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<p>Diffusion tensor (DT) and blood oxygen-level dependent (BOLD) imaging are two noninvasive magnetic resonance (MR) techniques that have been used to probe skeletal muscle microstructure and microvasculature, respectively. Over a series of four studies, the work in this thesis aimed at furthering our understanding of baseline DT metrics and BOLD signals in lower limb muscles (calf and foot) of healthy young subjects. Since postural changes have been shown to alter numerous quantities, including fluid volumes and muscle cross sectional area, DT indices and BOLD signal characteristics were examined in response to movement from upright to supine position.</p> <p>Reductions of 3.2-6.7% and 3.4-7.5% were measured in calf DT eigenvalues and apparent diffusion coefficient (ADC) in the various muscles, following 34 and 64 minutes of supine rest, respectively (P</p> <p>Establishment of baseline diffusion metrics in the foot region was feasible (chapter 6). Examination of foot DT indices in response to positional change showed that the metrics decreased from 2.7-4.6% following 34 minutes of supine rest (P</p> / Doctor of Philosophy (PhD)

Magnetic Resonance Imaging (MRI) is an imaging modality that acquires an image with little to no damage to the tissue. MRI does not introduce foreign particles or high energy radiation into the body, making it one of the least invasive medical imaging modalities. MRI can achieve excellent soft tissue contrast and is therefore useful for diagnosis of a wide variety of diseases. While there are a wide variety of available techniques for generating contrast in MRI, there are still many open areas for research. For example, many tissues in the human body exhibit such rapid signal decay that they are difficult to image with MRI: they are "MRI invisible". Furthermore, some of the newer MRI imaging techniques have not been fully validated to ensure that they are truly revealing accurate information about the underlying anatomical microstructure that they purport to image. This dissertation focuses on the development of new techniques in two distinct areas. First, a novel method for accurately assessing the MRI signal decay properties of tissues that are normally MRI invisible, such as tendons, ligaments, and certain pathological chemical deposits in the brain, is presented. This is termed "ultrashort MRI relaxometry". Second, two new image processing algorithms that operate on high resolution images of stained histological slices of the ex vivo brain are presented. The first of these image processing algorithms allows the semi-automated extraction of nerve fiber directionality from the histological slice images, a process that is normally done manually, is incredibly time consuming, and is prone to human error. This new technique represents one significant step in the complicated problem of attempting to validate a popular MRI technique, Diffusion Tensor Imaging (DTI), by ensuring that DTI results correlate with the true underlying physiology revealed by histological slicing and staining. The second of these image processing algorithms attempts to extract and segment regions of different "cytoarchitectonic characteristics" from stained histological slices of ex vivo brain. Again, traditional cytoarchitectonic segmentation relies on manual segmentation by an expert neuroanatomist, which is slow and sometimes inconsistent. The new technique is a first step towards automated this process, potentially providing greater accuracy and repeatability of the segmentations in a much shorter time. Together, these contributions represent a significant contribution to the body of MR imaging techniques, and associated image processing techniques for validation of newer MR neuroimaging techniques against the gold standard of stained histological slices of ex vivo brain.

L'objet de cette thèse était de mettre en place une séquence robuste d'imagerie rapide du tenseur de diffusion par RMN sur un imageur petit animal 7-T. Nous avons mis en place une séquence Twice Refocused Spin Echo afin de s'affranchir des problèmes de courants de Foucault. Nous avons préféré une acquisition spirale de l'espace-k à EPI pour son insensibilité aux artefacts de mouvement et de flux très importants en diffusion. Nous avons développé un logiciel sous Matlab pour la reconstruction des images du tenseur de diffusion et la visualisation des cartes d'anisotropie et cartes couleurs. Enfin nous avons utilisé un logiciel développé à l'INRIA de Nice-Sofia Antipolis (MedINRIA DTI Track) pour visualiser l'affichage des vecteurs propres et effectuer le « fiber tracking » à partir des datas collectées sur notre imageur 7-T sur cerveau de rat. Une fois la technique et les méthodes de reconstructions validées sur différents fantômes et sur cerveau de rat sain, nous l'avons appliqué à un modèle de rat traumatisé étudié au sein du laboratoire et traumatisé selon la méthode impact-accélération. L'objet de l'étude était de caractériser l'oedème cérébral post traumatique de manière précoce grâce à l'imagerie du tenseur de diffusion. Cette technique nous a permis de caractériser le type d'oedème cérébral post-traumatique par des modifications de la diffusivité moyenne. Des modifications d'anisotropie dans le corps calleux du cerveau de rat traumatisé ont montré la présence de lésions axonales diffuses. Enfin, l'imagerie fiber tracking a permis de détecter des lésions axonales au centre du corps calleux du cerveau de rat traumatisé.

