Dissertations / Theses on the topic 'Brain microstructure imaging'

The research presented in this thesis models the diffusion-weighted MRI signal within brain white matter tissue. We are interested in deriving descriptive microstructure indices such as white matter axon diameter and density from the observed diffusion MRI signal. The motivation is to obtain non-invasive reliable biomarkers for early diagnosis and prognosis of brain development and disease. We use both analytic and numerical models to investigate which properties of the tissue and aspects of the diffusion process affect the diffusion signal we measure. First we develop a numerical method to approximate the tissue structure as closely as possible. We construct three-dimensional meshes, from a stack of confocal microscopy images using the marching cubes algorithm. The experiment demonstrates the technique using a biological phantom (asparagus). We devise an MRI protocol to acquire data from the sample. We use the mesh models as substrates in Monte-Carlo simulations to generate synthetic MRI measurements. To test the feasibility of the method we compare simulated measurements from the three-dimensional mesh with scanner measurements from the same sample and simulated measurements from an extruded mesh and much simpler parametric models. The results show that the three-dimensional mesh model matches the data better than the extruded mesh and the parametric models revealing the sensitivity of the diffusion signal to the microstructure. The second study constructs a taxonomy of analytic multi-compartment models of white matter by combining intra- and extra-axonal compartments from simple models. We devise an imaging protocol that allows diffusion sensitisation parallel and perpendicular to tissue fibres. We use the protocol to acquire data from two fixed rat brains, which allows us to fit, study and evaluate the models. We conclude that models which incorporate non-zero axon radius describe the measurements most accurately. The key observation is a departure of signals in the parallel direction from the two-compartment models, suggesting restriction, most likely from glial cells or binding of water molecules to the membranes. The addition of the third compartment can capture this departure and explain the data. The final study investigates the estimates using in vivo brain diffusion measurements. We adjust the imaging protocol to allow an in vivo MRI acquisition of a rat brain and compare and assess the taxonomy of models. We then select the models that best explain the in vivo data and compare the estimates with those from the ex vivo measurements to identify any discrepancies. The results support the addition of the third compartment model as per the ex vivo findings, however the ranking of the models favours the zero radius intra-axonal compartments.

The possibility to perform virtual, non-invasive, quantitative, in vivo histological assessments might revolutionize entire fields, among which clinical and cognitive neurosciences. Magnetic Resonance Imaging (MRI) is an ideal non-invasive imaging technique to achieve these goals. Tremendous advancements in the last decades have favored the transition of MRI scanners from “imaging devices” to “measurement devices” (Novikov, 2021), thus capable to yield measurements in physical units, which might be further combined to provide quantities describing histological properties of substrates. A central role in this community endeavor has been played by diffusion-weighted MRI (dMRI), which by measuring the dynamics of spin diffusion, allows inferences on geometrical properties of tissues. Yet, conventional dMRI methodologies suffer from poor specificity. In this thesis, techniques aiming at improving the specificity of microstructural descriptions have been explored in dMRI datasets supporting an increasing level of complexity of the dMRI signal representations. Applications in individuals with different age range, in different populations, and for different MRI scanner fields, have been considered. Firstly, tractography has been combined with Diffusion Tensor Imaging (DTI), an along-tract framework, and morphometry, in the study of the microstructure of the optic radiations in different groups of blind individuals. Secondly, DTI has been combined with Free-Water Imaging (FWI) to monitor the effect of proton-irradiation in a pediatric brain tumor case study. Thirdly, FWI and Diffusion Kurtosis Imaging (DKI) have been combined with an advanced thalamic segmentation framework to study the associations between motor performance and thalamic microstructure in a cohort of individuals affected by Parkinson’s disease. Finally, the largest contribution of this thesis is represented by the adaptation of the Correlation Tensor Imaging - a technique increasing the specificity of DKI harnessing Double Diffusion Encoding previously applied only in preclinical settings - for a clinical 3 T scanner. The ensuing investigation revealed new important insights on the sources of diffusional kurtosis, in particular of the microscopic kurtosis (μK), a component so far neglected by contemporary neuroimaging techniques, which might carry an important clinical role (Alves et al., 2022), and can now be accessed by clinical scanners. In conclusion, strategies to increase the specificity of microstructural descriptions in the brain are presented for different datasets, and their strength and limitations are discussed.

