Relevant bibliographies by topics / 3D brain imaging

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic '3D brain imaging'

Author: Grafiati
Published: 7 July 2024
Last updated: 7 July 2024

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Journal articles on the topic "3D brain imaging"

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Sumithra, M., P. Madhumitha, S. Madhumitha, D. Malini, and B. Poorni Vinayaa. "3D Segmentation of Brain Tumor Imaging." International Journal of Advanced Engineering, Management and Science 6, no. 6 (2020): 256–60. http://dx.doi.org/10.22161/ijaems.66.5.

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Kakeda, Shingo, Yukunori Korogi, Yasuhiro Hiai, Norihiro Ohnari, Toru Sato, and Toshinori Hirai. "Pitfalls of 3D FLAIR Brain Imaging." Academic Radiology 19, no. 10 (2012): 1225–32. http://dx.doi.org/10.1016/j.acra.2012.04.017.

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Taranda, Julian, and Sevin Turcan. "3D Whole-Brain Imaging Approaches to Study Brain Tumors." Cancers 13, no. 8 (2021): 1897. http://dx.doi.org/10.3390/cancers13081897.

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Although our understanding of the two-dimensional state of brain tumors has greatly expanded, relatively little is known about their spatial structures. The interactions between tumor cells and the tumor microenvironment (TME) occur in a three-dimensional (3D) space. This volumetric distribution is important for elucidating tumor biology and predicting and monitoring response to therapy. While static 2D imaging modalities have been critical to our understanding of these tumors, studies using 3D imaging modalities are needed to understand how malignant cells co-opt the host brain. Here we summarize the preclinical utility of in vivo imaging using two-photon microscopy in brain tumors and present ex vivo approaches (light-sheet fluorescence microscopy and serial two-photon tomography) and highlight their current and potential utility in neuro-oncology using data from solid tumors or pathological brain as examples.
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Pooh, Ritsuko K. "Three-dimensional Evaluation of the Fetal Brain." Donald School Journal of Ultrasound in Obstetrics and Gynecology 11, no. 4 (2017): 268–75. http://dx.doi.org/10.5005/jp-journals-10009-1532.

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ABSTRACT Three-dimensional (3D) ultrasound is one of the most attractive modalities in the field of fetal ultrasound imaging. Combination of both transvaginal sonography and 3D ultrasound may be a great diagnostic tool for evaluation of 3D structure of fetal central nervous system (CNS). Recent advanced 3D ultrasound equipments have several useful functions, such as surface anatomy imaging; multiplanar imaging of the intracranial structure; tomographic ultrasound imaging of fetal brain in the any cutting section; bony structural imaging of the calvaria and vertebrae; thick slice imaging of the intracranial structure; simultaneous volume contrast imaging of the same section or vertical section of fetal brain structure; volume calculation of target organs, such as intracranial cavity, ventricle, choroid plexus, and intracranial lesions; and 3D sonoangiography of the brain circulation (3D power or color Doppler). Furthermore, recent advanced technologies, such as HDlive silhouette and HDlive flow are quite attractive modalities and they can be applied for neuroimaging. Up-to-date 3D technologies described in this study allow extending the detection of congenital brain maldevelopment, and it is beyond description that noninvasive direct viewing of the embryo/fetus by all-inclusive ultrasound technology is definitely the first modality in a field of fetal neurology and helps our goal of proper perinatal care and management, even in the era of molecular genetics and advanced sequencing of fetal deoxyribonucleic acid (DNA) in the maternal blood. As a future aspect, collaboration of both molecular genetics and 3D neuroimaging will reveal responsible gene mutation of neuronal migration disorder, and this fetal neuro-sono-genetics will be able to contribute to accurate diagnoses, proper management, possible genetic therapy, and prophylaxis. How to cite this article Pooh RK. Three-dimensional Evaluation of the Fetal Brain. Donald School J Ultrasound Obstet Gynecol 2017;11(4):268-275.
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Yao, Junjie. "Deep-brain imaging with 3D integrated photoacoustic tomography and ultrasound localization microscopy." Journal of the Acoustical Society of America 155, no. 3_Supplement (2024): A53. http://dx.doi.org/10.1121/10.0026774.