Magnetic resonance imaging (MRI) has long become one of the most powerful and accurate tools of medical diagnostic imaging. Central to the diagnostic capabilities of MRI is the notion of contrast, which is determined by the biochemical composition of examined tissue as well as by its morphology. Despite the importance of the prevalent T₁, T₂, and proton density contrast mechanisms to clinical diagnosis, none of them has demonstrated effectiveness in delineating the morphological structure of the white matter - the information which is known to be related to a wide spectrum of brain-related disorders. It is only with the recent advent of diffusion-weighted MRI that scientists have been able to perform quantitative measurements of the diffusivity of white matter, making possible the structural delineation of neural fibre tracts in the human brain. One diffusion imaging technique in particular, namely high angular resolution diffusion imaging (HARDI), has inspired a substantial number of processing methods capable of obtaining the orientational information of multiple fibres within a single voxel while boasting minimal acquisition requirements. HARDI characterization of fibre morphology can be enhanced by increasing spatial and angular resolutions. However, doing so drastically reduces the signal-to-noise ratio. Since pronounced measurement noise tends to obscure and distort diagnostically relevant details of diffusion-weighted MR signals, increasing spatial or angular resolution necessitates application of the efficient and reliable tools of image denoising. The aim of this work is to develop an effective framework for the filtering of HARDI measurement noise which takes into account both the manifold to which the HARDI signal belongs and the statistical nature of MRI noise. These goals are accomplished using an approach rooted in non-local means (NLM) weighted averaging. The average includes samples, and therefore dependencies, from the entire manifold and the result of the average is used to deduce an estimate of the original signal value in accordance with MRI statistics. NLM averaging weights are determined adaptively based on a neighbourhood similarity measure. The novel neighbourhood comparison proposed in this thesis is one of spherical neighbourhoods, which assigns large weights to samples with similar local orientational diffusion characteristics. Moreover, the weights are designed to be invariant to both spatial rotations as well as to the particular sampling scheme in use. This thesis provides a detailed description of the proposed filtering procedure as well as experimental results with synthetic and real-life data. It is demonstrated that the proposed filter has substantially better denoising capabilities as compared to a number of alternative methods.

