Journal articles: 'Imaging in a neural tissue'

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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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Yadav, R., S. Mukherjee, F. Maxfield, J. Jhaveri, R. Leung, S. Rao, G. Tan, and A. Tewari. "MP-4.16: Real-Time Tissue Imaging of Prostate and Periprostatic Neural Tissue with Multiphoton Microscopy." Urology 72, no. 5 (November 2008): S89—S90. http://dx.doi.org/10.1016/j.urology.2008.08.259.

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Okamoto, Nariaki, María Rita Rodríguez-Luna, Valentin Bencteux, Mahdi Al-Taher, Lorenzo Cinelli, Eric Felli, Takeshi Urade, et al. "Computer-Assisted Differentiation between Colon-Mesocolon and Retroperitoneum Using Hyperspectral Imaging (HSI) Technology." Diagnostics 12, no. 9 (September 15, 2022): 2225. http://dx.doi.org/10.3390/diagnostics12092225.

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Complete mesocolic excision (CME), which involves the adequate resection of the tumor-bearing colonic segment with “en bloc” removal of its mesocolon along embryological fascial planes is associated with superior oncological outcomes. However, CME presents a higher complication rate compared to non-CME resections due to a higher risk of vascular injury. Hyperspectral imaging (HSI) is a contrast-free optical imaging technology, which facilitates the quantitative imaging of physiological tissue parameters and the visualization of anatomical structures. This study evaluates the accuracy of HSI combined with deep learning (DL) to differentiate the colon and its mesenteric tissue from retroperitoneal tissue. In an animal study including 20 pig models, intraoperative hyperspectral images of the sigmoid colon, sigmoid mesentery, and retroperitoneum were recorded. A convolutional neural network (CNN) was trained to distinguish the two tissue classes using HSI data, validated with a leave-one-out cross-validation process. The overall recognition sensitivity of the tissues to be preserved (retroperitoneum) and the tissues to be resected (colon and mesentery) was 79.0 ± 21.0% and 86.0 ± 16.0%, respectively. Automatic classification based on HSI and CNNs is a promising tool to automatically, non-invasively, and objectively differentiate the colon and its mesentery from retroperitoneal tissue.

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Zeyad O Moussa and AE El Latif. "Breast cancer detection in ultrasound imaging." World Journal of Advanced Research and Reviews 12, no. 1 (October 30, 2021): 308–14. http://dx.doi.org/10.30574/wjarr.2021.12.1.0522.

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Breast cancer has become one of the most cancers among women in worldwide countries, as well as a leading cause of death. The use of ultrasound images in medical diagnosis and treatment of patients is critical. The success of cancer treatment and outcome is largely dependent on early detection. Imaging modalities such as ultrasonography are used to identify cancer. Ultrasound imaging is noninvasive, widely available, simple to use, and less expensive than other imaging technologies. As a result, ultrasonography is becoming more used as a cancer detection tool. Ultrasound imaging, on the other hand, is prone to noise and speckle artifacts. First, the ultrasound machine's raw picture data extraction is disabled. As a result, the process of recognizing malignant spots is prioritized. Artificial neural networks and other tissue characterization approaches are used. This technique was chosen because categorization and detection systems have greatly increased in their ability to assist medical experts in diagnosis. Manually classifying ultrasound images not only takes a long time and effort. As a result, a neural network classification-based automatic tissue characterization technique is proposed. Finally, the newly developed algorithms can aid specialists in recognizing suspicious aberrant tissue locations.

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Smith, Lauren C., and Adam Kimbrough. "Leveraging Neural Networks in Preclinical Alcohol Research." Brain Sciences 10, no. 9 (August 21, 2020): 578. http://dx.doi.org/10.3390/brainsci10090578.

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Alcohol use disorder is a pervasive healthcare issue with significant socioeconomic consequences. There is a plethora of neural imaging techniques available at the clinical and preclinical level, including magnetic resonance imaging and three-dimensional (3D) tissue imaging techniques. Network-based approaches can be applied to imaging data to create neural networks that model the functional and structural connectivity of the brain. These networks can be used to changes to brain-wide neural signaling caused by brain states associated with alcohol use. Neural networks can be further used to identify key brain regions or neural “hubs” involved in alcohol drinking. Here, we briefly review the current imaging and neurocircuit manipulation methods. Then, we discuss clinical and preclinical studies using network-based approaches related to substance use disorders and alcohol drinking. Finally, we discuss how preclinical 3D imaging in combination with network approaches can be applied alone and in combination with other approaches to better understand alcohol drinking.