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Photoacoustic computed tomography (PACT) is a proven technology for imaging hemodynamics in deep brain of small animal models. PACT is inherently compatible with ultrasound (US) imaging, providing complementary contrast mechanisms. While PACT can quantify the brain’s oxygen saturation of hemoglobin (sO2), US imaging can probe the blood flow based on the Doppler effect. Furthermore, by tracking gas-filled microbubbles, ultrasound localization microscopy (ULM) can map the blood flow velocity with sub-diffraction spatial resolution. In this work, we present a 3D deep-brain imaging system that seamlessly integrates PACT and ULM into a single device, 3D-PAULM. Using a low ultrasound frequency of 4 MHz, 3D-PAULM is capable of imaging the whole-brain hemodynamic functions with intact scalp and skull in a totally non-invasive manner. Using 3D-PAULM, we studied the mouse brain functions with ischemic stroke. Multi-spectral PACT, US B-mode imaging, microbubble-enhanced power Doppler (PD), and ULM were performed on the same mouse brain with intrinsic image co-registration. From the multi-modality measurements, we future quantified blood perfusion, sO2, vessel density, and flow velocity of the mouse brain, showing stroke-induced ischemia, hypoxia, and reduced blood flow. We expect that 3D-PAULM can find broad applications in studying deep brain functions on small animal models.
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Avasarala, Jagannadha, and Todd Pietila. "The first 3D printed multiple sclerosis brain: Towards a 3D era in medicine." F1000Research 6 (August 30, 2017): 1603. http://dx.doi.org/10.12688/f1000research.12336.1.

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Conventional magnetic resonance imaging (MRI) studies depict disease of the human brain in 2D but the reconstruction of a patient’s brain stricken with multiple sclerosis (MS) in 3D using 2D images has not been attempted. Using 3D reconstruction algorithms, we built a 3D printed patient-specific brain model to scale. It is a first of its kind model that depicts the total white matter lesion (WML) load using T2 FLAIR images in an MS patient. The patient images in Digital Imaging and Communications in Medicine (DICOM) format were imported into Mimics inPrint 2.0 (Materialise NV, Leuven, Belgium) a dedicated medical image processing software for the purposes of image segmentation and 3D modeling. The imported axial images were automatically formatted to display coronal and sagittal slices within the software. The imaging study was then segmented into regions and surface rendered to achieve 3D virtual printable files of the desired structures of interest. Rendering brain tumor(s) in 3D has been attempted with the specific intent of extending the options available to a surgeon but no study to our knowledge has attempted to quantify brain disease in MS that has, for all practical purposes, no surgical options.
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Avasarala, Jagannadha, and Todd Pietila. "The first 3D printed multiple sclerosis brain: Towards a 3D era in medicine." F1000Research 6 (September 20, 2017): 1603. http://dx.doi.org/10.12688/f1000research.12336.2.

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Conventional magnetic resonance imaging (MRI) studies depict disease of the human brain in 2D but the reconstruction of a patient’s brain stricken with multiple sclerosis (MS) in 3D using 2D images has not been attempted. Using 3D reconstruction algorithms, we built a 3D printed patient-specific brain model to scale. It is a first of its kind model that depicts the total white matter lesion (WML) load using T2 FLAIR images in an MS patient. The patient images in Digital Imaging and Communications in Medicine (DICOM) format were imported into Mimics inPrint 2.0 (Materialise NV, Leuven, Belgium) a dedicated medical image processing software for the purposes of image segmentation and 3D modeling. The imported axial images were automatically formatted to display coronal and sagittal slices within the software. The imaging study was then segmented into regions and surface rendered to achieve 3D virtual printable files of the desired structures of interest. Rendering brain tumor(s) in 3D has been attempted with the specific intent of extending the options available to a surgeon but no study to our knowledge has attempted to quantify brain disease in MS that has, for all practical purposes, no surgical options.
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Ren, Jiahao, Xiaocen Wang, Chang Liu, et al. "3D Ultrasonic Brain Imaging with Deep Learning Based on Fully Convolutional Networks." Sensors 23, no. 19 (2023): 8341. http://dx.doi.org/10.3390/s23198341.

