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Relevant bibliographies by topics / Imaging in a neural tissue

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Academic literature on the topic 'Imaging in a neural tissue'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "Imaging in a neural tissue"

1

KUHAR, M. "Imaging receptors for drugs in neural tissue." Neuropharmacology 26, no. 7 (July 1987): 911–16. http://dx.doi.org/10.1016/0028-3908(87)90069-4.

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Yadav, Rajiv, Sushmita Mukherjee, Frederick R. Maxfield, Jay K. Jhaveri, Sandhya Rao, Robert A. Leung, E. Darracott Vaughan, Watt W. Webb, and Ashutosh K. Tewari. "IMAGING OF PERIPROSTATIC NEURAL TISSUE WITH MULTIPHOTON MICROSCOPY." Journal of Urology 179, no. 4S (April 2008): 275–76. http://dx.doi.org/10.1016/s0022-5347(08)60801-0.

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Wu, Ed X., and Matthew M. Cheung. "MR diffusion kurtosis imaging for neural tissue characterization." NMR in Biomedicine 23, no. 7 (July 9, 2010): 836–48. http://dx.doi.org/10.1002/nbm.1506.

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Niesner, Raluca, Volker Siffrin, and Frauke Zipp. "Two-Photon Imaging of Immune Cells in Neural Tissue." Cold Spring Harbor Protocols 2013, no. 3 (March 2013): pdb.prot073528. http://dx.doi.org/10.1101/pdb.prot073528.

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Xuan, Jianhua, Uwe Klimach, Hongzhi Zhao, Qiushui Chen, Yingyin Zou, and Yue Wang. "Improved Diagnostics Using Polarization Imaging and Artificial Neural Networks." International Journal of Biomedical Imaging 2007 (2007): 1–11. http://dx.doi.org/10.1155/2007/74143.

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In recent years, there has been an increasing interest in studying the propagation of polarized light in biological cells and tissues. This paper presents a novel approach to cell or tissue imaging using a full Stokes imaging system with advanced polarization image analysis algorithms for improved diagnostics. The key component of the Stokes imaging system is the electrically tunable retarder, enabling high-speed operation of the system to acquire four intensity images sequentially. From the acquired intensity images, four Stokes vector images can be computed to obtain complete polarization information. Polarization image analysis algorithms are then developed to analyze Stokes polarization images for cell or tissue classification. Specifically, wavelet transforms are first applied to the Stokes components for initial feature analysis and extraction. Artificial neural networks (ANNs) are then used to extract diagnostic features for improved classification and prediction. In this study, phantom experiments have been conducted using a prototyped Stokes polarization imaging device. In particular, several types of phantoms, consisting of polystyrene latex spheres in various diameters, were prepared to simulate different conditions of epidermal layer of skin. The experimental results from phantom studies and a plant cell study show that the classification performance using Stokes images is significantly improved over that using the intensity image only.

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Jing, D., Y. Yi, W. Luo, S. Zhang, Q. Yuan, J. Wang, E. Lachika, Z. Zhao, and H. Zhao. "Tissue Clearing and Its Application to Bone and Dental Tissues." Journal of Dental Research 98, no. 6 (April 22, 2019): 621–31. http://dx.doi.org/10.1177/0022034519844510.

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Opaqueness of animal tissue can be attributed mostly to light absorption and light scattering. In most noncleared tissue samples, confocal images can be acquired at no more than a 100-µm depth. Tissue-clearing techniques have emerged in recent years in the neuroscience field. Many tissue-clearing methods have been developed, and they all follow similar working principles. During the tissue-clearing process, chemical or physical treatments are applied to remove components blocking or scattering the light. Finally, samples are immersed in a designated clearing medium to achieve a uniform refractive index and to gain transparency. Once the transparency is reached, images can be acquired even at several millimeters of depth with high resolution. Tissue clearing has become an essential tool for neuroscientists to investigate the neural connectome or to analyze spatial information of various types of brain cells. Other than neural science research, tissue-clearing techniques also have applications for bone research. Several methods have been developed for clearing bones. Clearing treatment enables 3-dimensional imaging of bones without sectioning and provides important new insights that are difficult or impossible to acquire with conventional approaches. Application of tissue-clearing technique on dental research remains limited. This review will provide an overview of the recent literature related to the methods and application of various tissue-clearing methods. The following aspects will be covered: general principles for the tissue-clearing technique, current available methods for clearing bones and teeth, general principles of 3-dimensional imaging acquisition and data processing, applications of tissue clearing on studying biological processes within bones and teeth, and future directions for 3-dimensional imaging.