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Durur-Subasi, Irmak, Adem Karaman, Elif Demirci, Sare Sipal, and Mufide Akcay. "The benign mimickers of carcinoma on breast MRI." Journal of Mind and Medical Sciences 9, no. 1 (April 10, 2022): 96–101. http://dx.doi.org/10.22543/7674.91.p96101.

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The similarity between benign and malignant pathologies on magnetic resonance imaging (MRI) and a wide-ranging variability of the lesions from benign proliferative changes to invasive breast carcinoma cause a lower and wide-ranging specificity of breast MRI relative to its surpass sensitivity. A wide range of tissue components such as the skin, the adipose tissue, vascular and neural tissues, connective tissues, glandular tissues, ducts, and muscle tissues are found here all together. This pictorial review was aimed at deliberating benign mimickers of breast carcinoma on MRI and trying to call attention to the overlapping and distinctive features.

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Hemalatha, C., S. Satheesh, N. Kamal, C. Devi, A. Vinothkumar, and A. Kannan. "Segmentation of Tissue-Injured Melanoma Convolution Neural Networks." Journal of Computational and Theoretical Nanoscience 18, no. 4 (April 1, 2021): 1256–62. http://dx.doi.org/10.1166/jctn.2021.9389.

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In global dermatological conditions, skin lesions are significant. Curable early in the diagnosis, only skin lesions can be accurately identified by highly trained dermatologists. Around 21 million patients are diagnosed with this disease and more than 10.12 million deaths worldwide. This paper presents basic work for the detection and ensuing purpose of the CNN to dermoscopic images of skin lesions with cancerous inclination. The models proposed are trained and evaluated in the 2018 International Skin Imaging Collaboration challenge, comprising 2100 training samples and 750 test samples, on normal benchmark datasets. Skin-injured images were mainly segment based on person thresholds for channel intensity. The images were added to CNN to extract features. The extracted characteristics were then used to classify the associated ANN classification. In the past, many approaches have been used to diagnose subjects with variable success levels. The methodology described in this paper showed associated accuracy of 97.13% in comparison to the previous best of ninety seven.

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Iglesias-Rey, Sara, Felipe Antunes-Santos, Cathleen Hagemann, David Gómez-Cabrero, Humberto Bustince, Rickie Patani, Andrea Serio, Bernard De Baets, and Carlos Lopez-Molina. "Unsupervised Cell Segmentation and Labelling in Neural Tissue Images." Applied Sciences 11, no. 9 (April 21, 2021): 3733. http://dx.doi.org/10.3390/app11093733.

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Neurodegenerative diseases are a group of largely incurable disorders characterised by the progressive loss of neurons and for which often the molecular mechanisms are poorly understood. To bridge this gap, researchers employ a range of techniques. A very prominent and useful technique adopted across many different fields is imaging and the analysis of histopathological and fluorescent label tissue samples. Although image acquisition has been efficiently automated recently, automated analysis still presents a bottleneck. Although various methods have been developed to automate this task, they tend to make use of single-purpose machine learning models that require extensive training, imposing a significant workload on the experts and introducing variability in the analysis. Moreover, these methods are impractical to audit and adapt, as their internal parameters are difficult to interpret and change. Here, we present a novel unsupervised automated schema for object segmentation of images, exemplified on a dataset of tissue images. Our schema does not require training data, can be fully audited and is based on a series of understandable biological decisions. In order to evaluate and validate our schema, we compared it with a state-of-the-art automated segmentation method for post-mortem tissues of ALS patients.

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Huang, Shiliang, Qiang Shen, and Timothy Q. Duong. "Artificial Neural Network Prediction of Ischemic Tissue Fate in Acute Stroke Imaging." Journal of Cerebral Blood Flow & Metabolism 30, no. 9 (April 28, 2010): 1661–70. http://dx.doi.org/10.1038/jcbfm.2010.56.