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Compared to magnetic resonance imaging (MRI) and X-ray computed tomography (CT), ultrasound imaging is safer, faster, and more widely applicable. However, the use of conventional ultrasound in transcranial brain imaging for adults is predominantly hindered by the high acoustic impedance contrast between the skull and soft tissue. This study introduces a 3D AI algorithm, Brain Imaging Full Convolution Network (BIFCN), combining waveform modeling and deep learning for precise brain ultrasound reconstruction. We constructed a network comprising one input layer, four convolution layers, and one pooling layer to train our algorithm. In the simulation experiment, the Pearson correlation coefficient between the reconstructed and true images was exceptionally high. In the laboratory, the results showed a slightly lower but still impressive coincidence degree for 3D reconstruction, with pure water serving as the initial model and no prior information required. The 3D network can be trained in 8 h, and 10 samples can be reconstructed in just 12.67 s. The proposed 3D BIFCN algorithm provides a highly accurate and efficient solution for mapping wavefield frequency domain data to 3D brain models, enabling fast and precise brain tissue imaging. Moreover, the frequency shift phenomenon of blood may become a hallmark of BIFCN learning, offering valuable quantitative information for whole-brain blood imaging.
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de Crespigny, Alex, Hani Bou-Reslan, Merry C. Nishimura, Heidi Phillips, Richard A. D. Carano, and Helen E. D’Arceuil. "3D micro-CT imaging of the postmortem brain." Journal of Neuroscience Methods 171, no. 2 (2008): 207–13. http://dx.doi.org/10.1016/j.jneumeth.2008.03.006.

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Miao, Peng, Zhixia Wu, Miao Li, et al. "Synchrotron Radiation X-Ray Phase-Contrast Tomography Visualizes Microvasculature Changes in Mice Brains after Ischemic Injury." Neural Plasticity 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/3258494.

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Imaging brain microvasculature is important in plasticity studies of cerebrovascular diseases. Applying contrast agents, traditionalμCT andμMRI methods gain imaging contrast for vasculature. The aim of this study is to develop a synchrotron radiation X-ray inline phase-contrast tomography (SRXPCT) method for imaging the intact mouse brain (micro)vasculature in high resolution (~3.7 μm) without contrast agent. A specific preparation protocol was proposed to enhance the phase contrast of brain vasculature by using density difference over gas-tissue interface. The CT imaging system was developed and optimized to obtain 3D brain vasculature of adult male C57BL/6 mice. The SRXPCT method was further applied to investigate the microvasculature changes in mouse brains (n=14) after 14-day reperfusion from transient middle cerebral artery occlusion (tMCAO). 3D reconstructions of brain microvasculature demonstrated that the branching radius ratio (post- to preinjury) of small vessels (radius < 7.4 μm) in the injury group was significantly smaller than that in the sham group (p<0.05). This result revealed the active angiogenesis in the recovery brain after stroke. As a high-resolution and contrast-agent-free method, the SRXPCT method demonstrates higher potential in investigations of functional plasticity in cerebrovascular diseases.
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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/.

