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

The purpose of our study was to determine the incidence, causes, and reversibility of leukoencephalopathies demonstrating confluent areas of restricted diffusion on magnetic resonant imaging (DWI+LE). We hypothesized DWI+LE would have a low incidence, and be primarily caused by toxic exposures. We performed a logic sentence based search of the Yale-New Haven MRI database to select for reports indicating restricted diffusion within the cerebral white matter. We examined patients neuroimaging studies and medical record. We identified a total of 35 cases of DWI+LE, which resulted in an overall incidence of 0.2% over the five-year period queried. The medical conditions associated with DWI+LE were as follows: toxic exposure (7), hypoxia with concurrent trauma (7), hypoxia with concurrent toxic exposure (4), hypoxia with concurrent metabolic derangements (4), seizure with concurrent metabolic derangements (2), metabolic derangements (2), antiepileptic therapy (2), hypoxia (1), trauma (1), and unknown (5). The most favorable outcomes were seen in patients with intrathecal methotrexate toxicity, while patients with hypoxia without a lucid interval fared worst. We concluded that DWI+ LE are rare, their etiology diverse, and its reversibility dependant upon the type and severity of the insult.

Thesis (Ph.D.). / Written for the School of Computer Science and Centre for Intelligent Machines. Title from title page of PDF (viewed 2009/06/11). Includes bibliographical references.

Diffusion magnetic resonance imaging (dmri) is a technique that can be used to examine the diffusion characteristics of water in the living brain. A recently developed application of this technique is tractography, in which information from brain images obtained using dmri is used to reconstruct the pathways which connect regions of the brain together. Proxy measures for the integrity, or coherence, of these pathways have also been defined using dmri-derived information. The disconnection hypothesis suggests that specific neurological impairments can arise from damage to these pathways as a consequence of the resulting interruption of information flow between relevant areas of cortex. The development of dmri and tractography have generated a considerable amount of renewed interest in the disconnectionist thesis, since they promise a means for testing the hypothesis in vivo in any number of pathological scenarios. However, in order to investigate the effects of pathology on particular pathways, it is necessary to be able to reliably locate them in three-dimensional dmri images. The aim of the work described in this thesis is to improve upon the robustness of existing methods for segmenting specific white matter tracts from image data, using tractography, and to demonstrate the utility of the novel methods for the comparative analysis of white matter integrity in groups of subjects. The thesis begins with an overview of probability theory, which will be a recurring theme throughout what follows, and its application to machine learning. After reviewing the principles of magnetic resonance in general, and dmri and tractography in particular, we then describe existing methods for segmenting particular tracts from group data, and introduce a novel approach. Our innovation is to use a reference tract to define the topological characteristics of the tract of interest, and then search a group of candidate tracts in the target brain volume for the best match to this reference. In order to assess how well two tracts match we define a heuristic but quantitative tract similarity measure. In later chapters we demonstrate that this method is capable of successfully segmenting tracts of interest in both young and old, healthy and unhealthy brains; and then describe a formalised version of the approach which uses machine learning methods to match tracts from different subjects. In this case the similarity between tracts is represented as a matching probability under an explicit model of topological variability between equivalent tracts in different brains. Finally, we examine the possibility of comparing the integrity of groups of white matter structures at a level more fine-grained than a whole tract.

Thesis (Ph. D.)--University of California, San Diego, 2010. / Title from first page of PDF file (viewed Feb. 18, 2010). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references.

Thesis (M.S.)--University of Missouri-Columbia, 2008. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on August 12, 2009) Includes bibliographical references.