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Multimodal magnetic resonance imaging of acute stroke provides predictive value that can be used to guide stroke therapy. A flexible artificial neural network (ANN) algorithm was developed and applied to predict ischemic tissue fate on three stroke groups: 30-, 60-minute, and permanent middle cerebral artery occlusion in rats. Cerebral blood flow (CBF), apparent diffusion coefficient (ADC), and spin–spin relaxation time constant (T2) were acquired during the acute phase up to 3 hours and again at 24 hours followed by histology. Infarct was predicted on a pixel-by-pixel basis using only acute (30-minute) stroke data. In addition, neighboring pixel information and infarction incidence were also incorporated into the ANN model to improve prediction accuracy. Receiver-operating characteristic analysis was used to quantify prediction accuracy. The major findings were the following: (1) CBF alone poorly predicted the final infarct across three experimental groups; (2) ADC alone adequately predicted the infarct; (3) CBF+ADC improved the prediction accuracy; (4) inclusion of neighboring pixel information and infarction incidence further improved the prediction accuracy; and (5) prediction was more accurate for permanent occlusion, followed by 60- and 30-minute occlusion. The ANN predictive model could thus provide a flexible and objective framework for clinicians to evaluate stroke treatment options on an individual patient basis.

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Gulani, V., G. A. Iwamoto, and P. C. Lauterbur. "Apparent water diffusion measurements in electrically stimulated neural tissue." Magnetic Resonance in Medicine 41, no. 2 (February 1999): 241–46. http://dx.doi.org/10.1002/(sici)1522-2594(199902)41:2<241::aid-mrm5>3.0.co;2-e.

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Streich, Lina, Juan Carlos Boffi, Ling Wang, Khaleel Alhalaseh, Matteo Barbieri, Ronja Rehm, Senthilkumar Deivasigamani, Cornelius T. Gross, Amit Agarwal, and Robert Prevedel. "High-resolution structural and functional deep brain imaging using adaptive optics three-photon microscopy." Nature Methods 18, no. 10 (September 30, 2021): 1253–58. http://dx.doi.org/10.1038/s41592-021-01257-6.

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AbstractMultiphoton microscopy has become a powerful tool with which to visualize the morphology and function of neural cells and circuits in the intact mammalian brain. However, tissue scattering, optical aberrations and motion artifacts degrade the imaging performance at depth. Here we describe a minimally invasive intravital imaging methodology based on three-photon excitation, indirect adaptive optics (AO) and active electrocardiogram gating to advance deep-tissue imaging. Our modal-based, sensorless AO approach is robust to low signal-to-noise ratios as commonly encountered in deep scattering tissues such as the mouse brain, and permits AO correction over large axial fields of view. We demonstrate near-diffraction-limited imaging of deep cortical spines and (sub)cortical dendrites up to a depth of 1.4 mm (the edge of the mouse CA1 hippocampus). In addition, we show applications to deep-layer calcium imaging of astrocytes, including fibrous astrocytes that reside in the highly scattering corpus callosum.

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Zhang, Yijie, Luzhe Huang, Tairan Liu, Keyi Cheng, Kevin de Haan, Yuzhu Li, Bijie Bai, and Aydogan Ozcan. "Virtual Staining of Defocused Autofluorescence Images of Unlabeled Tissue Using Deep Neural Networks." Intelligent Computing 2022 (October 27, 2022): 1–13. http://dx.doi.org/10.34133/2022/9818965.

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Deep learning-based virtual staining was developed to introduce image contrast to label-free tissue sections, digitally matching the histological staining, which is time-consuming, labor-intensive, and destructive to tissue. Standard virtual staining requires high autofocusing precision during the whole slide imaging of label-free tissue, which consumes a significant portion of the total imaging time and can lead to tissue photodamage. Here, we introduce a fast virtual staining framework that can stain defocused autofluorescence images of unlabeled tissue, achieving equivalent performance to virtual staining of in-focus label-free images, also saving significant imaging time by lowering the microscope’s autofocusing precision. This framework incorporates a virtual autofocusing neural network to digitally refocus the defocused images and then transforms the refocused images into virtually stained images using a successive network. These cascaded networks form a collaborative inference scheme: the virtual staining model regularizes the virtual autofocusing network through a style loss during the training. To demonstrate the efficacy of this framework, we trained and blindly tested these networks using human lung tissue. Using 4× fewer focus points with 2× lower focusing precision, we successfully transformed the coarsely-focused autofluorescence images into high-quality virtually stained H&E images, matching the standard virtual staining framework that used finely-focused autofluorescence input images. Without sacrificing the staining quality, this framework decreases the total image acquisition time needed for virtual staining of a label-free whole-slide image (WSI) by ~32%, together with a ~89% decrease in the autofocusing time, and has the potential to eliminate the laborious and costly histochemical staining process in pathology.