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Roth, Bradley J., and Peter J. Basser. "Mechanical model of neural tissue displacement during Lorentz effect imaging." Magnetic Resonance in Medicine 61, no. 1 (December 18, 2008): 59–64. http://dx.doi.org/10.1002/mrm.21772.

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Klontzas, Michail E., and Alexandros Protonotarios. "High-Resolution Imaging for the Analysis and Reconstruction of 3D Microenvironments for Regenerative Medicine: An Application-Focused Review." Bioengineering 8, no. 11 (November 10, 2021): 182. http://dx.doi.org/10.3390/bioengineering8110182.

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The rapid evolution of regenerative medicine and its associated scientific fields, such as tissue engineering, has provided great promise for multiple applications where replacement and regeneration of damaged or lost tissue is required. In order to evaluate and optimise the tissue engineering techniques, visualisation of the material of interest is crucial. This includes monitoring of the cellular behaviour, extracellular matrix composition, scaffold structure, and other crucial elements of biomaterials. Non-invasive visualisation of artificial tissues is important at all stages of development and clinical translation. A variety of preclinical and clinical imaging methods—including confocal multiphoton microscopy, optical coherence tomography, magnetic resonance imaging (MRI), and computed tomography (CT)—have been used for the evaluation of artificial tissues. This review attempts to present the imaging methods available to assess the composition and quality of 3D microenvironments, as well as their integration with human tissues once implanted in the human body. The review provides tissue-specific application examples to demonstrate the applicability of such methods on cardiovascular, musculoskeletal, and neural tissue engineering.

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Zhang, Lechao, Yao Zhou, Danfei Huang, Libin Zhu, Xiaoqing Chen, Zhonghao Xie, Guihua Cui, Guangzao Huang, Shujat Ali, and Xiaojing Chen. "Hyperspectral Imaging Combined with Deep Learning to Detect Ischemic Necrosis in Small Intestinal Tissue." Photonics 10, no. 7 (June 21, 2023): 708. http://dx.doi.org/10.3390/photonics10070708.

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Obtaining adequate resection margins in small intestinal necrotic tissue remains challenging due to the lack of intraoperative feedback. Here, we used hyperspectral imaging (HSI), an imaging technique for objective identification, combined with deep learning methods for automated small intestine tissue classification. As part of a prospective experimental study, we recorded hyperspectral datasets of small intestine biopsies from seven white rabbits. Based on the differences in the spectral characteristics of normal and ischemic necrotic small intestinal tissues in the wavelength range of 400–1000 nm, we applied deep learning techniques to objectively distinguish between these two types of tissues. The results showed that three-dimensional convolutional neural networks were more effective in extracting both spectral and spatial features of small intestine tissue hyperspectral data for classification. The combination of a deep learning model and HSI provided a new idea for the objective identification of ischemic necrotic tissue in the small intestine.

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Sheejakumari, V., and B. Sankara Gomathi. "MRI Brain Images Healthy and Pathological Tissues Classification with the Aid of Improved Particle Swarm Optimization and Neural Network." Computational and Mathematical Methods in Medicine 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/807826.

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The advantages of magnetic resonance imaging (MRI) over other diagnostic imaging modalities are its higher spatial resolution and its better discrimination of soft tissue. In the previous tissues classification method, the healthy and pathological tissues are classified from the MRI brain images using HGANN. But the method lacks sensitivity and accuracy measures. The classification method is inadequate in its performance in terms of these two parameters. So, to avoid these drawbacks, a new classification method is proposed in this paper. Here, new tissues classification method is proposed with improved particle swarm optimization (IPSO) technique to classify the healthy and pathological tissues from the given MRI images. Our proposed classification method includes the same four stages, namely, tissue segmentation, feature extraction, heuristic feature selection, and tissue classification. The method is implemented and the results are analyzed in terms of various statistical performance measures. The results show the effectiveness of the proposed classification method in classifying the tissues and the achieved improvement in sensitivity and accuracy measures. Furthermore, the performance of the proposed technique is evaluated by comparing it with the other segmentation methods.