Der menschliche inferiore Parietallappen (IPC) gehört zum Assoziationskortex und spielt eine wichtige Rolle bei der Integration von somatosensorischen (taktilen), visuellen und akustischen Reizen. Bisher gibt es keine eindeutigen Informationen über den strukturellen Aufbau dieser Hirnregion. Parzellierungen anhand der Zytoarchitektur reichen von zwei (Brodmann 1909) bis sieben Subareale (Caspers et al. 2006). Homologien zwischen dem IPC des Menschen und Makaken-Affen sind weitestgehend unbekannt. In der vorliegenden Arbeit wurden der Aufbau und die Konnektivitäten des menschlichen IPC genauer untersucht. Dazu führte man eine konnektivitätsbasierte Parzellierung des IPC an 20 Probanden durch. Als Methode kam Diffusions-Tensor-Imaging (DTI) kombiniert mit probabilistischer Traktogra-phie zum Einsatz. Der IPC konnte anhand der Konnektivitäten in drei Subareale (IPCa, IPCm, IPCp) parzelliert werden. Diese besitzen in beiden Hemisphären eine ähnliche Größe und eine rostro-kaudale Anordnung. Die Parzellierung ist vergleichbar mit der des Makaken-IPC, bei dem ebenfalls eine Unterteilung in drei Areale (PF, PFG, PG) und eine rostro-kaudale Anordnung nachgewiesen werden konnte. Jedes Subareal des menschlichen IPC besitzt ein individuelles Konnektivitätsmuster. Beim Menschen als auch beim Makaken gibt es starke Verbindungen zum lateralen prämotorischen Kortex und zum superioren Parietallappen. Diese Gemeinsamkeiten lassen darauf schließen, dass strukturelle Eigenschaften im Laufe der Evolution erhalten geblieben sind. Allerdings sind beim Menschen auch Neuentwicklungen nachweisbar. Dazu gehören die deutlich hervortretenden Verbindungen zum Temporallappen. Möglicherweise haben sich diese erst während der Evolution entwickelt und sind beim Menschen als Teil des perisylvischen Sprachnetzwerkes an der Sprachbildung beteiligt. / The human inferior parietal cortex convexity (IPCC) is an important association area, which integrates auditory, visual and somatosensory information. However, the structural organization of the IPCC is a controversial issue. For example, cytoarchitectonic parcellations reported in the literature range from two to seven areas. Moreover, anatomical descriptions of the human IPCC are often based on experiments in the macaque monkey. In this study we used diffusion-weighted magnetic resonance imaging (dMRI) combined with probabilistic tractography to quantify the connectivity of the human IPCC, and used this information to parcellate this cortex area. This provides a new structural map of the human IPCC, comprising three sub-areas (IPCa, IPCm, IPCp) of comparable size, in a rostro-caudal arrangement in the left and right hemisphere. Each sub-area is characterized by a connectivity fingerprint and the parcellation is similar to the subdivision reported for the macaque IPCC (rostro-caudal areas areas PF, PFG, and PG). However, the present study also reliably demonstrates new structural features in the connectivity pattern of the human IPCC, which are not known to exist in the macaque. This study quantifies inter-subject variability by providing a population representation of the sub-area arrangement, and demonstrates substantial lateralization of the connectivity patterns of IPCC.

Mild cognitive impairment (MCI) is a transition phase between normal aging and Alzheimer’s disease. Individuals with MCI show impairment in cognition as well as corresponding damage to areas of their brain. Performance on tasks such as discriminating objects with ambiguous features has been associated with damage to the perirhinal cortex, while scenes with structural (spatial) elements have been associated with damage to the hippocampus. In addition, attention is regarded as one of the first non-memory domains to decline in MCI. A relatively new MRI technique called diffusion tensor imaging (DTI) is sensitive to white matter microstructural integrity and has been associated with changes due to cognitive decline. 18 MCI (14 amnesic, 4 vascular) and 12 healthy matched controls were assessed in feature ambiguity, attention and structural learning to assess associated deficits in MCI. Associations with white matter microstructural integrity were then investigated. The MCI groups were discovered to perform worse than controls on the test of structural learning. In addition, altered attention networks were found in MCI and were associated with white matter microstructural integrity. No significant differences were found for feature ambiguity. These findings suggest there may be specific damage to the hippocampus while the perirhinal cortex may be preserved in MCI. Furthermore, dysfunction in attention was found to be associated with white matter microstructural integrity. These experimental tests may be useful in assessing dysfunction in MCI and identifying degeneration in white matter microstructural integrity. Further studies with larger sample sizes are needed to validate these findings.

