Dissertations / Theses on the topic '3D brain imaging'
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Matias, Correia T. M. "Assessment and optimisation of 3D optical topography for brain imaging." Thesis, University College London (University of London), 2010. http://discovery.ucl.ac.uk/19496/.
Law, Kwok-wai Albert, and 羅國偉. "3D reconstruction of coronary artery and brain tumor from 2D medical images." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B31245572.
Uthama, Ashish. "3D spherical harmonic invariant features for sensitive and robust quantitative shape and function analysis in brain MRI." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/438.
Olivero, Daniel. "Traumatic brain injury biomarker discovery using mass spectrometry imaging of 3D neural cultures." Thesis, Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/41102.
MomayyezSiahkal, Parya. "3D stochastic completion fields for mapping brain connectivity using diffusion magnetic resonance imaging." Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=110445.
Cette thèse propose un nouveau cadre probabiliste pour la reconstruction de la connectivité anatomique dans le cerveau basée sur les données obtenues avec l'imagerie de diffusion par résonance magnétique. Nous abordons le problème de la tractographie par le point de vue qu'une théorie basée sur des calculs numériques doit se rapporter à la quantité sous jacente qui est mesurée-la diffusion anisotropique des molécules d'eau. Pour atteindre cet objectif, la probabilité de complétion à priori entre deux régions d'intérêt est modélisée par une marche aléatoire tridimensionnelle (3D), représentative du déplacement anisotropique local capturé par l'IRM de diffusion. La marche aléatoire 3D varie selon un ensemble d'équations différentielles stochastiques dont la solution fournit la probabilité de passage entre tous les états dans l'espace, étant donné une source initiale et des régions d'intérêts. Dans un tel modèle, les particules ont tendance à se diriger en ligne droite à chaque passage, avec une légère perturbation dans leur orientation tridimensionnelle provenant de mouvement Brownien dans chaque composante de l'orientation. Étant données initialement une région source et une région finale, les fonctions de densité de probabilité décrivant la vraisemblance de passage par une position et orientation tridimensionnelle sont respectivement nommées champ stochastique source et champ stochastique d'intérêt. Le champ stochastique final est estimé par le produit de ces deux densités et représente la probabilité de passage par un état particulier dans l'espace. Nous montrons que les courbes de maximum de vraisemblance obtenues par le procédé de marche aléatoire directionnelle 3D est la courbe de moindre énergie qui minimise la somme pondérée de la courbature au carrée, de la torsion au carrée et de la longueur. La marche aléatoire directionnelle 3D et ses champ de complétion sont une extension du modèle de complétion de Williams et Jacobs pour la complétion 2D.Nous développons ensuite un modèle de calcul efficace, local et parallellisable pour calculer les champs de complétion stochastiques en exploitant l'équation Fokker-Planck de la marche aléatoire directionnelle 3D. Cette équation différentielle partielle décrit l'évolution de la distribution de probabilité pour les particules de suivre un tel processus aléatoire. De plus, une solution invariante par rotation est proposée en utilisant la base des fonctions harmoniques sphériques afin de capturer la direction sur la sphere. En analogie avec le modèle de complétion 2D, nous introduisions des termes de diffusion additionnels pour rendre les erreurs d'advection spatiales isotropiques. Le champ de complétion stochastique 3D est également adapté plus avant lorsque les données d'orientation sont dense, comme c'est le cas pour l'IRM de diffusion. L'insertion de termes de dérive angulaire dans le processus stochastique global fournit un moyen de calculer les complétions tout en exploitant les informations locales d'orientation accessibles dans chaque voxel. Notre algorithme fournit ainsi une nouvelle mesure de la connectivité entre deux régions d'intérêt en se basant sur la probabilité globale des courbes de complétions entre elles. Nous discutions ensuite d'un modèle alternatif de marche directionnelle aléatoire directionnelle, où la perturbation angulaire provient d'une seule distribution, i.e., une distribution 3D brownienne.Les performances de l'algorithme de champ de complétion sont validées qualitativement et quantitativement sur des données d'IRM de diffusion provenant de fantômes synthétiques et biologiques. Les données humaines acquises in vivo sur 12 patients sont utilisées pour comparer les performances de l'algorithme que nous proposons avec d'autres méthodes de tractographie de l'état de l'art. Nous concluons finalement par une discussion sur les avantages et les limitations de la méthode développée dans cette thèse et suggérons des orientations pour les travaux futurs.
Collins, D. Louis. "3D model-based segmentation of individual brain structures from magnetic resonance imaging data." Thesis, McGill University, 1994. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=28716.