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Dissertations / Theses on the topic "Imaging in a neural tissue"

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Hui, Sai-kam, and 許世鑫. "Magnetic resonance diffusion tensor imaging for neural tissue characterization." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B42841306.

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Hui, Sai-kam. "Magnetic resonance diffusion tensor imaging for neural tissue characterization." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B42841306.

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Eppelheimer, Maggie S. "Identification of Chiari Malformation Type I Brain Morphology and Biomechanics: A Multi-Faceted Approach to Determine Diagnostic and Treatment Criteria." University of Akron / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=akron1595680107882868.

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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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Abdeladim, Lamiae. "Large volume multicolor nonlinear microscopy of neural tissues." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLX070/document.

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La microscopie non linéaire a transformé le domaine de la neurobiologie depuis les années 1990, en permettant d'acquérir des images tridimensionnelles de tissus épais avec une résolution subcellulaire. Cependant, les profondeurs d'imagerie accessibles sont limitées à quelques centaines de micromètres dans des tissus diffusants tels que le tissu cérébral. Au cours des dernières années, plusieurs stratégies ont été développées pour dépasser cette limitation de profondeur et accéder à de plus grands volumes de tissu. Ces avancées récentes ont jusqu'à présent été limitées en terme de modes de contrastes accessibles, et ont souvent été réduites à des approches monochromes. Ce travail de thèse vise à développer des techniques d'imagerie non linéaires de grands volumes et de grande profondeur dotées de diverses possibilités de contrastes, indispensables pour l'étude de tissus complexes tels que le tissu cérébral. Dans un premier chapitre, nous présentons les difficultés associées à l'imagerie de grand volume de tissu cérébral, avec une emphase particulière sur les puissantes stratégies de marquages génétiques dont l'usage à jusqu'à présent été limité à des faibles étendues. Ensuite, nous introduisons la microscopie Chrom-SMP (chromatic serial multiphoton), une méthode développée au cours de cette thèse et consistant à combiner l’excitation deux-photon multicouleurs par mélange de fréquences avec une technique d'histologie automatisée (i.e découpe sériée) pour accéder à plusieurs contrastes non linéaires à travers de grands volumes de tissus ex vivo, allant de plusieurs mm3 à des cerveaux entiers, avec une résolution micrométrique et un coalignement intrinsèque des canaux spectraux. Dans un troisième chapitre, nous explorons le potentiel de cette nouvelle approche pour la neurobiologie. En particulier, nous démontrons l'histologie multicouleur de plusieurs mm3 de tissu "Brainbow" avec une résolution constante dans l’ensemble du volume imagé. Nous illustrons le potentiel de notre approche à travers l'analyse de la morphologie, des interactions et du lignage des astrocytes du cortex cérébral de souris. Nous explorons également l’apport du Chrom-SMP pour le suivi multiplexé de projections neuronales marquées par des traceurs de couleurs distinctes sur de grandes distances. Enfin, nous présentons dans un quatrième chapitre le développement de la microscopie à trois photons multimodale, approche permettant d’augmenter la profondeur d’imagerie sur tissus vivants
Multiphoton microscopy has transformed neurobiology since the 1990s by enabling 3D imaging of thick tissues at subcellular resolution. However the depths provided by multiphoton microscopy are limited to a few hundreds of micrometers inside scattering tissues such as the brain. In the recent years, several strategies have emerged to overcome this depth limitation and to access larger volumes of tissue. Although these novel approaches are transforming brain imaging, they currently lack efficient multicolor and multicontrast modalities. This work aims at developing large-scale and deep-tissue multiphoton imaging modalities with augmented contrast capabilities. In a first chapter, we present the challenges of high-content large-volume brain imaging, with a particular emphasis on powerful multicolor labeling strategies which have so far been restricted to limited scales. We then introduce chromatic serial multiphoton (Chrom-SMP) microscopy, a method which combines automated histology with multicolor two-photon excitation through wavelength-mixing to access multiple nonlinear contrasts across large volumes, from several mm3 to whole brains, with submicron resolution and intrinsic channel registration. In a third chapter, we explore the potential of this novel approach to open novel experimental paradigms in neurobiological studies. In particular, we demonstrate multicolor volumetric histology of several mm3 of Brainbow-labeled tissues with preserved diffraction-limited resolution and illustrate the strengths of this method through color-based tridimensional analysis of astrocyte morphology, interactions and lineage in the mouse cerebral cortex. We further illustrate the potential of the method through multiplexed whole-brain mapping of axonal projections labeled with distinct tracers. Finally, we develop multimodal three-photon microscopy as a method to access larger depths in live settings

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Channappa, Lakshmi [Verfasser], and Thomas [Akademischer Betreuer] Euler. "Electrical Imaging of Aberrant Activity in Neural Tissues Using High Density Microelectrode Arrays / Lakshmi Channappa ; Betreuer: Thomas Euler." Tübingen : Universitätsbibliothek Tübingen, 2016. http://d-nb.info/1199615544/34.

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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.

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Development of a three dimensional (3D) HYBROT model with targeted in vivo like intact cellular circuitry in thick brain slices for multi-site stimulation and recording will provide a useful in vitro model to study neuronal dynamics at network level. In order to make this in vitro model feasible, we need to develop several associated technologies. These technologies include development of a thick organotypic brain slice culturing method, a three dimensional (3D) micro-fluidic multielectrode Neural Interface system (ÂµNIS) and the associated electronic interfaces for stimulation and recording of/from tissue, development of targeted stimulation patterns for closed-loop interaction with a robotic body, and a deep-tissue non-invasive imaging system. To make progress towards this goal, I undertook two projects: (i) to develop a method to culture thick organotypic brain slices, and (ii) construct a multiphoton imaging system that allows long-term and deep-tissue imaging of two dimensional and three dimensional cultures. Organotypic brain slices preserve cytoarchitecture of the brain. Therefore, they make more a realistic reduced model for various network level investigations. However, current culturing methods are not successful for culturing thick brain slices due to limited supply of nutrients and oxygen to inner layers of the culture. We developed a forced-convection based perfusion method to culture viable 700Âµm thick brain slices. Multiphoton microscopy is ideal for imaging living 2D or 3D cultures at submicron resolution. We successfully fabricated a custom-designed high efficiency multiphoton microscope that has the desired flexibility to perform experiments using multiple technologies simultaneously. This microscope was used successfully for 3D and time-lapse imaging. Together these projects have contributed towards the progress of development of a 3D HYBROT. ----- 3D Hybrot: A hybrid system of a brain slice culture embodied with a robotic body.

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Wellen, Jeremy W. "Characterization of soft-tissue response to mechanical loading using nuclear magnetic resonance (NMR) and functional magnetic resonance imaging (fMRI) of neuronal activity during sustained cognitive-stimulus paradigms." Link to electronic thesis, 2003. http://www.wpi.edu/Pubs/ETD/Available/etd-0430103-140128.

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Bernal, Moyano Jose. "Deep learning for atrophy quantification in brain magnetic resonance imaging." Doctoral thesis, Universitat de Girona, 2020. http://hdl.handle.net/10803/671699.

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The quantification of cerebral atrophy is fundamental in neuroinformatics since it permits diagnosing brain diseases, assessing their progression, and determining the effectiveness of novel treatments to counteract them. However, this is still an open and challenging problem since the performance 2/2 of traditional methods depends on imaging protocols and quality, data harmonisation errors, and brain abnormalities. In this doctoral thesis, we question whether deep learning methods can be used for better estimating cerebral atrophy from magnetic resonance images. Our work shows that deep learning can lead to a state-of-the-art performance in cross-sectional assessments and compete and surpass traditional longitudinal atrophy quantification methods. We believe that the proposed cross-sectional and longitudinal methods can be beneficial for the research and clinical community
La cuantificación de la atrofia cerebral es fundamental en la neuroinformática ya que permite diagnosticar enfermedades cerebrales, evaluar su progresión y determinar la eficacia de los nuevos tratamientos para contrarrestarlas. Sin embargo, éste sigue siendo un problema abierto y difícil, ya que el rendimiento de los métodos tradicionales depende de los protocolos y la calidad de las imágenes, los errores de armonización de los datos y las anomalías del cerebro. En esta tesis doctoral, cuestionamos si los métodos de aprendizaje profundo pueden ser utilizados para estimar mejor la atrofia cerebral a partir de imágenes de resonancia magnética. Nuestro trabajo muestra que el aprendizaje profundo puede conducir a un rendimiento de vanguardia en las evaluaciones transversales y competir y superar los métodos tradicionales de cuantificación de la atrofia longitudinal. Creemos que los métodos transversales y longitudinales propuestos pueden ser beneficiosos para la comunidad investigadora y clínica

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Killich, Markus. "Tissue Doppler Imaging." Diss., lmu, 2007. http://nbn-resolving.de/urn:nbn:de:bvb:19-67089.

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Books on the topic "Imaging in a neural tissue"

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Brandt, Roland, and Lidia Bakota. Laser scanning microscopy and quantitative image analysis of neuronal tissue. New York: Humana Press, 2014.

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Bilston, Lynne E., ed. Neural Tissue Biomechanics. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-13890-4.

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Bilston, Lynne E. Neural Tissue Biomechanics. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2011.

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Maeda, Nobuaki. Neural proteoglycans, 2007. Trivandrum, India: Research Signpost, 2007.

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D, Murphey Mark, ed. Imaging of soft tissue tumors. 2nd ed. Philadelphia, PA: Lippincott Williams & Wilkins, 2006.

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Vanhoenacker, Filip M., Paul M. Parizel, and Jan L. Gielen, eds. Imaging of Soft Tissue Tumors. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-46679-8.

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Kang, Heung Sik, Sung Hwan Hong, Ja-Young Choi, and Hye Jin Yoo. Oncologic Imaging: Soft Tissue Tumors. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-287-718-5.

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De Schepper, Arthur M., Paul M. Parizel, Luc De Beuckeleer, and Filip Vanhoenacker, eds. Imaging of Soft Tissue Tumors. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-07856-3.

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De Schepper, Arthur M., Paul M. Parizel, Frank Ramon, Luc De Beuckeleer, and Jan E. Vandevenne, eds. Imaging of Soft Tissue Tumors. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-662-07859-4.

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Higer, H. Peter, and Gernot Bielke, eds. Tissue Characterization in MR Imaging. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-74993-3.

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Book chapters on the topic "Imaging in a neural tissue"

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Huisman, H. J., and J. M. Thijssen. "Application of Artificial Neural Networks in Ultrasonic Tissue Characterization." In Acoustical Imaging, 355–58. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4419-8772-3_57.

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Vu, Tania Q., and Sujata Sundara Rajan. "Quantum Dot Imaging of Neural Cells and Tissue." In Nanotechnology for Biology and Medicine, 151–68. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-0-387-31296-5_7.

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Li, Baowang, and Ralph D. Freeman. "Noninvasive Neural Imaging and Tissue Oxygenation in the Visual System." In Neurovascular Coupling Methods, 97–122. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0724-3_6.

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Rector, D. M., G. R. Poe, and R. M. Harper. "Fiber Optic Imaging of Subcortical Neural Tissue in Freely Behaving Animals." In Advances in Experimental Medicine and Biology, 81–86. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4899-2468-1_9.

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Zhang, Fan, Junlin Yang, Nariman Nezami, Fabian Laage-gaupp, Julius Chapiro, Ming De Lin, and James Duncan. "Liver Tissue Classification Using an Auto-context-based Deep Neural Network with a Multi-phase Training Framework." In Patch-Based Techniques in Medical Imaging, 59–66. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00500-9_7.

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Alzyadat, Tariq, Stephan Praet, Girija Chetty, Roland Goecke, David Hughes, Dinesh Kumar, Marijke Welvaert, Nicole Vlahovich, and Gordon Waddington. "Automatic Segmentation of Achilles Tendon Tissues Using Deep Convolutional Neural Network." In Machine Learning in Medical Imaging, 444–54. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59861-7_45.

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Li, Dongyu, Yanjie Zhao, Chao Zhang, and Dan Zhu. "In vivo skull optical clearing for imaging cortical neuron and vascular structure and function." In Handbook of Tissue Optical Clearing, 351–68. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003025252-21.

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Negahdar, Mohammadreza. "Automatic Grading of Emphysema by Combining 3D Lung Tissue Appearance and Deformation Map Using a Two-Stream Fully Convolutional Neural Network." In Machine Learning in Medical Imaging, 181–90. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-21014-3_19.

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Isogai, Yoh, Douglas S. Richardson, Catherine Dulac, and Joseph Bergan. "Optimized Protocol for Imaging Cleared Neural Tissues Using Light Microscopy." In Methods in Molecular Biology, 137–53. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-6688-2_11.

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Pati, Pushpak, Guillaume Jaume, Lauren Alisha Fernandes, Antonio Foncubierta-Rodríguez, Florinda Feroce, Anna Maria Anniciello, Giosue Scognamiglio, et al. "HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classification." In Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis, 208–19. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60365-6_20.

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Conference papers on the topic "Imaging in a neural tissue"

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Jaswal, Rajeshwer S., Mohammad A. Yaseen, Buyin Fu, David A. Boas, and Sava Sakadžic. "High-spatial-resolution mapping of the oxygen concentration in cortical tissue (Conference Presentation)." In Neural Imaging and Sensing, edited by E. Duco Jansen and Qingming Luo. SPIE, 2016. http://dx.doi.org/10.1117/12.2212716.

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Blodgett, David W., Carissa Rodriguez, Austen Lefebvre, Grace Hwang, Marek Mirski, Eyal Bar-Kochba, Aaron Criss, et al. "Brain imaging for neural tissue health assessment." In Micro- and Nanotechnology Sensors, Systems, and Applications X, edited by M. Saif Islam, Achyut K. Dutta, and Thomas George. SPIE, 2018. http://dx.doi.org/10.1117/12.2305789.

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Ornelas, Danielle, Md Hasan, Oscar Gonzalez, Giri Krishnan, Jenny I. Szu, Timothy Myers, Koji Hirota, Maxim Bazhenov, Devin K. Binder, and Boris H. Park. "Optical changes in cortical tissue during seizure activity using optical coherence tomography (Conference Presentation)." In Neural Imaging and Sensing, edited by Qingming Luo and Jun Ding. SPIE, 2017. http://dx.doi.org/10.1117/12.2253415.

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Kong, Zhenglun, Ting Li, Junyi Luo, and Shengpu Xu. "Automatic tissue image segmentation based on image processing and deep learning." In Neural Imaging and Sensing 2018, edited by Qingming Luo and Jun Ding. SPIE, 2018. http://dx.doi.org/10.1117/12.2293481.

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Zhong, Qiuyuan, Chen-Yuan Dong, Xinlei Fu, Xiayi Xu, and Shih-Chi Chen. "Fast drug screening platform for cancer treatment based on live tissue culturing and high-speed 3D imaging." In Neural Imaging and Sensing 2022, edited by Qingming Luo, Jun Ding, and Ling Fu. SPIE, 2022. http://dx.doi.org/10.1117/12.2609938.

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Salas, Matthias, Johanna Gesperger, Antonia Lichtenegger, Michael Niederleithner, Laurin Ginner, Adelheid Woehrer, Bernhard Baumann, Tilman Schmoll, Wolfgang Drexler, and Rainer A. Leitgeb. "Multi-scale investigation of Alzheimer’s disease brain tissue using 1060 nm swept source optical coherence tomography (Conference Presentation)." In Neural Imaging and Sensing 2020, edited by Qingming Luo, Jun Ding, and Ling Fu. SPIE, 2020. http://dx.doi.org/10.1117/12.2544765.