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Optical topography has recently evolved into a widespread research tool for non-invasively mapping blood flow and oxygenation changes in the adult and infant cortex. The work described in this thesis has focused on assessing the potential and limitations of this imaging technique, and developing means of obtaining images which are less artefactual and more quantitatively accurate. Due to the diffusive nature of biological tissue, the image reconstruction is an ill-posed problem, and typically under-determined, due to the limited number of optodes (sources and detectors). The problem must be regularised in order to provide meaningful solutions, and requires a regularisation parameter (\lambda), which has a large influence on the image quality. This work has focused on three-dimensional (3D) linear reconstruction using zero-order Tikhonov regularisation and analysis of different methods to select the regularisation parameter. The methods are summarised and applied to simulated data (deblurring problem) and experimental data obtained with the University College London (UCL) optical topography system. This thesis explores means of optimising the reconstruction algorithm to increase imaging performance by using spatially variant regularisation. The sensitivity and quantitative accuracy of the method is investigated using measurements on tissue-equivalent phantoms. Our optical topography system is based on continuous-wave (CW) measurements, and conventional image reconstruction methods cannot provide unique solutions, i.e., cannot separate tissue absorption and scattering simultaneously. Improved separation between absorption and scattering and between the contributions of different chromophores can be obtained by using multispectral image reconstruction. A method is proposed to select the optimal wavelength for optical topography based on the multispectral method that involves determining which wavelengths have overlapping sensitivities. Finally, we assess and validate the new three-dimensional imaging tools using in vivo measurements of evoked response in the infant brain.
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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.

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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.

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A novel framework for quantitative analysis of shape and function in magnetic resonance imaging (MRI) of the brain is proposed. First, an efficient method to compute invariant spherical harmonics (SPHARM) based feature representation for real valued 3D functions was developed. This method addressed previous limitations of obtaining unique feature representations using a radial transform. The scale, rotation and translation invariance of these features enables direct comparisons across subjects. This eliminates need for spatial normalization or manually placed landmarks required in most conventional methods [1-6], thereby simplifying the analysis procedure while avoiding potential errors due to misregistration. The proposed approach was tested on synthetic data to evaluate its improved sensitivity. Application on real clinical data showed that this method was able to detect clinically relevant shape changes in the thalami and brain ventricles of Parkinson's disease patients. This framework was then extended to generate functional features that characterize 3D spatial activation patterns within ROIs in functional magnetic resonance imaging (fMRI). To tackle the issue of intersubject structural variability while performing group studies in functional data, current state-of-the-art methods use spatial normalization techniques to warp the brain to a common atlas, a practice criticized for its accuracy and reliability, especially when pathological or aged brains are involved [7-11]. To circumvent these issues, a novel principal component subspace was developed to reduce the influence of anatomical variations on the functional features. Synthetic data tests demonstrate the improved sensitivity of this approach over the conventional normalization approach in the presence of intersubject variability. Furthermore, application to real fMRI data collected from Parkinson's disease patients revealed significant differences in patterns of activation in regions undetected by conventional means. This heightened sensitivity of the proposed features would be very beneficial in performing group analysis in functional data, since potential false negatives can significantly alter the medical inference. The proposed framework for reducing effects of intersubject anatomical variations is not limited to functional analysis and can be extended to any quantitative observation in ROIs such as diffusion anisotropy in diffusion tensor imaging (DTI), thus providing researchers with a robust alternative to the controversial normalization approach.
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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.

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Biomarker research is of great interest in the field of traumatic brain injury (TBI), since there are numerous potential markers that may indicate central nervous system damage, yet the brain is normally well isolated and discovery is at its infancy. Traditional methods for biomarker discovery include time consuming multi step chromatographic mass spectrometery (MS) techniques or pre-defined serial probing using traditional assays, making the identification of biomarker panels limiting and expensive. These shortfalls have motivated the development of a MS based probe that can be embedded into 3D neural cultures and obtain temporal and spatial information about the release of biomarkers. Using the high sensitivity MS ionization method of nano-electrospray ionization (nano-ESI) with an in-line microdialysis (MD) unit allows us to use MS to analyze low concentrations of TBI biomarkers from within cell cultures with no need for off-line sample manipulation. This thesis goes through the development of the probe by studying the theoretical principles, simulations and experimental results of the probe's capability to sample small local concentrations of a marker within cell culture matrix, the MD unit's sample manipulation capabilities, and the ability to detect markers using in-line MD-nano-ESI MS.
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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.