Magnetic resonance imaging (MRI) has long become one of the most powerful and accurate tools of medical diagnostic imaging. Central to the diagnostic capabilities of MRI is the notion of contrast, which is determined by the biochemical composition of examined tissue as well as by its morphology. Despite the importance of the prevalent T₁, T₂, and proton density contrast mechanisms to clinical diagnosis, none of them has demonstrated effectiveness in delineating the morphological structure of the white matter - the information which is known to be related to a wide spectrum of brain-related disorders. It is only with the recent advent of diffusion-weighted MRI that scientists have been able to perform quantitative measurements of the diffusivity of white matter, making possible the structural delineation of neural fibre tracts in the human brain. One diffusion imaging technique in particular, namely high angular resolution diffusion imaging (HARDI), has inspired a substantial number of processing methods capable of obtaining the orientational information of multiple fibres within a single voxel while boasting minimal acquisition requirements. HARDI characterization of fibre morphology can be enhanced by increasing spatial and angular resolutions. However, doing so drastically reduces the signal-to-noise ratio. Since pronounced measurement noise tends to obscure and distort diagnostically relevant details of diffusion-weighted MR signals, increasing spatial or angular resolution necessitates application of the efficient and reliable tools of image denoising. The aim of this work is to develop an effective framework for the filtering of HARDI measurement noise which takes into account both the manifold to which the HARDI signal belongs and the statistical nature of MRI noise. These goals are accomplished using an approach rooted in non-local means (NLM) weighted averaging. The average includes samples, and therefore dependencies, from the entire manifold and the result of the average is used to deduce an estimate of the original signal value in accordance with MRI statistics. NLM averaging weights are determined adaptively based on a neighbourhood similarity measure. The novel neighbourhood comparison proposed in this thesis is one of spherical neighbourhoods, which assigns large weights to samples with similar local orientational diffusion characteristics. Moreover, the weights are designed to be invariant to both spatial rotations as well as to the particular sampling scheme in use. This thesis provides a detailed description of the proposed filtering procedure as well as experimental results with synthetic and real-life data. It is demonstrated that the proposed filter has substantially better denoising capabilities as compared to a number of alternative methods.

Οι τεχνικές απεικόνισης μοριακής διάχυσης (DWI) και τανυστή διάχυσης (DTI) είναι από τις πιο δημοφιλείς τεχνικές μαγνητικής τομογραφίας (MRI) στην έρευνα του εγκεφάλου. Διάχυση (ή θερμική κίνηση Brown) είναι ένα τυχαίο φαινόμενο το οποίο περιγράφει τη μεταφορά υλικού (π.χ μόρια νερού) από μία χωρική θέση σε άλλη με την πάροδο του χρόνου. Η διάχυση του νερού σε βιολογικούς ιστούς παρατηρείται μέσα, έξω, γύρω από τις κυτταρικές δομές και είναι αποτέλεσμα της θερμικής ενέργειας των μορίων. Η κάθε τεχνική υποστηρίζεται από τον δικό της αλγόριθμο από τους οποίους προκύπτουν και οι αντίστοιχοι παραμετρικοί χάρτες. Πιο συγκεκριμένα από την τεχνική διάχυσης προκύπτει ο δείκτης της φαινόμενης σταθεράς διάχυσης (ADC-Apparent Diffusion Coefficient) , ενώ από την τεχνική του τανυστή διάχυσης προκύπτει ο δείκτης της κλασματικής ανισοτροπίας (FA-Fractional Anisotropy). Η παράμετρος ADC δείχνει πόσο διαφέρει η διάχυση στην περιοχή ενδιαφέροντος σε σχέση με την μέση τιμή διάχυσης. Η κλασματική ανισοτροπία (FA) είναι δείκτης μέτρησης του βαθμού ανισοτροπίας της διάχυσης και η τιμή της εξαρτάται άμεσα από την ακεραιότητα των νευρικών ινών. Το φάσμα εφαρμογής των δύο τεχνικών είναι ευρύ (εφαρμογή σε απομυελινωτικές νόσους, ισχαιμικά επεισόδια, εγκεφαλικοί όγκοι). Ο κύριος λόγος είναι ότι η διάχυση των μορίων νερού είναι ιδιαίτερα ευαίσθητη σε τυχόν αλλοιώσεις στη δομή των ινών της Λευκής ουσίας. Σκοπός της παρούσας ερευνητικής είναι η εφαρμογή των τεχνικών Τανυστή Διάχυσης (DTI) και Μοριακής Διάχυσης (DWI) σε τρείς κατηγορίες εγκεφαλικών όγκων (μηνιγγιώματα, γλοιώματα υψηλής και χαμηλής κακοήθειας, εγκεφαλικούς μεταστατικούς όγκους) με σκοπό τον διαχωρισμό αυτών. / The brain is a