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Ostrem, J. S., A. D. Valdes, and P. D. Edmonds. "Application of Neural Nets to Ultrasound Tissue Characterization." Ultrasonic Imaging 13, no. 3 (July 1991): 298–99. http://dx.doi.org/10.1177/016173469101300306.

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Ostrem, J. "Application of neural nets to ultrasound tissue characterization." Ultrasonic Imaging 13, no. 3 (July 1991): 298–99. http://dx.doi.org/10.1016/0161-7346(91)90078-v.

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Ostrem, J. "Application of neural nets to ultrasound tissue characterization." Ultrasonic Imaging 13, no. 2 (April 1991): 190. http://dx.doi.org/10.1016/0161-7346(91)90086-w.

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Hedges, R. E. M., Z. X. Jiang, C. Bronk Ramsey, A. Cowey, J. D. B. Roberts, and P. Somogyi. "Imaging of radiocarbon-labelled tracer molecules in neural tissue using accelerator mass spectrometry." Nature 383, no. 6603 (October 31, 1996): 823–26. http://dx.doi.org/10.1038/383823a0.

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Campi, G., G. Pezzotti, M. Fratini, A. Ricci, M. Burghammer, R. Cancedda, M. Mastrogiacomo, I. Bukreeva, and A. Cedola. "Imaging regenerating bone tissue based on neural networks applied to micro-diffraction measurements." Applied Physics Letters 103, no. 25 (December 16, 2013): 253703. http://dx.doi.org/10.1063/1.4852056.

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Baur, A., A. Stäbler, K. Psenner, Ch Hamburger, and M. Reiser. "Imaging findings in patients with ventral dural defects and herniation of neural tissue." European Radiology 7, no. 8 (September 1997): 1259–63. http://dx.doi.org/10.1007/s003300050286.

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Wei, Petert, Dalun Leong, Evan Calabrese, Leonard White, Theodore Pierce, Simon Platt, and James Provenzale. "Diffusion Tensor Imaging of Neural Tissue Organization: Correlations between Radiologic and Histologic Parameters." Neuroradiology Journal 26, no. 5 (October 2013): 501–10. http://dx.doi.org/10.1177/197140091302600502.

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Stanisz, Greg J., Stephanie Webb, Catherine A. Munro, Teresa Pun, and Rajiv Midha. "MR properties of excised neural tissue following experimentally induced inflammation." Magnetic Resonance in Medicine 51, no. 3 (February 25, 2004): 473–79. http://dx.doi.org/10.1002/mrm.20008.

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Shen, Che-Chou, and Jui-En Yang. "Estimation of Ultrasound Echogenicity Map from B-Mode Images Using Convolutional Neural Network." Sensors 20, no. 17 (August 31, 2020): 4931. http://dx.doi.org/10.3390/s20174931.

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In ultrasound B-mode imaging, speckle noises decrease the accuracy of estimation of tissue echogenicity of imaged targets from the amplitude of the echo signals. In addition, since the granular size of the speckle pattern is affected by the point spread function (PSF) of the imaging system, the resolution of B-mode image remains limited, and the boundaries of tissue structures often become blurred. This study proposed a convolutional neural network (CNN) to remove speckle noises together with improvement of image spatial resolution to reconstruct ultrasound tissue echogenicity map. The CNN model is trained using in silico simulation dataset and tested with experimentally acquired images. Results indicate that the proposed CNN method can effectively eliminate the speckle noises in the background of the B-mode images while retaining the contours and edges of the tissue structures. The contrast and the contrast-to-noise ratio of the reconstructed echogenicity map increased from 0.22/2.72 to 0.33/44.14, and the lateral and axial resolutions also improved from 5.9/2.4 to 2.9/2.0, respectively. Compared with other post-processing filtering methods, the proposed CNN method provides better approximation to the original tissue echogenicity by completely removing speckle noises and improving the image resolution together with the capability for real-time implementation.