Diffusion imaging is a non-invasive imaging method which has been successfully applied to study white matter. Most clinical approaches, based on Diffusion Tensor Imaging (DTI), are limited by the simple model of the underlying tissue imposed, failing to reconstruct the diffusion propagator, which fully encodes the displacement of water molecules. To do so, more comprehensive sampling schemes such as Diffusion Spectrum Imaging (DSI) have been developed. In this thesis, I have investigated the effect of different tissue configurations, sampling and processing steps in the performance of DSI. I identified specific configurations where DSI is unable to characterise diffusion without artefacts, namely aliasing caused by fast diffusion components. Furthermore, processing of the diffusion orientation distribution function (ODF) in these environments can lead to generation of spurious fibres in tractography reconstructions. To overcome this, I have applied a novel step in the processing pipeline of DSI, namely a different way of computing the ODF, which consists of restricting the range of integration to probabilities based on the physical displacement of “axonlike” diffusivities. Alternatively, it is possible to use a mathematical representation of the acquired signal, of which the Simple Harmonic Oscillator based Reconstruction and Estimation (SHORE) and Mean Apparent Propagator Magnetic Resonance Imaging (MAP-MRI) are examples. I have here used these methods and further provided optimised acquisitions based on standard propagator metrics. Finally, I have introduced new metrics that use microstructural information available at the different displacement scales, and can facilitate exploration of brain organisation even when no a-priori biophysical model is available.

L’ambition des très hauts champs magnétiques (≥ 7T) à forts gradients (≥ 300mT/m) est de dépasser la résolution millimétrique imposée à plus bas champ pour atteindre l’échelle mésoscopique en neuroimagerie. Etudier le cerveau à cette échelle est essentiel pour comprendre le lien entre fonction et substrat anatomique. Malgré les progrès réalisés sur les aimants cliniques à 7T, il n’en est pas de même des gradients. Cette thèse vise à cartographier le cerveau humain à l’échelle mésoscopique via l’étude de pièces anatomiques post mortem. Une approche alternative a été choisie, reposant sur l'utilisation d'imageurs précliniques à très hauts champs (7T et 11.7T) et forts gradients (780mT/m). Après une première étape de préparation (extraction et fixation) opérée au CHU de Tours, une pièce anatomique complète a été scannée à 3T, avant découpe de l’hémisphère gauche en sept blocs. Un protocole d’acquisition IRM ciblant une résolution mésoscopique a ensuite été mis en place à 11.7T. Ce protocole, incluant des séquences anatomiques, relaxométriques, et de diffusion, a été validé à l’aide de deux structures clé: un hippocampe et un tronc cérébral. Les données anatomiques et de diffusion acquises à une résolution mésoscopique sur l’hippocampe ont permis de segmenter ses sous-champs, d’extraire le circuit polysynaptique et d’observer l’existence d’un gradient de connectivité et de densité neuritique positif dans la direction postéro-antérieure de l’hippocampe. L’utilisation de modèles avancés d’étude de la microstructure a également révélé l’apport de ces techniques pour la segmentation de l’hippocampe, les cartes de densité neuritique révélant les trois couches des champs ammoniens. Un tronc cérébral a ensuite été scanné, avec une résolution atteignant la centaine de micromètres. Une segmentation de 53 de ses 71 noyaux a été réalisée au sein du CHU de Tours, permettant d’établir la cartographie IRM du tronc cérébral humain la plus complète à ce jour. Les principaux faisceaux de la substance blanche ont été reconstruits, ainsi que les projections du locus coeruleus, structure connue pour être atteinte dans le maladie de Parkinson. Forts de ces résultats, la campagne d'acquisition de l'hémisphère gauche, d’une durée de 10 mois, a été initiée. Le protocole d’acquisition à 11.7T intègre des séquences anatomiques (100/150µm) ainsi que des séquences d'imagerie 3D pondérées en diffusion (b=1500/4500/8000 s/mm², 25/60/90 directions) à 200µm. Des acquisitions complémentaires réalisées à 7T comprenant des séquence d’écho de spin rapide avec inversion-récupération ont par ailleurs permis d’étudier la myéloarchitecture du cortex cérébral et d’identifier automatiquement sa structure laminaire. Un nouveau modèle de mélange de Gaussiennes a été développé, intégrant les informations myéloarchitecturales issues de la cartographie T1 et les informations cytoarchitecturales issues de l’imagerie de diffusion. Il a ainsi pu être démontré que l’utilisation conjointe de ces deux informations permettait de mettre en évidence des couches du cortex visuel, l’information myéloarchitecturale favorisant l’extraction des couches externes et la densité neuritique celle des couches plus profondes. Enfin, l’exploitation des données IRM acquises à 11.7T sur les différents blocs a nécessité la mise en place d’une chaîne de prétraitements pour corriger les artéfacts d’imagerie et reconstruire l’hémisphère entier à l’aide de stratégies de recalage difféomorphe avancées. L’objectif de ce projet est l’obtention d’un jeu de données IRM de très haute résolution spatio-angulaire de l’hémisphère gauche. Ce jeu de données anatomique et de diffusion unique permettra à terme de constituer un nouvel atlas IRM mésoscopique de la structure, de la connectivité et de la cytoarchitecture du cerveau humain
