberating and aberrating environments: applications to human transcranial, transabdominal, and super-resolution imaging

Ultrasound imaging in the human body is degraded by effects of reverberation and aberration. The heterogeneous acoustical properties of different tissue types distort and reflect the wavefront as it travels to the target and as the echos travel back to the transducer. Transcranial imaging, has been a persistent challenge for ultrasound because the phase aberration, reverberation and attenuation from the human skull reduce the spatial resolution, to a millimeter or more, and limit contrast. Similar challenges arise in human abdominal imaging especially for patients with a large body mass index. Identifying, quantifying, and modeling these complex mechanisms of degradation are a critical component to develop rational strategies that can improve image quality. In this work, an experimental and simulation framework, calibrated to soft tissue measurements, that isolates and characterizes the individual sources of image degradation in ultrasound imaging is established. We show that using this simulation framework we can span the parameter space of image degradation in an independent or orthogonal fashion. Such flexibility offers advantages in the generation of training databases for machine learning applications as well as the development of beamforming strategies for challenging imaging scenarios. We also explored the framework's applications to lung ultrasound imaging, where the interpretation of reverberation artefacts occurring at the pleural surface is used to determine the underlying lung pathology. Using our acoustical simulation tools, B-mode images showcasing primary clinical features used in diagnostic lung imaging were successfully reproduced. These simulations establish a clear relationship of the artifacts to known underlying anatomical structures in a quantitative way. Transcranial simulations in 2D and 3D demonstrate that reverberations, whose role was previously unappreciated, are the principal source of image contrast and resolution degradation at shallow depths below 4~cm and when scattering tissue is present. Finally in the current work, the potential improvements offered by super-resolution imaging were explored by establishing the feasibility of transcranial super-resolution imaging through an intact human skull at a frequency of 2.5~MHz, with and without applying a phase correction, using with an existing clinical transducer.Doctor of Philosoph

Digital Signal Processing

Contains summary of research and reports on sixteen research projects.U.S. Navy - Office of Naval Research (Contract N00014-75-C-0852)National Science Foundation FellowshipNATO FellowshipU.S. Navy - Office of Naval Research (Contract N00014-75-C-0951)National Science Foundation (Grant ECS79-15226)U.S. Navy - Office of Naval Research (Contract N00014-77-C-0257)Bell LaboratoriesNational Science Foundation (Grant ECS80-07102)Schlumberger-Doll Research Center FellowshipHertz Foundation FellowshipGovernment of Pakistan ScholarshipU.S. Navy - Office of Naval Research (Contract N00014-77-C-0196)U.S. Air Force (Contract F19628-81-C-0002)Hughes Aircraft Company Fellowshi

A motion-based approach to abdominal clutter reduction

In ultrasound images, clutter is a noise artifact most easily observed in anechoic or hypoechoic regions. It appears as diffuse echoes overlying anatomical structures of diagnostic importance, obscuring tissue borders and reducing image contrast. A novel clutter reduction method for abdominal images is proposed, wherein the abdominal wall is displaced during successive-frame image acquisitions. A region of clutter distal to the abdominal wall was observed to move with the abdominal wall, and finite impulse response (FIR) and blind source separation (BSS) motion filters were implemented to reduce this clutter. The proposed clutter reduction method was tested in simulated and phantom data and applied to fundamental and harmonic in vivo bladder and liver images from 2 volunteers. Results show clutter reductions ranging from 0 to 18 dB in FIR-filtered images and 9 to 27 dB in BSS-filtered images. The contrast-to-noise ratio was improved by 21 to 68% and 44 to 108% in FIR- and BSS-filtered images, respectively. Improvements in contrast ranged from 4 to 12 dB. The method shows promise for reducing clutter in other abdominal images