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This thesis proposes a novel probabilistic method for measuring anatomical connectivity in the brain based on measurements obtained from diffusion magnetic resonance imaging. We approach the problem of fibre tractography from the viewpoint that a computational theory should relate to the underlying quantity that is being measured--the anisotropic diffusion of water molecules in fibrous tissues. To achieve this goal, the prior probability of completion between two particular regions of interest is modelled by a 3D directional random walk, which is representative of the ensemble anisotropic displacement to which diffusion-MRI measurements have been sensitized. The 3D directional random walk is controlled by a set of stochastic differential equations whose solution provides the probability of passing through every state in space given initial source and sink regions. Under such a model, particles tend to travel in a straight line, with a slight perturbation in their 3D orientation at each step governed by two consecutive Brownian motions in the spherical components of the orientation. The probability density functions describing the likelihood of passing through a particular position and orientation in 3D, given an initial source region and a final sink region, are respectively called a stochastic source field and a stochastic sink field. The final stochastic completion field is estimated by the product of these two densities and it represents the probability of passing through a particular state in space, while bridging the gap between the two regions. We show that the maximum likelihood curves obtained under the 3D directional random walk are curves of least energy which minimize a weighted sum of curvature squared, torsion squared and length. The 3D directional random walk and the associated completion fields are an extension of Williams and Jacobs' 2D completion model for curve completion in the plane. We then develop an efficient, local and parallelizable computational method to obtain the stochastic completion fields by exploiting the Fokker-Planck equation of the 3D directional random walk. This partial differential equation describes the evolution of the probability distribution of particles following such a random process. Additionally, a rotation invariant solution is proposed using a spherical harmonics basis to capture directions on the 2-sphere. In analogy with the 2D model of completion, we introduce additional diffusion terms to make spatial advection errors isotropic. The 3D stochastic completion field is further adapted to those problems where dense orientation data is present, as is the case for diffusion MRI measurements. The insertion of angular drift terms into the overall stochastic process provides a principled way to compute completions, while exploiting the local orientation information available at each voxel in the volume. Our algorithm provides a novel index of connectivity between two regions of interest, which is based on the overall probability for the computed completion curves between the two. We then discuss an alternative model of the directional random walk, where the 3D orientation change is drawn from a single distribution, i.e., a 3D Brownian distribution.The performance of the stochastic completion field algorithm is validated qualitatively and quantitatively on diffusion-MRI data from biological phantoms and on synthetic data. In vivo human data from 12 subjects is then used to investigate the algorithm's performance qualitatively by comparing the output of our method with published results based on another tractography method. Finally, we conclude by discussing the advantages and limitations of the method developed in this thesis and suggest directions for future work.<br>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.
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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.

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This thesis addresses a specific problem of model-based segmentation; namely, the automatic identification and delineation of gross anatomical structures of the human brain based on their appearance in magnetic resonance images (MRI). The approach developed in this thesis depends on a general, iterative, hierarchical registration procedure and a 3-D digital model of human brain anatomy that contains both volumetric intensity-based data and geometric atlas data that co-exist in a brain-based stereotaxic coordinate system. The model contains features derived from an MRI atlas of gross neuroanatomy, that is the result of an intensity average of 305 brains created with an automatic stereotaxic registration procedure developed here.<br>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.<br>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.
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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.

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Functional magnetic resonance imaging (fMRI) technology has been used in the investigation of human brain functionality and assist in brain disease diagnosis. While fMRI can be used to model both spatial and temporal brain functionality, the analysis of the fMRI images and the discovery of patterns for certain brain diseases is still a challenging task in medical imaging. Deep learning has been used more and more in medical field in an effort to further improve disease diagnosis due to its effectiveness in discovering high-level features in images. Convolutional neural networks (CNNs) is a class of deep learning algorithm that have been successfully used in medical imaging and extract spatial hierarchical features. The application of CNNs in fMRI and the extraction of brain functional patterns is an open field for research. This project focuses on how fMRIs can be used to improve Autism Spectrum Disorders (ASD) detection and diagnosis with 3D resting-state functional MRI (rs-fMRI) images. ASDs are a range of neurodevelopment brain diseases that mostly affect social function. Some of the symptoms include social and communicating difficulties, and also restricted  and repetitive  behaviors. The  symptoms appear on early childhood and tend to develop in time thus an early diagnosis is required. Finding a proper model for identifying between ASD and healthy subject is a challenging task and involves a lot of hyper-parameter tuning. In this project a grid search approach is followed in the quest of the optimal CNN architecture. Additionally, regularization and augmentation techniques are implemented in an effort to further improve the models performance.
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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.

