Letteratura scientifica selezionata sul tema "3D brain imaging"
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Articoli di riviste sul tema "3D brain imaging"
Sumithra, M., P. Madhumitha, S. Madhumitha, D. Malini e B. Poorni Vinayaa. "3D Segmentation of Brain Tumor Imaging". International Journal of Advanced Engineering, Management and Science 6, n. 6 (2020): 256–60. http://dx.doi.org/10.22161/ijaems.66.5.
Testo completoKakeda, Shingo, Yukunori Korogi, Yasuhiro Hiai, Norihiro Ohnari, Toru Sato e Toshinori Hirai. "Pitfalls of 3D FLAIR Brain Imaging". Academic Radiology 19, n. 10 (ottobre 2012): 1225–32. http://dx.doi.org/10.1016/j.acra.2012.04.017.
Testo completoTaranda, Julian, e Sevin Turcan. "3D Whole-Brain Imaging Approaches to Study Brain Tumors". Cancers 13, n. 8 (15 aprile 2021): 1897. http://dx.doi.org/10.3390/cancers13081897.
Testo completoPooh, Ritsuko K. "Three-dimensional Evaluation of the Fetal Brain". Donald School Journal of Ultrasound in Obstetrics and Gynecology 11, n. 4 (2017): 268–75. http://dx.doi.org/10.5005/jp-journals-10009-1532.
Testo completoYao, Junjie. "Deep-brain imaging with 3D integrated photoacoustic tomography and ultrasound localization microscopy". Journal of the Acoustical Society of America 155, n. 3_Supplement (1 marzo 2024): A53. http://dx.doi.org/10.1121/10.0026774.
Testo completoAvasarala, Jagannadha, e Todd Pietila. "The first 3D printed multiple sclerosis brain: Towards a 3D era in medicine". F1000Research 6 (30 agosto 2017): 1603. http://dx.doi.org/10.12688/f1000research.12336.1.
Testo completoAvasarala, Jagannadha, e Todd Pietila. "The first 3D printed multiple sclerosis brain: Towards a 3D era in medicine". F1000Research 6 (20 settembre 2017): 1603. http://dx.doi.org/10.12688/f1000research.12336.2.
Testo completoRen, Jiahao, Xiaocen Wang, Chang Liu, He Sun, Junkai Tong, Min Lin, Jian Li et al. "3D Ultrasonic Brain Imaging with Deep Learning Based on Fully Convolutional Networks". Sensors 23, n. 19 (9 ottobre 2023): 8341. http://dx.doi.org/10.3390/s23198341.
Testo completode Crespigny, Alex, Hani Bou-Reslan, Merry C. Nishimura, Heidi Phillips, Richard A. D. Carano e Helen E. D’Arceuil. "3D micro-CT imaging of the postmortem brain". Journal of Neuroscience Methods 171, n. 2 (giugno 2008): 207–13. http://dx.doi.org/10.1016/j.jneumeth.2008.03.006.
Testo completoMiao, Peng, Zhixia Wu, Miao Li, Yuanyuan Ji, Bohua Xie, Xiaojie Lin e Guo-Yuan Yang. "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.
Testo completoTesi sul tema "3D brain imaging"
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/.
Testo completoLaw, Kwok-wai Albert, e 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.
Testo completoUthama, 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.
Testo completoOlivero, 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.
Testo completoMomayyezSiahkal, 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.
Testo completoCette 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.
Testo completoThe 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.
Testo completoMayerich, 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.
Testo completoHeinzer, 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.
Testo completoNguyen, Peter. "CANNABINOID RECEPTORS IN THE 3D RECONSTRUCTED MOUSE BRAIN: FUNCTION AND REGULATION". VCU Scholars Compass, 2010. http://scholarscompass.vcu.edu/etd/2274.
Testo completoLibri sul tema "3D brain imaging"
Kretschmann, Hans-Joachim. Neurofunctional systems: 3D reconstructions with correlated neuroimaging. Stuttgart: Thieme, 1998.
Cerca il testo completo1957-, Lucerna S., a cura di. In vivo atlas of deep brain structures: With 3D reconstructions. Berlin: Springer, 2002.
Cerca il testo completoK, Costantini Jay, a cura di. 3D angiographic atlas of neurovascular anatomy and pathology. Cambridge: Cambridge University Press, 2007.