Structural brain networks can be constructed at a macroscopic scale using diffusion magnetic resonance imaging (dMRI) and whole-brain tractography. Under this approach, grey matter regions, such as Brodmann areas, form the nodes of a network and tractography is used to construct a set of white matter fibre tracts which form the connections. Graph-theoretic measures may then be used to characterise patterns of connectivity. In this study, we measured the test-retest properties of such networks by varying several factors affecting network construction using ten healthy volunteers who underwent a dMRI protocol at 1.5 T on two separate occasions. High resolution T1-weighted brains were parcellated into regions-of-interest and network connections were identified using dMRI and two alternative tractography algorithms, two alternative seeding strategies, constraints on anatomical plausibility and three alternative network weightings. Test-retest performance was found to improve when: 1) seeding from white matter, rather than grey; and 2) using probabilistic tractography, rather than deterministic. In terms of network weighting, a measure of streamline density produced better test-retest performance than tract-averaged diffusion anisotropy, although it remains unclear which is most representative of the underlying axonal connections. These findings were then used to inform network construction for two further cohorts: a casecontrol analysis of 30 patients with amyotrophic lateral sclerosis (ALS) compared with 30 age-matched healthy controls; and a cross-sectional analysis of 80 healthy volunteers aged 25– 64 years. In both cases, networks were constructed using a weighting reflecting tract-averaged fractional anisotropy (FA). A mass-univariate statistical technique called network-based statistics, identified an impaired motor-frontal-subcortical subnetwork (10 nodes and 12 bidirectional connections), consistent with upper motor neuron pathology, in the ALS group compared with the controls. Reduced FA for three of the impaired network connections, which involved fibres of the cortico-spinal tract, were significantly correlated with the rate of disease progression. Cross-sectional analysis of the 80 healthy volunteers was intended to provide supporting evidence for the widely reported age-related decline in white matter integrity. However, no meaningful relationships were found between increasing age and impaired connectivity based on global, lobar and nodal network properties – findings which were confirmed with a conventional voxel-based analysis of the dMRI data. In conclusion, whilst current acquisition protocols and methods can produce networks capable of characterising the genuine between-subject differences in connectivity, it is challenging to measure subtle white matter changes, for example, due to normal ageing. We conclude that future work should be undertaken to address these concerns.

No description available.

Ischaemic stroke is a complex disorder with many different aetiologies, but previous studies of stroke often did not differentiate aetiological subtypes of ischaemic stroke. However, different stroke subtypes may have different risk factors, and to target preventive treatments more effectively, we need to understand these associations. I studied the association of established vascular risk factors with different aetiological stroke subtypes in population-based cohorts of stroke patients. I studied Diffusion Weighted Magnetic Resonance Imaging (DWI) in patients with subacute minor stroke and TIA to determine whether DWI may be a useful addition to the management of such patients, and whether it may be a useful tool in future epidemiological studies of stroke. To determine whether carotid anatomy may be a risk factor for large vessel atheroma I studied angiographical data from the European Carotid Surgery Trial. My main findings are that the prevalence of risk factors differs between stroke subtypes. It also differs between hospitalised and non-hospitalised patients, highlighting that risk factor studies should be performed in population-based cohorts. Analysis of family history data suggests that future genetic studies may best be targeted at non-cardioembolic stroke and at younger patients, and that genetic studies of hypertension may help to unravel some of the genetic factors contributing to stroke risk. DWI is sensitive in subacute minor stroke, and inter- and intra-observer reproducibility are high. DWI frequently adds useful information and may influence patient management. More widespread use of DWI in patients with subacute stroke and TIA should be considered, and DWI may also be a useful tool in future epidemiological studies of stroke. Carotid anatomy varies considerably between individuals, is very asymmetrical within individuals, and it differs between men and women. These findings may partly explain differences in plaque development between individuals, asymmetrical plaque formation within individuals, and sex differences in the distribution of carotid plaque and in the prevalence of carotid atheroma in the general population. Carotid anatomy may be a risk factor for local plaque development. Although not amenable to treatment, knowing which anatomical configuration is associated with atheroma formation could help to identify high-risk individuals in whom other risk factors should be treated aggressively.

Computer vision encompasses a host of computational techniques to process visual information. Medical imagery is one particular area of application where data comes in various forms: X-rays, ultrasound probes, MRI volumes, EEG recordings, NMR spectroscopy, etc. This dissertation is concerned with techniques for accurate reconstruction of neural pathways from diffusion magnetic resonance imagery (dMRI). This dissertation describes a filtered approach to neural tractography. Existing methods independently estimate the diffusion model at each voxel so there is no running knowledge of confidence in the estimation process. We propose using tractography to drive estimation of the local diffusion model. Toward this end, we formulate fiber tracking as recursive estimation: at each step of tracing the fiber, the current estimate is guided by those previous. We argue that this approach is more accurate than conventional techniques. Experiments demonstrate that this filtered approach significantly improves the angular resolution at crossings and branchings. Further, we confirm its ability to trace through regions known to contain such crossing and branching while providing inherent path regularization. We also argue that this approach is flexible. Experiments demonstrate using various models in the estimation process, specifically combinations of Watson directional functions and rank-2 tensors. Further, this dissertation includes an extension of the technique to weighted mixtures using a constrained filter.