The aim of ultra-high field strength (≥7T) and ultra-strong gradient systems (≥300mT/m) is to go beyond the millimeter resolution imposed at lower field and to reach the mesoscopic scale in neuroimaging. This scale is essential to understand the link between brain structure and function. However, despite recent technological improvements of clinical UHF-MRI, gradient systems remain too limited to reach this resolution. This thesis aims at answering the need for mapping the human brain at a mesoscopic scale by the study of post mortem samples. An alternative approach has been developed, based on the use of preclinical systems equipped with ultra-high fields (7T/11.7T) and strong gradients (780mT). After its extraction and fixation at Bretonneau University Hospital (Tours), an entire human brain specimen was scanned on a 3T clinical system, before separating its two hemispheres and cutting each hemisphere into seven blocks that could fit into the small bore of an 11.7T preclinical system. An MRI acquisition protocol targeting a mesoscopic resolution was then set up at 11.7T. This protocol, including anatomical, quantitative, and diffusion-weighted sequences, was validated through the study of two key structures: the hippocampus and the brainstem. From the high resolution anatomical and diffusion dataset of the human hippocampus, it was possible to segment the hippocampal subfields, to extract the polysynaptic pathway, and to observe a positive gradient of connectivity and neuritic density in the posterior-anterior direction of the hippocampal formation. The use of advanced microstructural models (NODDI) also highlighted the potential of these techniques to reveal the laminar structure of the Ammon’s horn. A high resolution anatomical and diffusion MRI dataset was obtained from the human brainstem with an enhanced resolution of a hundred micrometers. The segmentation of 53 of its 71 nuclei was performed at the Bretonneau University Hospital, making it the most complete MR-based segmentation of the human brainstem to date. Major white matter bundles were reconstructed, as well as projections of the locus coeruleus, a structure known to be impaired in Parkinson’s disease. Buoyed by these results, a dedicated acquisition campaign targeting the entire left hemisphere was launched for total scan duration of 10 months. The acquisition protocol was performed at 11.7T and included high resolution anatomical sequences (100/150μm) as well as 3D diffusion-weighted sequences (b=1500/4500/8000 s/mm², 25/60/90 directions, 200μm). In addition, T1-weighted inversion recovery turbo spin echo scans were performed at 7T to further investigate the myeloarchitecture of the cortical ribbon at 300µm, revealing its laminar structure. A new method to automatically segment the cortical layers was developed relying on a Gaussian mixture model integrating both T1-based myeloarchitectural information and diffusion-based cytoarchitectural information. The results gave evidence that the combination of these two contrasts highlighted the layers of the visual cortex, the myeloarchitectural information favoring the extraction of the outer layers and the neuritic density favoring the extraction of the deeper layers. Finally, the analysis of the MRI dataset acquired at 11.7T on the seven blocks required the development of a preprocessing pipeline to correct artifacts and to reconstruct the entire hemisphere using advanced registration methods. The aim was to obtain an ultra-high spatio-angular resolution MRI dataset of the left hemisphere, in order to establish a new mesoscopic post mortem MRI atlas of the human brain, including key information about its structure, connectivity and microstructure

Diffusion of water molecules in biological tissues depends on several microstructural properties. Therefore, diffusion Magnetic Resonance Imaging (dMRI) is a useful tool to infer and study microstructural brain changes in the context of human development, ageing and neuropathology. In this thesis, the state-of-the-art of advanced dMRI techniques is explored and strategies to overcome or reduce its pitfalls are developed and validated. Firstly, it is shown that PCA denoising and Gibbs artefact suppression algorithms provide an optimal compromise between increased precision of diffusion measures and the loss of tissue's diffusion non-Gaussian information. Secondly, the spatial information provided by the diffusion kurtosis imaging (DKI) technique is explored and used to resolve crossing fibres and generalize diffusion measures to cases not limited to well-aligned white matter fibres. Thirdly, as an alternative to diffusion microstructural modelling techniques such as the neurite orientation dispersion and density imaging (NODDI), it is shown that spherical deconvolution techniques can be used to characterize fibre crossing and dispersion simultaneously. Fourthly, free water volume fraction estimates provided by the free water diffusion tensor imaging (fwDTI) are shown to be useful to detect and remove voxels corrupted by cerebrospinal fluid (CSF) partial volume effects. Finally, dMRI techniques are applied to the diffusion data from the large collaborative Cambridge Centre for Ageing and Neuroscience (CamCAN) study. From these data, the inference provided by diffusion anisotropy measures on maturation and degeneration processes is shown to be biased by age-related changes of fibre organization. Inconsistencies of previous NODDI ageing studies are also revealed to be associated with the different age ranges covered. The CamCAN data is also processed using a novel non-Gaussian diffusion characterization technique which is invariant to different fibre configurations. Results show that this technique can provide indices specific to axonal water fraction which can be linked to age-related fibre density changes.

L'estimation non invasive de la microstructure du cerveau, qui se compose de nombreux neurites, de somas et de cellules gliales, est essentielle pour l'imagerie cérébrale. L'IRM de diffusion (IRMd) est une technique prometteuse pour sonder les propriétés microstructurelles du cerveau en dessous de la résolution spatiale des scanners IRM. En raison de la complexité structurelle du tissu cérébral et du mécanisme complexe de l'IRM de diffusion, l'estimation de la microstructure in vivo est un défi. Les méthodes existantes utilisent généralement des géométries simplifiées, notamment des sphères et des bâtons, pour modéliser les structures neuronales et obtenir des expressions analytiques des signaux intracellulaires. La validité des hypothèses faites par ces méthodes reste indéterminée. Cette thèse vise à faciliter l'estimation de la microstructure du cerveau par simulation en remplaçant les géométries simplifiées par des modèles réalistes de la géométrie des neurones et les expressions analytiques des signaux intracellulaires par des simulations d'IRM de diffusion. Combinées à des modèles précis de la géométrie des neurones, les simulations numériques d'IRMd peuvent donner des signaux intracellulaires précis et incorporer les effets dus, par exemple, à l'ondulation des neurites ou à l'échange d'eau entre le soma et les neurites.Malgré ces avantages, les simulations d'IRMd n'ont pas été largement adoptées en raison de l'inaccessibilité des fantômes numériques, de la faible efficacité de calcul des simulateurs d'IRMd et de la difficulté d'approximer les mappings implicites entre les signaux d'IRMd et les propriétés de la microstructure. Cette thèse contribue à la résolution des problèmes susmentionnés de la manière suivante : (1) en développant un générateur de maillage de neurones open-source et en rendant accessibles au public plus d'un millier de maillages cellulaires réalistes ; (2) en augmentant d'un facteur dix l'efficacité de calcul de la méthode du formalisme matriciel numérique ; (3) en mettant en œuvre une nouvelle méthode de simulation qui fournit une représentation de type Fourier des signaux IRMd ; (4) en proposant un cadre d'apprentissage supervisé basé sur la simulation pour estimer la microstructure du cerveau par IRM de diffusion
Non-invasively estimating brain microstructure that consists of a very large number of neurites, somas, and glial cells is essential for future neuroimaging. Diffusion MRI (dMRI) is a promising technique to probe brain microstructural properties below the spatial resolution of MRI scanners. Due to the structural complexity of brain tissue and the intricate diffusion MRI mechanism, in vivo microstructure estimation is challenging.Existing methods typically use simplified geometries, particularly spheres, and sticks, to model neuronal structures and to obtain analytical expressions of intracellular signals. The validity of the assumptions made by these methods remains undetermined. This thesis aims to facilitate simulationdriven brain microstructure estimation by replacing simplified geometries with realistic neuron geometry models and the analytical intracellular signal expressions with diffusion MRI simulations. Combined with accurate neuron geometry models, numerical dMRI simulations can give accurate intracellular signals and seamlessly incorporate effects arising from, for instance, neurite undulation or water exchange between soma and neurites.Despite these advantages, dMRI simulations have not been widely adopted due to the difficulties in constructing realistic numerical phantoms, the high computational cost of dMRI simulations, and the difficulty in approximating the implicit mappings between dMRI signals and microstructure properties. This thesis addresses the above problems by making four contributions. First, we develop a high-performance opensource neuron mesh generator and make publicly available over a thousand realistic cellular meshes.The neuron mesh generator, swc2mesh, can automatically and robustly convert valuable neuron tracing data into realistic neuron meshes. We have carefully designed the generator to maintain a good balance between mesh quality and size. A neuron mesh database, NeuronSet, which contains 1213 simulation-ready cell meshes and their neuroanatomical measurements, was built using the mesh generator. These meshes served as the basis for further research. Second, we increased the computational efficiency of the numerical matrix formalism method by accelerating the eigendecomposition algorithm and exploiting GPU computing. The speed was increased tenfold. With similar accuracy, the optimized numerical matrix formalism is 20 times faster than the FEM method and 65 times faster than a GPU-based Monte Carlo method. By performing simulations on realistic neuron meshes, we investigated the effect of water exchange between somas and neurites, and the relationship between soma size and signals. We then implemented a new simulation method that provides a Fourier-like representation of the dMRI signals. This method was derived theoretically and implemented numerically. We validated the convergence of the method and showed that the error behavior is consistent with our error analysis. Finally, we propose a simulation-driven supervised learning framework to estimate brain microstructure using diffusion MRI. By exploiting the powerful modeling and computational capabilities that are mentioned above, we have built a synthetic database containing the dMRI signals and microstructure parameters of 1.4 million artificial brain voxels. We have shown that this database can help approximate the underlying mappings of the dMRI signals to volume and surface fractions using artificial neural networks

L'imagerie de diffusion (IRMd) est une méthode unique permettant d'étudier la microstructure cérébrale et la connectivité du cerveau de manière non-invasive. Cependant, la faible résolution et la qualité de cette imagerie restreint son utilisation dans certaines applications. L'objectif de cette thèse est de développer l'IRMd cérébrale à très haute résolution sur un modèle de macaque anesthésié au moyen d'une séquence d'imagerie 3D écho-planaire segmentée (3D-msEPI) à 3T. Après une étape de développement de la reconstruction et du post-traitement des données, nous avons réalisé des images de diffusion sur le cerveau de macaque à une résolution spatiale isotrope de 0.5mm. Cette résolution nous a permis de délimiter et caractériser les structures fines comme les sous-couches de l'hippocampe ou la matière blanche superficielle, qui sont indétectables avec des séquences classiques. Cependant, cette méthode se révèle vulnérable aux mouvements élastiques des tissus cérébraux induits par les pulsations cardio-vasculaires. Une stratégie de synchronisation de l'acquisition sur celle-ci nous a permis de caractériser leurs effets sur l'IRMd à très haute résolution chez le singe anesthésié. Ces effets se caractérisent par des artefacts de ghosting et des pertes de signal qui corrompent les images, le tenseur et la tractographie dans des zones spécifiques du cerveau. La synchronisation nous a ainsi permis de réaliser une imagerie de diffusion cérébrale de macaque à des résolutions spatiales et des pondérations en diffusion très élevées jamais atteintes auparavant. Ces résultats préliminaires démontrent le potentiel de notre méthode pour les applications neuroscientifiques et médicales chez l'homme
Diffusion imaging (dMRI) is a unique method for studying brain microstructure and brain connectivity in a non-invasive way. However, the low resolution and quality of this imaging restricts its use in some applications. The aim of this thesis is to develop very high resolution cerebral MRI on an anesthetized macaque model on a 3T scanner using a segmented 3D echo-planar 3D imaging sequence (3D-msEPI). After a stage of development of the reconstruction and post-processing of the data, we made diffusion images on the macaque brain at an isotropic spatial resolution of 0.5mm. This resolution allowed us to delineate and characterize fine structures such as hippocampal sublayers or superficial white matter, which are undetectable with classical sequences. However, this method is vulnerable to the elastic movements of the brain tissue induced by the cardiovascular pulsations. A strategy of synchronization of the acquisition on this one allowed us to characterize their effects on the very high resolution MRI in the anesthetized monkey. These effects are characterized by ghosting artifacts and signal losses that corrupt images, tensor, and tractography in specific areas of the brain. The synchronization allowed us to realize macaque brain diffusion imaging at spatial resolutions and very high diffusion weights never reached before. These preliminary results demonstrate the potential of our method for neuroscientific and medical applications in humans

Thesis (Ph. D.)--University of California, San Diego and San Diego State University, 2008.
Title from first page of PDF file (viewed April 7, 2008). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 127-149).

Thesis (Ph. D.)--University of California, San Diego and San Diego State University, 2008.
Title from first page of PDF file (viewed September 4, 2008). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 81-94).

Diffusion tensor imaging (DTI) is a specialist MRI modality that can identify microstructural changes or abnormalities in the brain. It can also be used to show fibre tract pathways. Both of these features were used in this thesis. Firstly, standard imaging analysis techniques were used to study the effects of mild, repetitive closed head injury on a group of professional boxers. Such data is extremely rare, so the findings of regions of brain abnormalities in the boxers are important, adding to the body of knowledge about more severe traumatic brain injury. The author developed a novel multivariate analysis technique which was used on the same data. This new technique proved to be more sensitive than the standard univariate methods commonly used. An important part of diagnosing and monitoring brain damage involves the use of biomarkers. A novel investigation of whether diffusion parameters obtained from DTI data could serve as bio-markers of cognitive impairment in Parkinson's disease was conducted. This also involved developing a multivariate approach, which displayed increased sensitivity compared with any of the component parameters used singly, and suggested these diffusion measures could be robust bio-markers of cognitive impairment. Fibre tract connectivity between regions of the brain is also a potentially valuable measure for diagnosis and monitoring brain integrity. The feasibility of this was investigated in a multi-modal MRI study. Functional MRI (fMRI) identifies regions of activation associated with a particular task. DTI can then find the pathway of the fibre bundles connecting these regions. The feasibility of using regional connectivity to interrogate brain integrity was investigated using a single healthy volunteer. Fibre pathways between regions activated and deactivated by a working memory paradigm were determined. Though the results are only preliminary, they suggest that this line of research should be continued.

In recent years there has been a considerable research effort in improving the specificity of magnetic resonance imaging (MRI) techniques by employing quantitative methods. These methods offer greater reproducibility over traditional acquisitions, and hold the potential for obtaining improved information at the microstructural level. However, they typically require a longer duration for the experiments as the quantitative information is often obtained from multiple acquisitions. Here, a multi-echo extension of the MP2RAGE pulse sequence for the simultaneous mapping of T1, T2* (and magnetic susceptibility) is introduced, optimized and validated. This acquisition technique can be faster than the separate acquisition of T1 and T2*, and has the advantage of producing intrinsically co-localized maps. This is helpful in reducing the preprocessing complexity, but most importantly it removes the need for image alignment (registration) which is shown to introduce significant bias in quantitative MRI maps. One of the reasons why the knowledge of T1 and T2* is of relevance in neuroscience is because their reciprocal, R1 and R2*, have been shown to predict quantitatively myelin and iron content in ex vivo experiments using a linear model of relaxation. However, the post-mortem results cannot be applied directly to the in vivo case. Therefore, an adaptation of the linear relaxation model to the in vivo case is proposed. This is capable of predicting (with some limitations) the myelin and iron contents of the brain under in vivo conditions, by using prior knowledge from the literature to calibrate the linear coefficients. The dependence of the relaxation parameters from the biochemical composition in brain tissues is further explored with ex vivo experiments. In particular, the hyaluronan component of the extracellular matrix is investigated. The contribution to T1 and T2* is measured with a sophisticated experiments that allow for a greater control over experimental conditions compared to a typical MRI experiment. The result indicate a small but appreciable contribution of hyaluronan to the relaxation parameters. In conclusion, this work develops a method for measuring T1 and T2* maps simultaneously. These are then used to quantify myelin and iron under in vivo conditions using a linear model of relaxation. In parallel, the hyaluronan-based extracellular matrix was shown to be a marginal but measurable component in T1 and T2* relaxation maps in ex vivo experiments.

碩士
國立臺灣大學
物理研究所
102
Although morbidity and mortality rate of some diseases, such as ischemic cardiac attack or stroke, decrease prominently with improvement in medical technology, disability and suicide rate related to mental diseases remain as before. Therefore, it is crucial for us to achieve a better comprehension of human brains. However, in vivo evaluation of human brains is always difficult and challenging. Magnetic Resonance Imaging (MRI) is the most commonly used method to tackle the mystery of human brains. Diffusion Spectrum Imaging (DSI), a kind of diffusion MRI, is an advanced technique for evaluating microstructure of the human brain, such as axonal direction and general fractional anisotropy (GFA). Its signal in the q space may convey other important biological parameters such as axonal water fraction (AWF). There are lots of researchers who integrate AWF measurement into their models, including Bi-Exponential model, Diffusional Kurtosis Imaging (DKI), CHARMED model, ActiveAx model, and NODDI model. To confirm the reliability of various models, we focus on AWF of the human corpus callosum (CC), in which axonal directions are very coherent. In our first experiment, we acquire a special DSI data, which is 2D in q space as well as in image space. Applying a modified ActiveAx model to our 2D DSI data, we can estimate AWF over the human CC. Our result is consistent to Aboitiz’s observation in histology, demonstrating the feasibility of our experimental setup. In the second experiment, we construct a DKI template from NTU-DSI-122 template, which is a DSI template. Constrained Linear Least Square (CLLS) is chosen for calculating kurtosis tensor elements. In addition, using a tissue model for DKI, we are able to obtain AWF map of whole brain with low computational loads. In conclusion, the proposed methods can facilitate further researches in probing microstructure of the human brain, and hence contribute to early diagnosis of mental or neurological diseases such as multiple sclerosis.

Human brain has been subject of deep interest for centuries, given it's central role in controlling and directing the actions and functions of the body as response to external stimuli. The neural tissue is primarily constituted of neurons and, together with dendrites and the nerve synapses, constitute the gray matter (GM) which plays a major role in cognitive functions. The information processed in the GM travel from one region to the other of the brain along nerve cell projections, called axons. All together they constitute the white matter (WM) whose wiring organization still remains challenging to uncover. The relationship between structure organization of the brain and function has been deeply investigated on humans and animals based on the assumption that the anatomic architecture determine the network dynamics. In response to that, many different imaging techniques raised, among which diffusion-weighted magnetic resonance imaging (DW-MRI) has triggered tremendous hopes and expectations. Diffusion-weighted imaging measures both restricted and unrestricted diffusion, i.e. the degree of movement freedom of the water molecules, allowing to map the tissue fiber architecture in vivo and non-invasively. Based on DW-MRI data, tractography is able to exploit information of the local fiber orientation to recover global fiber pathways, called streamlines, that represent groups of axons. This, in turn, allows to infer the WM structural connectivity, becoming widely used in many different clinical applications as for diagnoses, virtual dissections and surgical planning. However, despite this unique and compelling ability, data acquisition still suffers from technical limitations and recent studies have highlighted the poor anatomical accuracy of the reconstructions obtained with this technique and challenged its effectiveness for studying brain connectivity. The focus of this Ph.D. project is to specifically address these limitations and to improve the anatomical accuracy of the structural connectivity estimates. To this aim, we developed a global optimization algorithm that exploits micro and macro-structure information, introducing an iterative procedure that uses the underlying tissue properties to drive the reconstruction using a semi-global approach. Then, we investigated the possibility to dynamically adapt the position of a set of candidate streamlines while embedding the anatomical prior of trajectories smoothness and adapting the configuration based on the observed data. Finally, we introduced the concept of bundle-o-graphy by implementing a method to model groups of streamlines based on the concept that axons are organized into fascicles, adapting their shape and extent based on the underlying microstructure.

Dissertação de Mestrado Integrado em Engenharia Biomédica apresentada à Faculdade de Ciências e Tecnologia
O Complexo de Esclerose Tuberosa (CET) é uma doença hereditária, resultado de uma mutação num ou ambos os genes Tsc1 e Tsc2. Clinicamente, as manifestações incluem tumores benignos em diversos órgãos, como a pele, o cérebro e os rins. Esta doença apresenta elevada comorbidade com outros distúrbios, conhecidos como distúrbios neuropsiquiátricos associados ao CET (TAND), que incluem hiperatividade, agressão, transtornos do espectro do autismo (TEA) e deficiências intelectuais.A imagem por tensor de difusão (DTI) é uma técnica de ressonância magnética (MRI) que quantifica o movimento anisotrópico de moléculas de água, oferecendo informação estrutural relevante sobre o tecido cerebral. Outras técnicas, nomeadamente a imagem por amostra q generalizada (GQI), que quantifica anisotropia foi também aplicada nesta tese. Para além disto, a espectroscopia de protões por ressonância magnética (1H-MRS) foi utlizada para investigar o perfil neuroquímico cerebral do modelo de murganho Tsc2+/-.No presente estudo, observámos no córtex pré-frontal uma redução da anisotropia fracional (AF) em murganhos machos Tsc2+/-, acompanhada de um aumento na difusividade axial (DA) e difusividade média (DM) na amígdala. No sentido oposto, em fêmeas Tsc2+/- mostraram um aumento de AF e DA cortical. Relativamente aos dados de 1H-MRS, machos transgénicos apresentaram menores níveis de alanina (Ala) e glutationa (GSH) corticais, enquanto em fêmeas transgénicas, foi detetada uma sub-regulação em níveis de ácido gama-aminobutírico (GABA). No hipocampo, murganhos machos Tsc2+/- apresentaram níveis mais elevados de lactato, taurina e inositol. Os nossos resultados levam-nos a assumir que a organização estrutural do cérebro e o perfil neuroquímico do modelo de murganho Tsc2+/- é dependente do sexo. Para além disto, os resultados estão de acordo com o viés masculino nos distúrbios do espetro do autismo. Esta investigação ajuda um melhor entendimento dos efeitos neuronais deste distúrbio, e potencialmente auxilia o desenvolvimento de terapêuticas.
Tuberous sclerosis complex (TSC) is a hereditary disorder, resulting from a mutation in either Tsc1 or Tsc2. This disorder is characterized by benign tumours in several organ systems, such as the skin, brain and kidneys. TSC individuals also present high comorbidity with other disorders, often referred to as TSC-associated neuropsychiatric disorders (TAND), which include hyperactivity, aggression, autism spectrum disorders (ASD), epilepsy and intellectual impairments. Diffusion tensor imaging (DTI) is an MRI-based technique that quantifies the anisotropic movement of water molecules, providing relevant structural information in the brain tissue. Other techniques, namely generalized q-sampling imaging (GQI), that quantify anisotropy was also used in this thesis. Moreover, proton magnetic resonance spectroscopy (1H-MRS) was performed to investigate neurochemical profile of Tsc2+/- mouse model.Here, we observed that in prefrontal cortex there is a reduction of fractional anisotropy (FA) in male Tsc2+/- mice, together with an increase in axial diffusion (AD) and mean diffusivity (MD) in the amygdala. On the other hand, female Tsc2+/- mice displayed augmentation of cortical FA and AD. Concerning 1H-MRS data, we found that transgenic males exhibited lower levels of cortical alanine (Ala) and glutathione (GSH), while in transgenic females, detected a down-regulation of gamma-amino butyric acid (GABA) levels were detected. In the hippocampus, male Tsc2+/- mice showed higher lactate, taurine, and inositol levels, without changes in females.Our results lead us to postulate that brain structural organization and neurochemical profile of Tsc2+/- mouse model are sex-dependent. Furthermore, our results agree with the male bias described in autism spectrum disorders. This work may contribute to better understand the neural effects of Tsc2+/- disorder and potential therapeutic targets, and therefore possibly aid the development of therapeutic approaches.