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Shah, Lubdha M., and Jeffrey S. Ross. "Imaging of Spine Trauma." Neurosurgery 79, no. 5 (July 12, 2016): 626–42. http://dx.doi.org/10.1227/neu.0000000000001336.

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Abstract Imaging with computed tomography and magnetic resonance imaging is fundamental to the evaluation of traumatic spinal injury. Specifically, neuroradiologic techniques show the exact location of injury, evaluate the stability of the spine, and determine neural element compromise. This review focuses on the complementary role of different radiologic modalities in the diagnosis of patients with traumatic injuries of the spine. The role of imaging in spinal trauma classifications will be addressed. The importance of magnetic resonance imaging in the assessment of soft tissue injury, particularly of the spinal cord, will be discussed. Last, the increasing role of advanced imaging techniques for prognostication of the traumatic spine will be explored.

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Cakirer, S., B. Yagmurlu, and M. R. Savas. "Sturge‐weber syndrome: diffusion magnetic resonance imaging and proton magnetic resonance spectroscopy findings." Acta Radiologica 46, no. 4 (July 2005): 407–10. http://dx.doi.org/10.1080/02841850510021274.

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We report on the diffusion magnetic resonance imaging (MRI) and proton MR spectroscopy findings of a 26‐year‐old female patient with Sturge‐Weber syndrome. Echo‐planar trace diffusion MRI revealed mildly high signal intensity changes at parieto‐occipital lobes on b = 1000 s/mm2 images, suggesting restricted diffusion. On corresponding apparent diffusion coefficient maps, those areas had moderately high signal intensity and high apparent diffusion coefficient values (around 0.9×10(−3) mm2/s) compared with the contralateral symmetrical normal side of the brain (0.776×10(−3) mm2/s). This finding was consistent with increased motion of water molecules (disintegration of the neural tissue) in these regions. Proton MR spectroscopy revealed decreased N‐acetyl aspartate and increased choline peaks, indicating disintegration of neural tissue associated with neuronal loss as well.

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Osorio-Londoño, Diana María, José Rafael Godínez-Fernández, Ma Cristina Acosta-García, Juan Morales-Corona, Roberto Olayo-González, and Axayácatl Morales-Guadarrama. "Pyrrole Plasma Polymer-Coated Electrospun Scaffolds for Neural Tissue Engineering." Polymers 13, no. 22 (November 10, 2021): 3876. http://dx.doi.org/10.3390/polym13223876.

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Promising strategies for neural tissue engineering are based on the use of three-dimensional substrates for cell anchorage and tissue development. In this work, fibrillar scaffolds composed of electrospun randomly- and aligned-oriented fibers coated with plasma synthesized pyrrole polymer, doped and undoped with iodine, were fabricated and characterized. Infrared spectroscopy, thermogravimetric analysis, and X-ray diffraction analysis revealed the functional groups and molecular integration of each scaffold, as well as the effect of plasma polymer synthesis on crystallinity. Scanning microscopy imaging demonstrated the porous fibrillar micrometric structure of the scaffolds, which afforded adhesion, infiltration, and survival for the neural cells. Orientation analysis of electron microscope images confirmed the elongation of neurite-like cell structures elicited by undoped plasma pyrrole polymer-coated aligned scaffolds, without any biochemical stimuli. The MTT colorimetric assay validated the biocompatibility of the fabricated composite materials, and further evidenced plasma pyrrole polymer-coated aligned scaffolds as permissive substrates for the support of neural cells. These results suggest plasma synthesized pyrrole polymer-coated aligned scaffolds are promising materials for tissue engineering applications.

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Hoffmann, Nico, Yordan Radev, Edmund Koch, Uwe Petersohn, Gerald Steiner, and Matthias Kirsch. "Intraoperative mapping of the sensory cortex by time-resolved thermal imaging." Biomedical Engineering / Biomedizinische Technik 63, no. 5 (October 25, 2018): 567–72. http://dx.doi.org/10.1515/bmt-2017-0229.

