PARAMETERS AFFECTING THE IMAGING AND DETECTION OF MICROBUBBLES IN BLOOD

Stabilized micron-sized bubbles, known as contrast agents, are often injected into the body to enhance ultrasound imaging of blood flow. The ability to detect such bubbles in blood depends on the relative magnitude of the acoustic power backscattered from the microbubbles (‘signal’) to the power backscattered from the red blood cells (‘noise’). Erythrocytes are acoustically small (Rayleigh regime), weak scatterers, and therefore the backscatter coefficient (BSC) of blood increases as the fourth power of frequency throughout the diagnostic frequency range. Microbubbles, on the other hand, are either resonant or super-resonant in the range 5-30 MHz. Above resonance, their total scattering cross-section remains constant with increasing frequency. In the present thesis, a theoretical model of the BSC of a suspension of red blood cells is presented and compared to the BSC of Optison® contrast agent microbubbles. It is predicted that, as the frequency increases, the BSC of red blood cell suspensions eventually exceeds the BSC of the strong scattering microbubbles, leading to a dramatic reduction in signal-to-noise ratio (SNR). This decrease in SNR with increasing frequency was also confirmed experimentally by use of an active cavitation detector for different concentrations of Optison® microbubbles in erythrocyte suspensions of different hematocrits. The magnitude of the observed decrease in SNR correlated well with theoretical predictions in most cases, except for very dense suspensions of red blood cells, where it is hypothesized that the close proximity of erythrocytes inhibits the acoustic response of the microbubbles.

SCANNING ACOUSTIC MICROSCOPE FOR CHARACTERIZATION OF ARTERIAL PLAQUE

Unstable arterial plaque is likely the key component of atherosclerosis, a disease which is responsible for two-thirds of heart attacks and strokes, leading to approximately 1 million deaths in the United States. Ultrasound imaging is able to detect plaque but as of yet is not able to distinguish unstable plaque from stable plaque. In this work a scanning acoustic microscope (SAM) was implemented and validated as tool to measure the acoustic properties of a sample. The goal for the SAM is to be able to provide quantitative measurements of the acoustic properties of different plaque types, to understand the physical basis by which plaque may be identified acoustically. The SAM consists of a spherically focused transducer which operates in pulse-echo mode and is scanned in a 2D raster pattern over a sample. A plane wave analysis is presented which allows the impedance, attenuation and phase velocity of a sample to be de- termined from measurements of the echoes from the front and back of the sample. After the measurements, the attenuation and phase velocity were analysed to ensure that they were consistent with causality. The backscatter coefficient of the samples was obtained using the technique outlined by Chen et al [8]. The transducer used here was able to determine acoustic properties from 10-40 MHz. The results for the impedance, attenuation and phase velocity were validated for high and low-density polyethylene against published results. The plane wave approximation was validated by measuring the properties throughout the focal region and throughout a range of incidence angles from the transducer. The SAM was used to characterize a set of recipes for tissue-mimicking phantoms which demonstrate indepen- dent control over the impedance, attenuation, phase velocity and backscatter coefficient. An initial feasibility study on a human artery was performed.

EVALUATION OF HARMONIC MOTION ELASTOGRAPHY AND ACOUSTO-­OPTIC IMAGING FOR MONITORING LESION FORMATION BY HIGH INTENSITY FOCUSED ULTRASOUND

Malignant or benign tumors may be ablated with high‐intensity focused ultrasound (HIFU). This technique, known as focused ultrasound surgery (FUS), has been actively investigated for decades, but slow to be implemented and difficult to control due to lack of real‐time feedback during ablation. Two methods of imaging and monitoring HIFU lesions during formation were implemented simultaneously, in order to investigate the efficacy of each and to increase confidence in the detection of the lesion. The first, Acousto‐Optic Imaging (AOI) detects the increasing optical absorption and scattering in the lesion. The intensity of a diffuse optical field in illuminated tissue is mapped at the spatial resolution of an ultrasound focal spot, using the acousto‐optic effect. The second, Harmonic Motion Imaging (HMI), detects the changing stiffness in the lesion. The HIFU beam is modulated to force oscillatory motion in the tissue, and the amplitude of this motion, measured by ultrasound pulse‐echo techniques, is influenced by the stiffness. Experiments were performed on store‐bought chicken breast and freshly slaughtered bovine liver. The AOI results correlated with the onset and relative size of forming lesions much better than prior knowledge of the HIFU power and duration. For HMI, a significant artifact was discovered due to acoustic nonlinearity. The artifact was mitigated by adjusting the phase of the HIFU and imaging pulses. A more detailed model of the HMI process than previously published was made using finite element analysis. The model showed that the amplitude of harmonic motion was primarily affected by increases in acoustic attenuation and stiffness as the lesion formed and the interaction of these effects was complex and often counteracted each other. Further biological variability in tissue properties meant that changes in motion were masked by sample‐to‐sample variation. The HMI experiments predicted lesion formation in only about a quarter of the lesions made. In simultaneous AOI/HMI experiments it appeared that AOI was a more robust method for lesion detection.

Neural Modeling and Imaging of the Cortical Interactions Underlying Syllable Production

This paper describes a neural model of speech acquisition and production that accounts for a wide range of acoustic, kinematic, and neuroimaging data concerning the control of speech movements. The model is a neural network whose components correspond to regions of the cerebral cortex and cerebellum, including premotor, motor, auditory, and somatosensory cortical areas. Computer simulations of the model verify its ability to account for compensation to lip and jaw perturbations during speech. Specific anatomical locations of the model's components are estimated, and these estimates are used to simulate fMRI experiments of simple syllable production with and without jaw perturbations.