highly organized organ with a complex microstructural organization . The microstructural organization of brain tissue affects the molecular motion (diffusion) of water. Diffusion therefore reflects the structural organization of tissue. Diffusion imaging is a Magnetic Resonance (MR) imaging technique that allows the quantification to the molecular motion of water. Magnitude and directionality (anisotropy) of molecular motion of water can be described. Measurements of the magnitude of diffusion have been used to identify abnormal tissue in tumors, stroke, multiple sclerosis and status epilepticus. Diffusion tensor imaging (DTI) is a relatively new technique that allows rotationally invariant measurements of both magnitude and directionality of water diffusion. DTI sequences with calculation of apparent diffusion coefficient (ADC) and fractional anisotropy (FA) scalars allow characterization of the shape and magnitude of the diffusion ellipsoid. These parameters consequently reflect the microstructural architecture of the human brain. In addition, quantification of diffusion can be especially helpful as it may allow early diagnosis of pathology . The purpose of this study was to correlate the changes in FA and ADC between three different brain tumors and outline the probability of presurgical tumor differentiation.

The primate visual system is organised and wired in a topological manner. From the eye well into extrastriate visual cortex, a preserved spatial representation of the vi- sual world is maintained across many levels of processing. Diffusion-weighted imaging (DWI), together with probabilistic tractography, is a non-invasive technique for map- ping connectivity within the brain. In this thesis I probed the sensitivity and accuracy of DWI and probabilistic tractography by quantifying its capacity to detect topolog- ical connectivity in the post mortem macaque brain, between the lateral geniculate nucleus (LGN) and primary visual cortex (V1). The results were validated against electrophysiological and histological data from previous studies. Using the methodol- ogy developed in this thesis, it was possible to segment the LGN reliably into distinct subregions based on its structural connectivity to different parts of the visual field represented in V1. Quantitative differences in connectivity from magno- and parvo- cellular subcomponents of the LGN to different parts of V1 could be replicated with this method in post mortem brains. The topological corticocortical connectivity be- tween extrastriate visual area V5/MT and V1 could also be mapped in the post mortem macaque. In vivo DWI scans previously obtained from the same brains have lower resolution and signal-to-noise because of the shorter scan times. Nevertheless, in many cases, these yielded topological maps similar to the post mortem maps. These results indicate that the preserved topology of connection between LGN to V1, and V5/MT to V1, can be revealed using non-invasive measures of diffusion-weighted imaging and tractography in vivo. In a preliminary investigation using Human Connectome data obtained in vivo, I was not able to segment the retinotopic map in LGN based on con- nections to V1. This may be because information about the topological connectivity is not carried in the much lower resolution human diffusion data, or because of other methodological limitations. I also investigated the mechanisms of perceptual learning by developing a novel task-irrelevant perceptual learning paradigm designed to adapt neuronal elements early on in visual processing in a certain region of the visual field. There is evidence, although not clear-cut, to suggest that the paradigm elicits task- irrelevant perceptual learning, but that these effects only emerge when practice-related effects are accounted for. When orientation and location specific effects on perceptual performance are examined, the largest improvement occurs at the trained location, however, there is also significant improvement at one other 'untrained' location, and there is also a significant improvement in performance for a control group that did not receive any training at any location. The work highlights inherent difficulties in inves- tigating perceptual learning, which relate to the fact that learning likely takes place at both lower and higher levels of processing, however, the paradigm provides a good starting point for comprehensively investigating the complex mechanisms underlying perceptual learning.