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A fast method for gathering large-scale data sets through the serial sectioning of brain tissue is described. These data sets are retrieved using knife-edge scanning microscopy, a new technique developed in the Brain Networks Laboratory at Texas A&M University. This technique allows the imaging of tissue as it is cut by an ultramicrotome. In this thesis the development of a knife-edge scanner is discussed as well as the scanning techniques used to retrieve high-resolution data sets. Problems in knife-edge scanning microscopy, such as illumination, knife chatter, and focusing are discussed. Techniques are also shown to reduce these problems so that serial sections of tissue can be sampled at resolutions that are high enough to allow reconstruction of neurons at the cellular level.
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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.

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Nguyen, Peter. "CANNABINOID RECEPTORS IN THE 3D RECONSTRUCTED MOUSE BRAIN: FUNCTION AND REGULATION." VCU Scholars Compass, 2010. http://scholarscompass.vcu.edu/etd/2274.

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CB1 receptors (CB1R) mediate the psychoactive and therapeutic effects of cannabinoids including ∆9-tetrahydrocannabinol (THC), the main psychoactive constituent in marijuana. However, therapeutic use is limited by side effects and tolerance and dependence with chronic administration. Tolerance to cannabinoid-mediated effects is associated with CB1R adaptations, including desensitization (receptor-G-protein uncoupling) and downregulation (receptor degradation). The objectives of this thesis are to investigate the regional-specificity in CB1R function and regulation. Previous studies have investigated CB1Rs in a subset of regions involved in cannabinoid effects, but an inclusive regional comparison of the relative efficacies of different classes of cannabinoids to activate G-proteins has not been conducted. A novel unbiased whole-brain analysis was developed based on Statistical Parametric Mapping (SPM) for 3D-reconstructed mouse brain images derived from agonist-stimulated [35S]GTPgS autoradiography, which has not been described before. SPM demonstrated regional differences in the relative efficacies of cannabinoid agonists methanandamide (M-AEA), CP55,940 (CP), and WIN55,212-2 (WIN) in mouse brains. To assess potential contribution of novel sites, CB1R knockout (KO) mice were used. SPM analysis revealed that WIN, but not CP or M-AEA, stimulated [35S]GTPgS binding in regions that partially overlapped with the expression of CB1Rs. We then examined the role of the regulatory protein Beta-arrestin-2 (βarr2) in CB1R adaptations to chronic THC treatment. Deletion of βarr2 reduced CB1R desensitization/downregulation in the cerebellum, caudal periaqueductal gray (PAG), and spinal cord. However in hippocampus, amygdala and rostral PAG, similar desensitization was present in both genotypes. Interestingly, enhanced desensitization was found in the hypothalamus and cortex in βarr2 KO animals. Intra-regional differences in the magnitude of desensitization were noted in the caudal hippocampus, where βarr2 KO animals exhibited greater desensitization compared to WT. Regional differences in βarr2-mediated CB1R adaptation were associated with differential effects on tolerance, where THC-mediated antinociception, but not catalepsy or hypothermia, was attenuated in βarr2 KO mice. Overall, studies using SPM revealed intra- and inter-regional specificity in the function and regulation of CB1Rs and underscores an advantage of using a whole-brain unbiased approach. Understanding the regulation of CB1R signaling within different anatomical contexts represents an important fundamental prerequisite in the therapeutic exploitation of the cannabinoid system.
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Books on the topic "3D brain imaging"

1

Kretschmann, Hans-Joachim. Neurofunctional systems: 3D reconstructions with correlated neuroimaging. Thieme, 1998.