The objective of this thesis is achieved by inverting the traditional segmentation strategy. Instead of matching geometric contours from an idealized atlas directly to the MRI data, segmentation is achieved by identifying the spatial transformation that, under certain constraints, best maps corresponding features between the model and a particular volumetric data set. After automatic recovery of the linear registration transform, the 3-D non-linear transformation is recovered by estimating the local deformation fields, recursively selected by stepping through the entire target volume in a 3D grid pattern, using cross-correlation of invariant intensity features derived from image data. This registration process is performed hierarchically, with each step in decreasing scale refining the fit of the previous step and providing input to the next. When completed, atlas contours defined in the model are mapped through the recovered transformation to segment structures in the original data set and identify them by name.
Experiments for registration and segmentation are presented using simple phantoms, a realistic digital brain phantom as well as human MRI data. The algorithm is used to estimate neuro-anatomical variability, to automatically segment cerebral structures and to create probabilistic representations of the same structures. Validation with manual methods shows that the procedure performs well, is objective and its implementation robust.
Christopoulos, Charitos Andreas. "Brain disease classification using multi-channel 3D convolutional neural networks." Thesis, Linköpings universitet, Statistik och maskininlärning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-174329.
Mayerich, David Matthew. "Acquisition and reconstruction of brain tissue using knife-edge scanning microscopy." Texas A&M University, 2003. http://hdl.handle.net/1969.1/563.
Heinzer, Stefan. "Hierarchical 3D imaging and quantification of brain microvasculature in a mouse model for Alzheimer's disease /." Zürich : ETH, 2007. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=17293.
Nguyen, Peter. "CANNABINOID RECEPTORS IN THE 3D RECONSTRUCTED MOUSE BRAIN: FUNCTION AND REGULATION." VCU Scholars Compass, 2010. http://scholarscompass.vcu.edu/etd/2274.
Rambani, Komal. "Thick brain slice cultures and a custom-fabricated multiphoton imaging system: progress towards development of a 3D hybrot model." Thesis, Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/22702.
D'Souza, Aswin Cletus. "Automated counting of cell bodies using Nissl stained cross-sectional images." [College Station, Tex. : Texas A&M University, 2007. http://hdl.handle.net/1969.1/ETD-TAMU-2035.
Li, Fan. "Segmentation and Symbolic Representation of Brain Vascular Network : Application to ArterioVenous Malformations." Thesis, Paris Est, 2016. http://www.theses.fr/2016PESC1048/document.
The processing and analysis of 3D Rotational Angiographic images (3DRA) of high spatial resolution to facilitate intervention planning in interventional neuroradiology is a new and booming research area. Neuroradiologists need interactive tools for the planning of embolization procedures and the optimization of the guidance of micro-catheters during endovascular interventions. The exploitation of imaging data to help in diagnosis and treatment requires the development of robust algorithms and efficient methods. These methods allow integrating information included in these images in order to extract useful anatomical descriptors during preoperative and peroperative phases.This thesis is dedicated to the development of a complete processing pipeline including segmentation, three-dimensional (3D) reconstruction and symbolic representation of cerebral vessels from 3DRA images, aiming to facilitate the embolization intervention planning for the treatment of cerebral ArterioVenous Malformations (AVMs).The first part of the work is devoted to the study of the different approaches used for the segmentation of vessels. Two segmentation methods are then proposed. First, a 2D slice-by-slice segmentation method is developed, followed by a robust vessel tracking process that enables detecting bifurcations and further following several branches of the same vessel. A mesh based on the Constrained Delaunay triangulation allows then the 3D reconstruction and visualization of the obtained vessels. An automated 3D segmentation method of 3DRA images is then developed, which presents the advantage of being faster and processing the whole 3D volume of images. This method is region growing based. The 3D process starts from an initial pre-segmented slice using the geodesic reconstruction, where the seeds are automatically placed. Finally, a representation of the vasculature is obtained, in which these three entities are clearly visible: the feeding arteries, the draining veins and the nidus.The second part of the thesis is devoted to the symbolic representation of the vessels. The hierarchical study of the skeleton allows giving a graphic description of the cerebral vascular network. From this graphic description, the vessels and their branches are labeled and one or more vessels can be isolated from the rest of network for a more accurate visual analysis, which is not possible with the original 3D reconstructions. Moreover, this improves the determination of the optimal paths for the AVM embolization and reduces the complexity due to the entanglement of the malformed vessels.The complete processing pipeline thus developed leads to a precise 3D description of the vessels. It allows a better understanding of the cerebral vascular network structure and provides the possibility to neuroradiologists of extracting anatomical and geometric descriptors (size, diameter...) of the vessels. Finally, a verification step of the results by a neuroradiology expert enabled clinical validation of the 3D segmentation and reconstruction results. The integration of the developed algorithms in a user-friendly graphical interface should be achieved to allow the exploitation of our results in clinical routine
KAWAI, HISASHI, KIMINORI BOKURA, SHINJI NAGANAWA, and MASAHIRO YAMAZAKI. "VISUALIZATION OF BRAIN WHITE MATTER TRACTS USING HEAVILY T2-WEIGHTED THREE-DIMENSIONAL FLUID-ATTENUATED INVERSION-RECOVERY MAGNETIC RESONANCE IMAGING." Nagoya University School of Medicine, 2014. http://hdl.handle.net/2237/20547.