Cerca il testo completoNaidich, Thomas P. Duvernoy’s Atlas of the Human Brain Stem and Cerebellum: High-Field MRI: Surface Anatomy, Internal Structure, Vascularization and 3D Sectional Anatomy. Vienna: Springer Vienna, 2009.
Cerca il testo completoKumazawa-Manita, Noriko, Tsutomu Hashikawa e Atsushi Iriki. The 3D Stereotaxic Brain Atlas of the Degu: With MRI and Histology Digital Model with a Freely Rotatable Viewer. Springer, 2018.
Cerca il testo completoKumazawa-Manita, Noriko, Tsutomu Hashikawa e Atsushi Iriki. The 3D Stereotaxic Brain Atlas of the Degu: With MRI and Histology Digital Model with a Freely Rotatable Viewer. Springer, 2019.
Cerca il testo completoHarder, B., T. Hagemann, Martin C. Hirsch, Thomas Kramer e C. Zinecker. Neuroanatomy: 3D-Stereoscopic Atlas of the Human Brain. Springer London, Limited, 2012.
Cerca il testo completoSalpietro, F. M., S. Lucerna, C. Alafaci e F. Tomasello. In Vivo Atlas of Deep Brain Structures: With 3D Reconstructions. Springer Berlin / Heidelberg, 2012.
Cerca il testo completoSalpietro, F. M., S. Lucerna, C. Alafaci e F. Tomasello. In Vivo Atlas of Deep Brain Structures: With 3D Reconstructions. Springer London, Limited, 2012.
Cerca il testo completoHirsch, Martin C., e Thomas Kramer. Neuroanatomy: 3D-Stereoscopic Atlas of the Human Brain (With CD-ROM). Springer, 1999.
Cerca il testo completoCapitoli di libri sul tema "3D brain imaging"
Kikinis, Ron, Ferenc A. Jolesz, Guido Gerig, Tamas Sandor, Harvey E. Cline, William E. Lorensen, Michael Halle e Stephen A. Benton. "3D Morphometric and Morphologic Information Derived From Clinical Brain MR Images". In 3D Imaging in Medicine, 441–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-84211-5_28.
Testo completoSong, Zigen, Melinda Baxter, Mingwu Jin, Jian-Xiong Wang, Ren-Cang Li, Talon Johnson e Jianzhong Su. "Sparse Sampling and Fully-3D Fast Total Variation Based Imaging Reconstruction for Chemical Shift Imaging in Magnetic Resonance Spectroscopy". In Brain Informatics, 479–85. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05587-5_45.
Testo completoHerrmannsdörfer, Frank, Benjamin Flottmann, Siddarth Nanguneri, Varun Venkataramani, Heinz Horstmann, Thomas Kuner e Mike Heilemann. "3D d STORM Imaging of Fixed Brain Tissue". In Methods in Molecular Biology, 169–84. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-6688-2_13.
Testo completoWu, Biao, Yutong Xie, Zeyu Zhang, Jinchao Ge, Kaspar Yaxley, Suzan Bahadir, Qi Wu, Yifan Liu e Minh-Son To. "BHSD: A 3D Multi-class Brain Hemorrhage Segmentation Dataset". In Machine Learning in Medical Imaging, 147–56. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45673-2_15.
Testo completoTudosiu, Petru-Daniel, Walter Hugo Lopez Pinaya, Mark S. Graham, Pedro Borges, Virginia Fernandez, Dai Yang, Jeremy Appleyard et al. "Morphology-Preserving Autoregressive 3D Generative Modelling of the Brain". In Simulation and Synthesis in Medical Imaging, 66–78. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16980-9_7.
Testo completoPetrov, Dmitry, Boris A. Gutman, Egor Kuznetsov, Christopher R. K. Ching, Kathryn Alpert, Artemis Zavaliangos-Petropulu, Dmitry Isaev et al. "Deep Learning for Quality Control of Subcortical Brain 3D Shape Models". In Shape in Medical Imaging, 268–76. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04747-4_25.
Testo completoYaqub, Mohammad, Remi Cuingnet, Raffaele Napolitano, David Roundhill, Aris Papageorghiou, Roberto Ardon e J. Alison Noble. "Volumetric Segmentation of Key Fetal Brain Structures in 3D Ultrasound". In Machine Learning in Medical Imaging, 25–32. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02267-3_4.
Testo completoFidon, Lucas, Michael Aertsen, Nada Mufti, Thomas Deprest, Doaa Emam, Frédéric Guffens, Ernst Schwartz 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, 263–73. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87735-4_25.
Testo completoFang, Longwei, Lichi Zhang, Dong Nie, Xiaohuan Cao, Khosro Bahrami, Huiguang He e Dinggang Shen. "Brain Image Labeling Using Multi-atlas Guided 3D Fully Convolutional Networks". In Patch-Based Techniques in Medical Imaging, 12–19. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67434-6_2.
Testo completoRusak, Filip, Rodrigo Santa Cruz, Pierrick Bourgeat, Clinton Fookes, Jurgen Fripp, Andrew Bradley e Olivier Salvado. "3D Brain MRI GAN-Based Synthesis Conditioned on Partial Volume Maps". In Simulation and Synthesis in Medical Imaging, 11–20. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59520-3_2.
Testo completoAtti di convegni sul tema "3D brain imaging"
Saladi, S., P. Pinnamaneni e 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.
Testo completoXiao, Sheng, Hua-an Tseng, Howard Gritton, Xue Han e Jerome Mertz. "Video-rate Volumetric Neuronal Imaging Using 3D Targeted Illumination". In Optics and the Brain. Washington, D.C.: OSA, 2018. http://dx.doi.org/10.1364/brain.2018.bw2c.6.
Testo completoXue, Yujia, Ian G. Davison, David A. Boas e Lei Tian. "Computational Miniature Mesoscope for Single-shot 3D Fluorescence Imaging". In Optics and the Brain. Washington, D.C.: OSA, 2020. http://dx.doi.org/10.1364/brain.2020.btu2c.5.
Testo completoLoncaric, Sven, Ivan Ceskovic, Ratimir Petrovic e Srecko Loncaric. "3D quantitative analysis of brain SPECT images". In Medical Imaging 2001, a cura di Milan Sonka e Kenneth M. Hanson. SPIE, 2001. http://dx.doi.org/10.1117/12.431055.
Testo completoRózsa, Balázs, Zoltán Szadai, Linda Judák, Balázs Chiovini, Gábor Juhász, Katalin Ócsai, Dénes Pálfi et al. "Imaging of dendrites and sparse interneuronal networks with 3D random access microscopy". In Optics and the Brain. Washington, D.C.: Optica Publishing Group, 2023. http://dx.doi.org/10.1364/brain.2023.bw3b.6.
Testo completoSzalay, Gergely, Zoltán Szadai, Linda Judák, Pál Maák, Katalin Ócsai, Máté Veress, Tamás Tompa, Balázs Chiovini, Gergely Katona e Balázs Rózsa. "Fast 3D imaging and photostimulation by 3D acousto-optical microscopy revealed spatiotemporally orchestrated clusters in the visual cortex". In Optics and the Brain. Washington, D.C.: OSA, 2019. http://dx.doi.org/10.1364/brain.2019.bm3a.1.
Testo completoLi, Wenze, Venkatakaushik Voleti, Evan Schaffer, Rebecca Vaadia, Wesley B. Grueber, Richard S. Mann e Elizabeth Hillman. "SCAPE Microscopy for High Speed, 3D Whole-Brain Imaging in Drosophila Melanogaster". In Optics and the Brain. Washington, D.C.: OSA, 2016. http://dx.doi.org/10.1364/brain.2016.btu4d.3.
Testo completoPan, Jinghong, Wieslaw L. Nowinski, Loe K. Fock, Douglas E. Dow e Teh H. Chuan. "3D atlas of brain connections and functional circuits". In Medical Imaging 1997, a cura di Yongmin Kim. SPIE, 1997. http://dx.doi.org/10.1117/12.273940.
Testo completoLeporé, Natasha, Yi-Yu Chou, Oscar L. Lopez, Howard J. Aizenstein, James T. Becker, Arthur W. Toga e Paul M. Thompson. "Fast 3D fluid registration of brain magnetic resonance images". In Medical Imaging, a cura di Xiaoping P. Hu e Anne V. Clough. SPIE, 2008. http://dx.doi.org/10.1117/12.774338.
Testo completoWelsh, Tom F., Klaus D. Mueller, Wei Zhu, Jeffrey R. Meade e Nora Volkow. "Brain miner: a 3D visual interface for the investigation of functional relationships in the brain". In Medical Imaging 2001, a cura di Chin-Tu Chen e Anne V. Clough. SPIE, 2001. http://dx.doi.org/10.1117/12.428163.
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