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AbstractThe resection of brain tumor requires a precise distinction between eloquent areas of the brain and pathological tumor tissue in order to improve the extent of resection as well as the patient’s progression free survival time. In this study, we discuss mathematical tools necessary to recognize neural activity using thermal imaging cameras. The main contribution to thermal radiation of the exposed human cortex is regional cerebral blood flow (CBF). In fact, neurovascular coupling links neural activity to changes in regional CBF which in turn affects the cortical temperature. We propose a statistically sound framework to visualize neural activity of the primary somatosensory cortex. The framework incorporatesa prioriknown experimental conditions such as the thermal response to neural activity as well as unrelated effects induced by random neural activity and autoregulation. These experimental conditions can be adopted to certain electrical stimulation protocols so that the framework allows to unveil arbitrary evoked neural activity. The method was applied to semisynthetic as well as two intraoperative cases with promising results as we were able to map the eloquent sensory cortex with high sensitivity. Furthermore, the results were validated by anatomical localization and electrophysiological measurements.

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Khaladkar, Sanjay M., Aarushi Gupta, Shibani Saluja, Ronak Savani, and Radhika Jaipuria. "Neurovascular Lipomatous Hamartoma in scapular region." International Journal of Biomedical Science 14, no. 2 (December 15, 2018): 85–88. http://dx.doi.org/10.59566/ijbs.2018.14085.

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Hamartoma is defined as non-neoplastic malformation characterized by proliferation of mature cells and tissues indigenous to the affected part. Excess of one or more tissues in a disorganized manner can occur. Usually hamartomas are present at birth or in young age but also reported in later ages of life. Hence diagnosis of hematomas cannot be made on the age factor. No diagnostic criteria have been laid down for Neurovascular Hamartoma (NVH). NVH contain small to medium sized vessels and closely packed groups of well-formed nerve bundles in loose connective tissue matrix. NMH are rare intra-neural hamartomas having admixture of mature neural elements, mature skeletal elements and no cellular atypia. We report a case of 25-year-old male patient with swelling in left scapular region since 12 years. On imaging, it was diagnosed as neurovascular lipomatous hamartoma due to admixture of neural elements, fat and vessels.

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Minami, Akira, Yuuki Kurebayashi, Tadanobu Takahashi, Tadamune Otsubo, Kiyoshi Ikeda, and Takashi Suzuki. "The Function of Sialidase Revealed by Sialidase Activity Imaging Probe." International Journal of Molecular Sciences 22, no. 6 (March 20, 2021): 3187. http://dx.doi.org/10.3390/ijms22063187.

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Sialidase cleaves sialic acid residues from glycans such as glycoproteins and glycolipids. In the brain, desorption of the sialic acid by sialidase is essential for synaptic plasticity, learning and memory and synaptic transmission. BTP3-Neu5Ac has been developed for sensitive imaging of sialidase enzyme activity in mammalian tissues. Sialidase activity in the rat hippocampus detected with BTP3-Neu5Ac increases rapidly by neuronal depolarization. It is presumed that an increased sialidase activity in conjunction with neural excitation is involved in the formation of the neural circuit for memory. Since sialidase inhibits the exocytosis of the excitatory neurotransmitter glutamate, the increased sialidase activity by neural excitation might play a role in the negative feedback mechanism against the glutamate release. Mammalian tissues other than the brain have also been stained with BTP3-Neu5Ac. On the basis of information on the sialidase activity imaging in the pancreas, it was found that sialidase inhibitor can be used as an anti-diabetic drug that can avoid hypoglycemia, a serious side effect of insulin secretagogues. In this review, we discuss the role of sialidase in the brain as well as in the pancreas and skin, as revealed by using a sialidase activity imaging probe. We also present the detection of influenza virus with BTP3-Neu5Ac and modification of BTP3-Neu5Ac.

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Hillman, Elizabeth M. C., Venkatakaushik Voleti, Wenze Li, and Hang Yu. "Light-Sheet Microscopy in Neuroscience." Annual Review of Neuroscience 42, no. 1 (July 8, 2019): 295–313. http://dx.doi.org/10.1146/annurev-neuro-070918-050357.