Walker, Matthew David. "Quantitative dynamic 3D PET scanning of the body and brain using LSO tomographs." Thesis, University of Manchester, 2009. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:78135.
Castelli, Filippo Maria. "3D CNN methods in biomedical image segmentation." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18796/.
Gibert, Guillaume. "Quantification of the Cerebral Perfusion with the Arterial Spin Labelling 3D-MRI method. Quantification of the Cerebral Perfusion with the Arterial Spin Labelling 3D-MRI method." Thesis, KTH, Skolan för teknik och hälsa (STH), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-148020.
Dohmen, Melanie [Verfasser]. "Towards the Reconstruction of Fiber Tracts in the Human Brain by Means of 3D Polarized Light Imaging / Melanie Dohmen." Wuppertal : Universitätsbibliothek Wuppertal, 2013. http://d-nb.info/1045118958/34.
Frost, Stephen Robert. "Diffusion-weighted magnetic resonance imaging with readout-segmented echo-planar imaging." Thesis, University of Oxford, 2012. https://ora.ox.ac.uk/objects/uuid:94421cdc-6bcb-49c2-b9d9-64e016b875f8.
Mercier, Corentin. "Geometrical modeling, simplification and visualization of brain white matter tractograms." Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAT048.
Tractography data (fibers) obtained from diffusion MRI present several challenges.In this thesis, we propose some useful methods and algorithms for simplification, visualization, and manipulation of these data.We introduce a new multi-resolution representation for tractograms, faster, and with higher geometric accuracy than existing simplification approaches.We also investigate various geometric representations and focus on moving least square (MLS) projection with algebraic point set surfaces (APSS), on which we reduce the complexity, allowing for the use of global kernels for analysis and modeling.A segmentation technique using the multi-resolution representation is presented, achieving better reproducibility than other approaches.Tractograms being massive, we also introduce a compression algorithm taking advantage of data obtention from diffusion MRI.The algorithm speed even allows for the direct use of compressed data for visualization, as it can be decompressed on-the-fly on the GPU.This research and the obtained results lie at the intersection between Computer Graphics and Medical Data Analysis, paving the way for numerous perspectives
Manganas, Spyridon. "A Novel Methodology for Timely Brain Formations of 3D Spatial Information with Application to Visually Impaired Navigation." Wright State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=wright1567452284983244.
Morgan, Leah. "Development of a 3D radial MR Imaging sequence to be used for (self) navigation during the scanning of the fetal brain in utero." Master's thesis, University of Cape Town, 2016. http://hdl.handle.net/11427/22735.
Guardiola, Garcia Marta. "Multi-antenna multi-frequency microwave imaging systems for biomedical applications." Doctoral thesis, Universitat Politècnica de Catalunya, 2013. http://hdl.handle.net/10803/134967.
Martin, Matthieu. "Reconstruction 3D de données échographiques du cerveau du prématuré et segmentation des ventricules cérébraux et thalami par apprentissage supervisé." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI118.
About 15 million children are born prematurely each year worldwide. These patients are likely to suffer from brain abnormalities that can cause neurodevelopmental disorders: cerebral palsy, deafness, blindness, intellectual development delay, … Studies have shown that the volume of brain structures is a good indicator which enables to reduce and predict these risks in order to guide patients through appropriate care pathways during childhood. This thesis aims to show that 3D ultrasound could be an alternative to MRI that would enable to quantify the volume of brain structures in all premature infants. This work focuses more particularly on the segmentation of the lateral ventricles (VL) and thalami. Its four main contributions are: the development of an algorithm which enables to create 3D ultrasound data from 2D transfontanellar ultrasound of the premature brain, the segmentation of thigh quality he lateral ventricles and thalami in clinical time and the learning by a convolutional neural networks (CNN) of the anatomical position of the lateral ventricles. In addition, we have created several annotated databases in partnership with the CH of Avignon. Our reconstruction algorithm was used to reconstruct 25 high-quality ultrasound volumes. It was validated in-vivo where an accuracy 0.69 ± 0.14 mm was obtained on the corpus callosum. The best segmentation results were obtained with the V-net, a 3D CNN, which segmented the CVS and the thalami with respective Dice of 0.828± 0.044 and 0.891±0.016 in a few seconds. Learning the anatomical position of the CVS was achieved by integrating a CPPN (Compositional Pattern Producing Network) into the CNNs. It significantly improved the accuracy of CNNs when they had few layers. For example, in the case of the 7-layer V-net network, the Dice has increased from 0.524± 0.076 to 0.724±0.107. This thesis shows that it is possible to automatically segment brain structures of the premature infant into 3D ultrasound data with precision and in a clinical time. This proves that high quality 3D ultrasound could be used in clinical routine to quantify the volume of brain structures and paves the way for studies to evaluate its benefit to patients
Tounekti, Slimane. "Développements des méthodes d'acquisition à haute résolution spatiale en IRM de diffusion." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSE1008/document.
Diffusion MRI (dMRI) is the unique non-invasive technique that allows exploring the cerebral microstructure. Besides a wide use for medical applications, dMRI is also employed in neuroscience to understand the brain organization and connectivity. However, the low spatial resolution and the sensitivity to artefacts limit its application to non-human primates.This work aims to develop a new approach that allows to acquire dMRI at very high spatial resolution on anesthetized macaque brains. This method is based on a 3D sampling of Fourier space with a segmented Echo Planar imaging readout module. This method has been firstly implemented on a 3 Tesla MR scanner (Prisma, Siemens), validated and optimized in-vitro and in-vivo. Compared to the conventional acquisition method, a gain of sensitivity of 3 for the cerebral grey matter and of 4.7 for the white matter was obtained with the proposed approach.This method allowed us to acquire dMRI data on the macaque brain with a spatial isotropic resolution of 0.5 mm ever reached before. The interest to acquire dMRI data with such a spatial resolution to visualize and analyze in-vivo fine structures not detectable with the classical acquisition method, like the sub-fields of hippocampus and the superficial white matter, has also illustrated in this study. Finally, very encouraging preliminary results were also obtained in humans
Habermehl, Christina Verfasser], Jens [Akademischer Betreuer] Steinbrink, Christoph [Akademischer Betreuer] Schmitz, Klaus-Robert [Akademischer Betreuer] [Müller, and Hamid [Akademischer Betreuer] Dehghani. "High-resolution 3D diffuse optical tomography for non-invasive functional brain imaging in the sub-centimeter range / Christina Habermehl. Gutachter: Klaus-Robert Müller ; Hamid Dehghani ; Jens Steinbrink. Betreuer: Jens Steinbrink ; Christoph Schmitz." Berlin : Technische Universität Berlin, 2014. http://d-nb.info/1066549095/34.
Habermehl, Christina [Verfasser], Jens Akademischer Betreuer] Steinbrink, Christoph [Akademischer Betreuer] Schmitz, Klaus-Robert [Akademischer Betreuer] [Müller, and Hamid [Akademischer Betreuer] Dehghani. "High-resolution 3D diffuse optical tomography for non-invasive functional brain imaging in the sub-centimeter range / Christina Habermehl. Gutachter: Klaus-Robert Müller ; Hamid Dehghani ; Jens Steinbrink. Betreuer: Jens Steinbrink ; Christoph Schmitz." Berlin : Technische Universität Berlin, 2014. http://d-nb.info/1066549095/34.
Murtin, Chloé Isabelle. "Traitement d’images de microscopie confocale 3D haute résolution du cerveau de la mouche Drosophile." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSEI081/document.
Although laser scanning microscopy is a powerful tool for obtaining thin optical sections, the possible depth of imaging is limited by the working distance of the microscope objective but also by the image degradation caused by the attenuation of both excitation laser beam and the light emitted from the fluorescence-labeled objects. Several workaround techniques have been employed to overcome this problem, such as recording the images from both sides of the sample, or by progressively cutting off the sample surface. The different views must then be combined in a unique volume. However, a straightforward concatenation is often not possible, because the small rotations that occur during the acquisition procedure, not only in translation along x, y and z axes but also in rotation around those axis, making the fusion uneasy. To address this problem we implemented a new algorithm called 2D-SIFT-in-3D-Space using SIFT (scale Invariant Feature Transform) to achieve a robust registration of big image stacks. Our method register the images fixing separately rotations and translations around the three axes using the extraction and matching of stable features in 2D cross-sections. In order to evaluate the registration quality, we created a simulator that generates artificial images that mimic laser scanning image stacks to make a mock pair of image stacks one of which is made from the same stack with the other but is rotated arbitrarily with known angles and filtered with a known noise. For a precise and natural-looking concatenation of the two images, we also developed a module progressively correcting the sample brightness and contrast depending on the sample surface. Those tools we successfully used to generate tridimensional high resolution images of the fly Drosophila melanogaster brain, in particular, its octopaminergic and dopaminergic neurons and their synapses. Those monoamine neurons appear to be determinant in the correct operating of the central nervous system and a precise and systematic analysis of their evolution and interaction is necessary to understand its mechanisms. If an evolution over time could not be highlighted through the pre-synaptic sites analysis, our study suggests however that the inactivation of one of these neuron types triggers drastic changes in the neural network
Nazaran, Amin. "Ultra Short MR Relaxometry and Histological Image Processing for Validation of Diffusion MRI." BYU ScholarsArchive, 2016. https://scholarsarchive.byu.edu/etd/6348.
ARAÚJO, Caio Fernandes. "Segmentação de imagens 3D utilizando combinação de imagens 2D." Universidade Federal de Pernambuco, 2016. https://repositorio.ufpe.br/handle/123456789/21040.
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CAPES
Segmentar imagens de maneira automática é um grande desafio. Apesar do ser humano conseguir fazer essa distinção, em muitos casos, para um computador essa divisão pode não ser tão trivial. Vários aspectos têm de ser levados em consideração, que podem incluir cor, posição, vizinhanças, textura, entre outros. Esse desafio aumenta quando se passa a utilizar imagens médicas, como as ressonâncias magnéticas, pois essas, além de possuírem diferentes formatos dos órgãos em diferentes pessoas, possuem áreas em que a variação da intensidade dos pixels se mostra bastante sutil entre os vizinhos, o que dificulta a segmentação automática. Além disso, a variação citada não permite que haja um formato pré-definido em vários casos, pois as diferenças internas nos corpos dos pacientes, especialmente os que possuem alguma patologia, podem ser grandes demais para que se haja uma generalização. Mas justamente por esse possuírem esses problemas, são os principais focos dos profissionais que analisam as imagens médicas. Este trabalho visa, portanto, contribuir para a melhoria da segmentação dessas imagens médicas. Para isso, utiliza a ideia do Bagging de gerar diferentes imagens 2D para segmentar a partir de uma única imagem 3D, e conceitos de combinação de classificadores para uni-las, para assim conseguir resultados estatisticamente melhores, se comparados aos métodos populares de segmentação. Para se verificar a eficácia do método proposto, a segmentação das imagens foi feita utilizando quatro técnicas de segmentação diferentes, e seus resultados combinados. As técnicas escolhidas foram: binarização pelo método de Otsu, o K-Means, rede neural SOM e o modelo estatístico GMM. As imagens utilizadas nos experimentos foram imagens reais, de ressonâncias magnéticas do cérebro, e o intuito do trabalho foi segmentar a matéria cinza do cérebro. As imagens foram todas em 3D, e as segmentações foram feitas em fatias 2D da imagem original, que antes passa por uma fase de pré-processamento, onde há a extração do cérebro do crânio. Os resultados obtidos mostram que o método proposto se mostrou bem sucedido, uma vez que, em todas as técnicas utilizadas, houve uma melhoria na taxa de acerto da segmentação, comprovada através do teste estatístico T-Teste. Assim, o trabalho mostra que utilizar os princípios de combinação de classificadores em segmentações de imagens médicas pode apresentar resultados melhores.
Automatic image segmentation is still a great challenge today. Despite the human being able to make this distinction, in most of the cases easily and quickly, to a computer this task may not be that trivial. Several characteristics have to be taken into account by the computer, which may include color, position, neighborhoods, texture, among others. This challenge increases greatly when it comes to using medical images, like the MRI, as these besides producing images of organs with different formats in different people, have regions where the intensity variation of pixels is subtle between neighboring pixels, which complicates even more the automatic segmentation. Furthermore, the above mentioned variation does not allow a pre-defined format in various cases, because the internal differences between patients bodies, especially those with a pathology, may be too large to make a generalization. But specially for having this kind of problem, those people are the main targets of the professionals that analyze medical images. This work, therefore, tries to contribute to the segmentation of medical images. For this, it uses the idea of Bagging to generate different 2D images from a single 3D image, and combination of classifiers to unite them, to achieve statistically significant better results, if compared to popular segmentation methods. To verify the effectiveness of the proposed method, the segmentation of the images is performed using four different segmentation techniques, and their combined results. The chosen techniques are the binarization by the Otsu method, K-Means, the neural network SOM and the statistical model GMM. The images used in the experiments were real MRI of the brain, and the dissertation objective is to segment the gray matter (GM) of the brain. The images are all in 3D, and the segmentations are made using 2D slices of the original image that pass through a preprocessing stage before, where the brain is extracted from the skull. The results show that the proposed method is successful, since, in all the applied techniques, there is an improvement in the accuracy rate, proved by the statistical test T-Test. Thus, the work shows that using the principles of combination of classifiers in medical image segmentation can obtain better results.
Kadalie, Emile. "Development of multi-parametric human MRI at 3T." Electronic Thesis or Diss., Bordeaux, 2023. http://www.theses.fr/2023BORD0493.
