Thesis (M.S. in Engineering Acoustics and M.S. in Applied Science (Antisubmarine Warfare))--Naval Postgraduate School, September 1990. / Thesis Advisor(s): Baker, Steven R. Second Reader: Wilson, Oscar B. "September 1990." Description based on title screen as viewed on December 17, 2009. DTIC Identifier(s): Acoustic resonators, acoustic transducers, coupled network analysis, theses. Author(s) subject terms: Low frequency active, multiple scatter, sympathetic resonance, quasiresonance, coupled network analysis. Includes bibliographical references (p. 54). Also available in print.

Thesis (Ph. D.)--University of Queensland, 2002. / Includes bibliographical references.

Thesis (Ph. D.) -- California Institute of Technology, 2010. / Title from home page (viewed 2/25/2010). Advisor and committee chair names found in the thesis' metadata record in the digital repository. Includes bibliographical references.

Thesis (Ph. D.)--Ohio State University, 2003. / Title from first page of PDF file. Document formatted into pages; contains xxx, 268 p.: ill. Includes abstract and vita. Advisor: Robert Lee, Dept. of Electrical Engineering. Includes bibliographical references (p. 256-268).

Thesis (Ph. D.)--University of Texas at Austin, 2002. / Vita. Includes bibliographical references. Available also from UMI Company.

Thesis (Ph.D.)--University of Central Florida, 2009. / Adviser: Viatcheslav Kokoouline. Includes bibliographical references (p. 125-138).

published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy

Magnetic resonance (MR) is a well-known non-invasive technique that provides spectra (by MR spectroscopy, MRS) and images (by magnetic resonance imaging, MRI) of the examined tissue with detailed metabolic, structural, and functional information. This doctoral work is focused on advanced methodologies and applications of MRS for probing cellular and molecular changes in vivo. A single-voxel diffusion-weighted (DW) MRS method was first developed for monitoring the size changes of intramyocellular lipid droplets in vivo. This DWMRS technique was then utilized for exploring the vascular origins of the functional blood-oxygen-level-dependent (BOLD) signal. Magnetic resonance spectroscopic imaging (MRSI) enables simultaneous MRS acquisition in multiple voxels. However, MRSI is conventionally time-consuming. Therefore, a compressed sensing (CS) method was proposed in this thesis to accelerate the acquisition speed of the in vivo MRSI. It holds the potential for promoting the realization of multiple-voxel DW-MRS experiments, though the latter is still constrained by hardware in the present. The single-voxel DW-MRS method for probing lipid diffusion was first developed and evaluated in oil and muscle phantoms. The experimental sequence was demonstrated to be sensitive to diffusion restriction and free of significant artifacts. Experiments were then performed in rat hindlimb muscles in vivo. The restricted lipid diffusion behavior was characterized by apparent diffusion coefficient (ADC) changes and utilized for quantifying the sizes of intramyocellular lipid (IMCL) droplets in normal, fasting, diabetic and obese rats. The sizes of IMCL droplets reflect their vital roles in muscle energy metabolism. The IMCL droplet size estimated by ADC here was closely correlated with that measured by transmission electron microscopy. IMCL ADC was sensitive to metabolic alterations, decreasing in the fasting and diabetic groups while increasing in the obese group. These results clearly demonstrate DW MRS as a new means to examine the dynamics of IMCL metabolism in vivo. The DW-MRS technique was then utilized to characterize water ADC during neuronal activation to explore the vascular origins of the BOLD signal in rat brains. MRS experiments with acoustic stimulation were performed with a dynamic point-resolved spectroscopy (PRESS) acquisition on conditions with or without the diffusion gradient for blood suppression in the same voxel and same experimental session, which enabled the simultaneous T2/T2*/diffusion measurements. The T2*% changes with and without diffusion gradient showed no significant difference, while the spin echo (SE)-BOLD% (T2%) change significantly decreased after applying the diffusion gradient, suggesting an intravascular component in the SE-BOLD signal. This intravascular component was not venous blood, as the T2* of this component was comparable with the T2* of the brain tissue. These results provide new insights into the vascular origins of BOLD signals. A CS approach was developed to accelerate in vivo magnetic resonance spectroscopic imaging (MRSI) which enables multi-voxel MRS measurements. The CS undersampling was performed by acquiring a pseudo-random and density-varying subset of phase encodings. The proposed CS approach preserved the spectral and spatial resolution, while substantially reduced the number of phase encodings with accelerations up to seven fold for phantom and up to six fold for in vivo rat brains. / published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

Diffusion weighted magnetic resonance images offer unique insights into the neural networks of in vivo human brain. In this study, we investigate estimation and statistical analysis of multi-compartment models in high angular resolution diffusion imaging (HARDI) involving the Rician noise model. In particular, we address four important issues in multi-compartment diffusion model estimation, namely, the modelling of Rician noise in diffusion weighted (DW) images, the automatic determination of the number of compartments in the diffusion signal, the application of spatial prior on multi-compartment models, and the evaluation of parameter indeterminacy in diffusion models. We propose an expectation maximization (EM) algorithm to estimate the parameters of a multi-compartment model by maximizing the Rician likelihood of the diffusion signal. We introduce a novel scheme for automatically selecting the number of compartments, via a sparsity-inducing prior on the compartment weights. A non-local weighted maximum likelihood estimator is proposed to improve estimation accuracy utilizing repetitive patterns in the image. Experimental results show that the proposed algorithm improves estimation accuracy in low signal-to-noise-ratio scenarios, and it provides better model selection than several alternative strategies. In addition, we derive the Cram´er-Rao Lower Bound (CRLB) of the maximum Rician likelihood estimator for the balland-stick model and general differentiable diffusion models. The CRLB provides a general theoretical tool for comparing diffusion models and examining parameter indeterminacy in the maximum likelihood estimation problem. / published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy