Thesis (Masters)--California Institute of Technology, 1959. / Advisor names found in the Acknowledgments pages of the thesis. Title from home page. Viewed 02/09/10. Includes bibliographical references.

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

published_or_final_version / Electrical and Electronic Engineering / Master / Master 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

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Conventional MRI relies on a strong fixed magnetic field B0 which is stable during the imaging process. By contrast, field-cycling MRI switches the strength of B0 up or down during an experiment. In this way, field-cycling provides access to endogenous information not accessible to standard MRI, such as enhanced T1 relaxation at certain NMR frequencies due to interactions between hydrogen and nitrogen nuclei in proteins. However, biomedical research of T1 dispersion is limited by the unavailability of equipment and rapid software methods. Strategies are presented to address these deficiencies. A removable electromagnet was designed and implemented for use with a 59-mT vertical-field, permanent-magnet based imager. The resistive magnet locally offsets the primary field over a small projected region to enable field-cycling relaxometry on an otherwise-conventional imager. Radiofrequency coils were constructed to suit the electromagnet’s configuration. T1 dispersion measurements were demonstrated for, separately, the finger joints and forearm of a human volunteer. Prior to this work, producing graphs of T1 dispersion from a volume of interest required lengthy T1 mapping at each field strength step. A new pulse sequence combining SR/IR T1 determination with field-cycling and point-resolved spectroscopy localization enables the measurement of dispersion curves of a volume selected from a pilot image. Its advantages include less partial voluming than whole-sample relaxometry, as well as better SNR and faster acquisition times than image-based techniques. The sequence’s sensitivity is sufficient to reveal distinctive ‘quadrupole dips’ in dispersion curves. To the author’s knowledge, it is the first pulse sequence to enable the relationship between T1 and field strength to be examined in times which are feasible for clinical investigations. Used together as presented in this thesis, the hardware and software developed represent a step towards field-cycling being used to reveal useful diagnostic information inaccessible to conventional MRI.

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