Magnetic Resonance Imaging (MRI) is a method of choice for the diagnosis, prognosis and monitoring of pathologies in soft tissues. Indeed, strong contrasts between tissues are obtained and can be modulated on demand. This imaging technique has not shown any harmful influence, enabling repetitive high-resolution exams.Quantitative MRI (qMRI) has become incredibly interesting these last ten years, as it provides maps in which each pixel contains a measurement of a physical parameter. As such, contrarily to conventional images obtained on a gray scale, this MR quantification can be employed to obtain objective interpretations of the images, and to provide a scale for comparing time points and patients.Among the physics parameters that can be quantified, the longitudinal (T1) and the transversal (T2) relaxation times are the most common.The Dual Echo Steady State (DESS) sequence has often been used in musculo-skeletal MRI to rapidly obtain high-contrast morphological images and 3D quantitative T2 mapping. By acquiring two distinct steady-state free precession echoes, two images with different contrasts are built, whose ratio can be used to procure T2 maps by matching it to a computed dictionary of many simulated signals. Nonetheless, the sequence has often been described as sensitive to physiological motion, generating artifacts as well as discrepancies in T2 values. Consequently, the main objective of my work was to implement a rapid 3D DESS sequence for brain imaging that enables T2 estimation accurately and with high repeatability at 3T.To do so, I first identified the cause of the ghosting artifact present in the DESS brain images, through the insertion of a Self-Gating (SG) module. As, its phase was correlated to the signal retrieved from a respiratory belt, B0 variations due to breathing were consequently identified as the source of the ghosting on the brain images.To correct this artifact, a Compressed Sensing dedicated trajectory was implemented so as to retrospectively bin the data into multiple respiratory phases, and reconstruct undersampled k-spaces into images with high quality. To increase the repeatability of the method, the conventional Cartesian encoding was replaced by a Spiral Cartesian encoding, which further distributed the artifact as noise on the images. 3D T2 maps were then acquired with a spatial resolution of 1.2mm isotropic, and were of high quality and repeatable on all the volunteers.The new methodology was then implemented on a 1.5T and a 7T Siemens scanners. This proved that the corrected DESS method set in place during this PhD can be used on images obtained from different scanners, and different magnetic fields.Despite these improvements, the brain T2 values remained short compared to a Spin-Echo sequence. To investigate this issue, several simulations were performed and highlighted the influence of B0 variations in the T2 errors, as well as the high sensitivity to short T2 components. To correct the measurements, a dictionary taking into account multiple B0 variations was created. This will be useful in the case where the DESS sequence is applied on a moving organ like the liver.Also, as additional sequences have to be inserted into the protocol to correct the T2 measurements, a simultaneous T1 and T2 estimation was investigated. To reach this goal, the gradient echo trains within the MP2RAGE sequence were replaced by DESS trains. Sequence parameter optimization was explored via the Cramer Rao Lower Bound method so as to obtain both accurate T1 and T2 maps.In conclusion, through the implementation of a self-gating module, a spiral Cartesian encoding and a Compressed-Sensing acceleration, the new DESS sequence allows a rapid and repeatable estimation of T2 in the whole brain in 3D at multiple magnetic fields.Further improvements are needed to improve the T2 accuracy, and to simultaneously measure multiple quantitative parameters though the application of one sequence
Pinto, Sílvia Cristina Dias. "Análise de formas 3D usando wavelets 1D, 2D e 3D." Universidade de São Paulo, 2005. http://www.teses.usp.br/teses/disponiveis/76/76132/tde-02052007-085441/.
This work presents new methods for three-dimensional shape analysis in the context of computational vision, being emphasized the use of 1D, 2D and 3D wavelet transforms, which provide a multiscale analysis of the studied shapes. The analyzed shapes are divided in three different types depending on their representation: f(t)=(x(t),y(t),z(t)), f(x,y)=z and f(x,y,z)=w. Each type of shape is analyzed by a more suitable method. Firstly, such shapes undergo a pre-processing procedure followed by the characterization using the 1D, 2D or 3D wavelet transform, depending on its representation. This application allows to extract features that are rotation- and translation-invariant, based on some mathematical concepts of differential geometry. In this work, we emphasize that it is not necessary to use the parameterized version of the 2D and 3D shapes. The experimental results obtained from shapes extracted from medical and biological images, that corroborate the introduced methods, are presented.
Bartikian, Mickael Varoujan. "3D atlas of the human brain : ex vivo magnetic resonance imaging." Master's thesis, 2020. http://hdl.handle.net/10451/48284.
Introdução: Os atlas 3D do cérebro são ferramentas essenciais na investigação em neurociências humanas, no ensino da neuroanatomia e no planeamento cirúrgico. Fornecem níveis macroscópicos/microscópicos de informação numa estrutura espacial e permitem a visualização de volumes reconstruídos a partir de dados de Ressonância Magnética (RM), bem como de estudos histológicos. A fusão das duas modalidades combina a precisão dimensional da RM com o detalhe das secções histológicas. Com a evolução da tecnologia de processamento de imagem, é relevante renovar os dados e focar em estruturas anteriormente menos exploradas em atlas, como o hipotálamo, o núcleo basal de Meynert e o núcleo accumbens. Embora existam muitos estudos cerebrais multimodais, vários aspectos podem ser optimizados na preparação do cérebro de cadáver para a RM. Objectivos: Este trabalho constitui a primeira parte da elaboração de um atlas 3D do cérebro humano combinando RM e histologia. O objectivo foi construir um recipiente personalizado para segurar e posicionar um cérebro humano de cadáver adulto na RM, minimizando artefactos de movimento e realizar aquisições volumétricas. Métodos: Um recipiente personalizado foi desenhado e fabricado por impressão 3D para colocar um cérebro de cadáver na RM. Aquisições volumétricas foram obtidas com as sequências T1-MPGR, FGATIR e T2-FLAIR e comparadas quanto ao detalhe anatómico por contraste de substância branca e cinzenta. Resultados: O recipiente do cérebro foi implementado com sucesso, reduzindo a complexidade do protocolo de preparação, e o cérebro foi estabilizado correctamente durante a ressonância magnética. Discussão: Com melhorias, o uso de um recipiente personalizado fabricado por impressão 3D pode ajudar na optimização e padronização da preparação de cérebros de cadáver para RM. As sequências T1-MPGR e FGATIR forneceram a melhor qualidade para detalhes anatómicos Conclusão: A criação do recipiente abordou vários problemas da preparação para RM de cérebro de cadáver. As imagens foram obtidas com sucesso e serão usadas nas etapas seguintes do projecto.
Introduction: 3D brain atlases are essential tools in human brain research, neuroanatomy teaching, and surgical planning. They can provide macroscopic/microscopic levels of information in a spatial framework and allow visualisation of reconstructed volumes from MRI data of in/ex vivo brains as well as histological studies. Fusion of both modalities combines the dimensional accuracy of MRI with the detail of histological sections. As image processing technology evolves, it is relevant to renew the data and focus on previously less explored structures, such as the hypothalamus, the nucleus basalis of Meynert and the nucleus accumbens. Although many multimodal brain studies exist, several aspects of the post-mortem preparation for MRI can be optimised. Objectives: This work is the first part of the construction of a 3D atlas of the human brain combining ex vivo MRI and histology. The goal was to build an MRI compatible custom-made container to securely hold an ex vivo adult human brain, minimising movement artifacts, and to perform an MRI scan of the brain. Methods: A custom-shaped container was designed and 3D printed to hold an ex vivo human brain in position in the MRI scanner. Volumetric acquisitions were obtained with T1-MPGR, FGATIR, and T2-FLAIR pulse sequences and compared for their anatomical detail by grey matter to white matter contrast. Results: The brain container was successfully implemented, reducing the complexity of the preparation protocol, and the brain was correctly positioned and stabilised during MRI scanning. Discussion: With some improvements, the use of a 3D-printed custom container can be a step towards the optimisation and standardisation of ex vivo brain preparation for MRI. The T1-MPGR and FGATIR sequences provided the best image quality for anatomical detail Conclusion: Designing the container addressed several issues with ex vivo brain preparation for MRI. Imaging data were successfully obtained and will be used in the following steps of the 3D brain atlas project.
Lindsey, Brooks. "The Ultrasound Brain Helmet: Simultaneous Multi-transducer 3D Transcranial Ultrasound Imaging." Diss., 2012. http://hdl.handle.net/10161/6129.
In this work, I examine the problem of rapid imaging of stroke and present ultrasound-based approaches for addressing it. Specifically, this dissertation discusses aberration and attenuation due to the skull as sources of image degradation and presents a prototype system for simultaneous 3D bilateral imaging via both temporal acoustic windows. This system uses custom sparse array transducers built on flexible multilayer circuits that can be positioned for simultaneous imaging via both temporal acoustic windows, allowing for registration and fusion of multiple real-time 3D scans of cerebral vasculature. I examine hardware considerations for new matrix arrays--transducer design and interconnects--in this application. Specifically, it is proposed that signal-to-noise ratio (SNR) may be increased by reducing the length of probe cables. This claim is evaluated as part of the presented system through simulation, experimental data, and in vivo imaging. Ultimately, gains in SNR of 7 dB are realized by replacing a standard probe cable with a much shorter flex interconnect; higher gains may be possible using ribbon-based probe cables. In vivo images are presented depicting cerebral arteries with and without the use of microbubble contrast agent that have been registered and fused using a search algorithm which maximizes normalized cross-correlation.