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Zhu, Jun, Hercules Freitas, Izumi Maezawa, Lee-Way Jin, and Vivek J. Srinivasan. "1700 nm optical coherence microscopy enables minimally invasive, volumetric, deep tissue optical biopsy of the mouse brain in vivo." In Neural Imaging and Sensing 2021, edited by Qingming Luo, Jun Ding, and Ling Fu. SPIE, 2021. http://dx.doi.org/10.1117/12.2577001.

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Turovets, S. I., and D. M. Tucker. "NIR Imaging of Labeled Human Neural Tissue: Computational Feasibility Studies." In Biomedical Optics. Washington, D.C.: OSA, 2008. http://dx.doi.org/10.1364/biomed.2008.bmd18.

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Buhmann, Julia M., Stephan Gerhard, Matthew Cook, and Jan Funke. "Tracking of microtubules in anisotropic volumes of neural tissue." In 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI 2016). IEEE, 2016. http://dx.doi.org/10.1109/isbi.2016.7493275.

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Strenge, Paul, Birgit Lange, Wolfgang Draxinger, Christian Hagel, Christin Grill, Veit Danicke, Dirk Theisen-Kunde, et al. "Demarcation of brain and tumor tissue with optical coherence tomography using prior neural networks." In Optical Coherence Imaging Techniques and Imaging in Scattering Media, edited by Maciej Wojtkowski, Yoshiaki Yasuno, and Benjamin J. Vakoc. SPIE, 2023. http://dx.doi.org/10.1117/12.2670907.

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Reports on the topic "Imaging in a neural tissue"

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Diebold, Gerald J. Electroacoustic Tissue Imaging. Fort Belvoir, VA: Defense Technical Information Center, April 2006. http://dx.doi.org/10.21236/ada456398.

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Diebold, Gerald J. Electroacoustic Tissue Imaging. Fort Belvoir, VA: Defense Technical Information Center, April 2005. http://dx.doi.org/10.21236/ada435025.

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Diebold, Gerald J. Electroacoustic Tissue Imaging. Fort Belvoir, VA: Defense Technical Information Center, April 2003. http://dx.doi.org/10.21236/ada415818.

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Bao, Gang. Multifunctional Magnetic Nanoparticle Probes for Deep-Tissue Imaging. Fort Belvoir, VA: Defense Technical Information Center, June 2005. http://dx.doi.org/10.21236/ada434280.

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Subhash, Ghatu. Cavitation Induced Structural and Neural Damage in Live Brain Tissue Slices: Relevance to TBI. Fort Belvoir, VA: Defense Technical Information Center, September 2014. http://dx.doi.org/10.21236/ada612616.

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Fasching, G. E., W. J. Loudin, D. E. Paton, and N. S. Jr Smith. Use of neural networks in the capacitance imaging system. Technical note. Office of Scientific and Technical Information (OSTI), October 1993. http://dx.doi.org/10.2172/10121969.

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Diebold, Gerald J. High Resolution X-ray Phase Contrast Imaging with Acoustic Tissue-Selective Contrast Enhancement. Fort Belvoir, VA: Defense Technical Information Center, June 2008. http://dx.doi.org/10.21236/ada488612.

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Diebold, Gerald J. High Resolution X-Ray Phase Contrast Imaging With Acoustic Tissue-Selective Contrast Enhancement. Fort Belvoir, VA: Defense Technical Information Center, June 2006. http://dx.doi.org/10.21236/ada457700.

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Diebold, Gerald J. High Resolution X-Ray Phase Contrast Imaging with Acoustic Tissue-Selective Contrast Enhancement. Fort Belvoir, VA: Defense Technical Information Center, June 2007. http://dx.doi.org/10.21236/ada472126.

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Peehl, Donna M. Discovery of Hyperpolarized Molecular Imaging Biomarkers in a Novel Prostate Tissue Slice Culture Model. Fort Belvoir, VA: Defense Technical Information Center, June 2013. http://dx.doi.org/10.21236/ada580953.

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