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1957-, Lucerna S., ed. In vivo atlas of deep brain structures: With 3D reconstructions. Springer, 2002.

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K, Costantini Jay, ed. 3D angiographic atlas of neurovascular anatomy and pathology. Cambridge University Press, 2007.

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Naidich, Thomas P. Duvernoy’s Atlas of the Human Brain Stem and Cerebellum: High-Field MRI: Surface Anatomy, Internal Structure, Vascularization and 3D Sectional Anatomy. Springer Vienna, 2009.

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Kumazawa-Manita, Noriko, Tsutomu Hashikawa, and Atsushi Iriki. The 3D Stereotaxic Brain Atlas of the Degu: With MRI and Histology Digital Model with a Freely Rotatable Viewer. Springer, 2018.

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Kumazawa-Manita, Noriko, Tsutomu Hashikawa, and Atsushi Iriki. The 3D Stereotaxic Brain Atlas of the Degu: With MRI and Histology Digital Model with a Freely Rotatable Viewer. Springer, 2019.

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Harder, B., T. Hagemann, Martin C. Hirsch, Thomas Kramer, and C. Zinecker. Neuroanatomy: 3D-Stereoscopic Atlas of the Human Brain. Springer London, Limited, 2012.

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Salpietro, F. M., S. Lucerna, C. Alafaci, and F. Tomasello. In Vivo Atlas of Deep Brain Structures: With 3D Reconstructions. Springer Berlin / Heidelberg, 2012.

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Salpietro, F. M., S. Lucerna, C. Alafaci, and F. Tomasello. In Vivo Atlas of Deep Brain Structures: With 3D Reconstructions. Springer London, Limited, 2012.

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Hirsch, Martin C., and Thomas Kramer. Neuroanatomy: 3D-Stereoscopic Atlas of the Human Brain (With CD-ROM). Springer, 1999.

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Book chapters on the topic "3D brain imaging"

1

Kikinis, Ron, Ferenc A. Jolesz, Guido Gerig, et al. "3D Morphometric and Morphologic Information Derived From Clinical Brain MR Images." In 3D Imaging in Medicine. Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-84211-5_28.

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Song, Zigen, Melinda Baxter, Mingwu Jin, et al. "Sparse Sampling and Fully-3D Fast Total Variation Based Imaging Reconstruction for Chemical Shift Imaging in Magnetic Resonance Spectroscopy." In Brain Informatics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05587-5_45.

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Herrmannsdörfer, Frank, Benjamin Flottmann, Siddarth Nanguneri, et al. "3D d STORM Imaging of Fixed Brain Tissue." In Methods in Molecular Biology. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-6688-2_13.

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Wu, Biao, Yutong Xie, Zeyu Zhang, et al. "BHSD: A 3D Multi-class Brain Hemorrhage Segmentation Dataset." In Machine Learning in Medical Imaging. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45673-2_15.

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Tudosiu, Petru-Daniel, Walter Hugo Lopez Pinaya, Mark S. Graham, et al. "Morphology-Preserving Autoregressive 3D Generative Modelling of the Brain." In Simulation and Synthesis in Medical Imaging. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16980-9_7.

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Petrov, Dmitry, Boris A. Gutman, Egor Kuznetsov, et al. "Deep Learning for Quality Control of Subcortical Brain 3D Shape Models." In Shape in Medical Imaging. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04747-4_25.

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Yaqub, Mohammad, Remi Cuingnet, Raffaele Napolitano, et al. "Volumetric Segmentation of Key Fetal Brain Structures in 3D Ultrasound." In Machine Learning in Medical Imaging. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02267-3_4.

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Fidon, Lucas, Michael Aertsen, Nada Mufti, et al. "Distributionally Robust Segmentation of Abnormal Fetal Brain 3D MRI." In Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87735-4_25.