The scanning geometry of a brain helmet-type system is also utilized to allow each matrix array to serve as a correction source for the opposing array. Aberration is estimated using cross-correlation of RF channel signals followed by least mean squares solution of the resulting overdetermined system. Delay maps are updated and real-time 3D scanning resumes. A first attempt is made at using multiple arrival time maps to correct multiple unique aberrators within a single transcranial imaging volume, i.e. several isoplanatic patches. This adaptive imaging technique, which uses steered unfocused waves transmitted by the opposing or "beacon" array, updates the transmit and receive delays of 5 isoplanatic patches within a 64°×64° volume. In phantom experiments, color flow voxels above a common threshold have increased by an average of 92% while color flow variance decreased by an average of 10%. This approach has been applied to both temporal acoustic windows of two human subjects, yielding increases in echo brightness in 5 isoplanatic patches with a mean value of 24.3 ± 9.1%, suggesting such a technique may be beneficial in the future for improving image quality in non-invasive 3D color flow imaging of cerebrovascular disease including stroke.
Acoustic window failure and the possibility of overcoming it using a low frequency, large aperture array are also examined. In performing transcranial ultrasound examinations, 8-29% of patients in a general population may present with window failure, in which it is not possible to acquire clinically useful sonographic information through the temporal acoustic window. The incidence of window failure is higher in the elderly and in populations of African descent, making window failure an important concern for stroke imaging through the intact skull. To this end, I describe the technical considerations, design, and fabrication of low-frequency (1.2 MHz), large aperture (25.3 mm) sparse matrix array transducers for 3D imaging in the event of window failure. These transducers are integrated into the existing system for real-time 3D bilateral transcranial imaging and color flow imaging capabilities at 1.2 MHz are directly compared with arrays operating at 1.8 MHz in a flow phantom with approximately 47 dB/cm0.8/MHz0.8 attenuators. In vivo contrast-enhanced imaging allowed visualization of the arteries of the Circle of Willis in 5 of 5 subjects and 8 of 10 sides of the head despite probe placement outside of the acoustic window. Results suggest that the decrease from approximately 2 to 1 MHz for 3D transcranial ultrasound may be sufficient to allow acquisition of useful images either in individuals with poor windows or outside of the temporal acoustic window by untrained operators in the field.
Dissertation
Bouchard, Matthew Bryan. "2D and 3D high-speed multispectral optical imaging systems for in-vivo biomedical research." Thesis, 2014. https://doi.org/10.7916/D8D798G5.
Eissa, Amir. "Investigation of gradient echo MRI for blood vessel imaging and susceptibility-weighted imaging in the human brain." Phd thesis, 2010. http://hdl.handle.net/10048/1131.
Title from pdf file main screen (viewed on July 17, 2010). A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Doctor of Philosophy, [Department of] Physics, University of Alberta. Includes bibliographical references.
Chen, An-chia, and 陳安嘉. "Image Fusion and 3D Visualization for Head and Neck Magnetic Resonance Angiography and Brain Magnetic Resonance Imaging." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/86501586160936575238.
雲林科技大學
工業工程與管理研究所碩士班
96
In Taiwan, there are about 13,000 people per year die from cerebrovascular diseases, the second highest cause of death. Magnetic Resonance Angiography (MRA) is one of the main tools for diagnosis of cerebrovascular diseases, which can provide a complete view of bilateral internal carotid artery and intracranial vessel. Because of the sampling constraints, MRA axial plane only presents a smaller scope of intracranial vessel imaging. Head and neck integrity of the vascular diagnostic information often need comparison with the axial and coronal plane MRA. Brain tumor invasion of the central nervous disease is the most common one, whether benign or malignant tumor, may threaten the patient''s life. Most of brain tumors were growing slowly, patients are difficult to detect early, and when symptoms appear the brain tumors are usually become large. Sometimes, brain tumors will surround the nerves or vessels that resulted in treatment more difficult. In the clinically, Magnetic Resonance Imaging (MRI) is the main tools for diagnosis the brain tumors of the region. In this thesis, we expect to align the axial and coronal plane MRA in order to obtain the complete head and neck vascular information. And brain tumors with blood vessels or nerve actual relative position in the treatment of very important. Therefore, through brain tumors and head and neck vascular imaging integration, can be clearly observed that the brain tumor will move the vessel, or including the vessel. The performance of hybrid Particle Swarm Optimization (hPSO), particle swarm optimization (PSO) and genetic algorithm (GA) for optimal alignment parameters is compared, to enhance clinical research and physician preoperative planning. In this paper, the alignment quality performance of measurement is used root mean square error (RMSE) to measure the algorithms'' quality. The results of performance comparison showed that hPSO outperformed PSO and GA in alignment quality. This paper used pulse coupled neural network (PCNN) algorithms to segment the axial plane vascular image, and used active contours using level sets (ACLS) algorithms to segment the brain tumor image. Finally, the optimal geometric transformation parameters used in brain tumors and head and neck vascular imaging integration. Meanwhile, the fused information is presented as the form of 3D visualization, which provides the integrated information for clinical diagnosis and medical research.
Lindvere, Liis. "Functional Stimulation Induced Change in Cerebral Blood Volume: A Two Photon Fluorescence Microscopy Map of the 3D Microvascular Network Response." Thesis, 2011. http://hdl.handle.net/1807/31314.