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Fang, Longwei, Lichi Zhang, Dong Nie, et al. "Brain Image Labeling Using Multi-atlas Guided 3D Fully Convolutional Networks." In Patch-Based Techniques in Medical Imaging. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67434-6_2.

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Rusak, Filip, Rodrigo Santa Cruz, Pierrick Bourgeat, et al. "3D Brain MRI GAN-Based Synthesis Conditioned on Partial Volume Maps." In Simulation and Synthesis in Medical Imaging. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59520-3_2.

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Conference papers on the topic "3D brain imaging"

1

Saladi, S., P. Pinnamaneni, and J. Meyer. "Texture-based 3D brain imaging." In Proceedings 2nd Annual IEEE International Symposium on Bioinformatics and Bioengineering (BIBE 2001). IEEE, 2001. http://dx.doi.org/10.1109/bibe.2001.974422.

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Xiao, Sheng, Hua-an Tseng, Howard Gritton, Xue Han, and Jerome Mertz. "Video-rate Volumetric Neuronal Imaging Using 3D Targeted Illumination." In Optics and the Brain. OSA, 2018. http://dx.doi.org/10.1364/brain.2018.bw2c.6.

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Xue, Yujia, Ian G. Davison, David A. Boas, and Lei Tian. "Computational Miniature Mesoscope for Single-shot 3D Fluorescence Imaging." In Optics and the Brain. OSA, 2020. http://dx.doi.org/10.1364/brain.2020.btu2c.5.

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Loncaric, Sven, Ivan Ceskovic, Ratimir Petrovic, and Srecko Loncaric. "3D quantitative analysis of brain SPECT images." In Medical Imaging 2001, edited by Milan Sonka and Kenneth M. Hanson. SPIE, 2001. http://dx.doi.org/10.1117/12.431055.

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Rózsa, Balázs, Zoltán Szadai, Linda Judák, et al. "Imaging of dendrites and sparse interneuronal networks with 3D random access microscopy." In Optics and the Brain. Optica Publishing Group, 2023. http://dx.doi.org/10.1364/brain.2023.bw3b.6.

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Acousto-optical microscopy is a powerful tool to study spare networks and extensive dendritic arborization from the cortex of behaving animals. We used this novel approach for imaging dendrites and somata of sparse interneuron populations in a combination with auditory discrimination and detection tasks. Our results shed light of not yet known subcellular and network mechanisms from multiple brain regions.
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Szalay, Gergely, Zoltán Szadai, Linda Judák, et al. "Fast 3D imaging and photostimulation by 3D acousto-optical microscopy revealed spatiotemporally orchestrated clusters in the visual cortex." In Optics and the Brain. OSA, 2019. http://dx.doi.org/10.1364/brain.2019.bm3a.1.

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Li, Wenze, Venkatakaushik Voleti, Evan Schaffer, et al. "SCAPE Microscopy for High Speed, 3D Whole-Brain Imaging in Drosophila Melanogaster." In Optics and the Brain. OSA, 2016. http://dx.doi.org/10.1364/brain.2016.btu4d.3.

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Pan, Jinghong, Wieslaw L. Nowinski, Loe K. Fock, Douglas E. Dow, and Teh H. Chuan. "3D atlas of brain connections and functional circuits." In Medical Imaging 1997, edited by Yongmin Kim. SPIE, 1997. http://dx.doi.org/10.1117/12.273940.

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Leporé, Natasha, Yi-Yu Chou, Oscar L. Lopez, et al. "Fast 3D fluid registration of brain magnetic resonance images." In Medical Imaging, edited by Xiaoping P. Hu and Anne V. Clough. SPIE, 2008. http://dx.doi.org/10.1117/12.774338.

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Welsh, Tom F., Klaus D. Mueller, Wei Zhu, Jeffrey R. Meade, and Nora Volkow. "Brain miner: a 3D visual interface for the investigation of functional relationships in the brain." In Medical Imaging 2001, edited by Chin-Tu Chen and Anne V. Clough. SPIE, 2001. http://dx.doi.org/10.1117/12.428163.

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