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Light-sheet microscopy is an imaging approach that offers unique advantages for a diverse range of neuroscience applications. Unlike point-scanning techniques such as confocal and two-photon microscopy, light-sheet microscopes illuminate an entire plane of tissue, while imaging this plane onto a camera. Although early implementations of light sheet were optimized for longitudinal imaging of embryonic development in small specimens, emerging implementations are capable of capturing light-sheet images in freely moving, unconstrained specimens and even the intact in vivo mammalian brain. Meanwhile, the unique photobleaching and signal-to-noise benefits afforded by light-sheet microscopy's parallelized detection deliver the ability to perform volumetric imaging at much higher speeds than can be achieved using point scanning. This review describes the basic principles and evolution of light-sheet microscopy, followed by perspectives on emerging applications and opportunities for both imaging large, cleared, and expanded neural tissues and high-speed, functional imaging in vivo.

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Wirth, Edward D., Daniel P. Theele, Thomas H. Mareci, Douglas K. Anderson, Stacey A. Brown, and Paul J. Reier. "In vivo magnetic resonance imaging of fetal cat neural tissue transplants in the adult cat spinal cord." Journal of Neurosurgery 76, no. 2 (February 1992): 261–74. http://dx.doi.org/10.3171/jns.1992.76.2.0261.

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✓ Magnetic resonance (MR) imaging was evaluated for its possible diagnostic application in determining the survival of fetal central nervous system tissue grafts in the injured spinal cord. Hemisection cavities were made at the T11—L1 level of eight adult female cats. Immediately thereafter, several pieces of tissue, either obtained from the fetal cat brain stem on embryonic Day 37 (E-37), from the fetal neocortex on E-37, or from the fetal spinal cord on E-23, were implanted into the cavities made in seven cats. The eighth cat served as a control for the effect of the lesion only. In another group of four animals, a static-load compression injury was made at the L-2 level. Seven weeks later, the lesion was resected in three cases and fragments of either fetal brainstem or spinal cord tissue were introduced. A small cyst was observed in a fourth cat in the compression injury group and a suspension of dissociated E-23 brain-stem cells was injected into this region of cavitation without disturbing the surrounding leptomeninges. Five months to 2 years posttransplantation, MR imaging was performed with a 2.0-tesla VIS imaging spectrometer by acquiring multislice spin-echo images (TR 1000 msec, TE 30 msec) in both the transverse and sagittal planes. Collectively, these intermediate-weighted images revealed homogeneous, slightly hyperintense signals at the graft site relative to the neighboring host tissue in seven of the 11 graft recipients. Two of the remaining four cats exhibited signals from the graft site that were approximately isointense with the adjacent host spinal cord, and the final two cats and the lesion-only control presented with very hypointense transplant/resection regions. The hyperintense and isointense images were tentatively interpreted as representing viable graft tissue, whereas the hypointense transplant/resection sites were considered to be indicative of a lack of transplant survival or the absence of tissue in the lesion-only control animal. Postmortem gross inspection of fixed specimens and light microscopy verified the MR findings in the control animal in 10 of the 11 graft recipients by showing either transplants and/or cysts corresponding to the MR images obtained. In one cat in the hemisection group, histological analysis revealed a very small piece of graft tissue that was not detected on the MR images. Therefore, it is suggested that within certain spatial- and contrast-resolution limits, MR imaging can reliably detect the presence of transplanted neural tissue in both the hemisected and compression-injured spinal cord of living animals. Thus, MR imaging can serve as an important adjunct to histological, electrophysiological, and long-term behavioral analyses of graft-mediated anatomical and functional repair of the injured spinal cord. It is further suggested that this noninvasive diagnostic approach offers many advantages in terms of the judicious and optimum use of valuable animal models, and that these findings address an important prerequisite (in situ verification of transplant survival) for any future clinical trials involving these or equivalent neural tissue grafting approaches, when such are warranted.

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Freeman, Ralph D., and Baowang Li. "Neural–metabolic coupling in the central visual pathway." Philosophical Transactions of the Royal Society B: Biological Sciences 371, no. 1705 (October 5, 2016): 20150357. http://dx.doi.org/10.1098/rstb.2015.0357.

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Studies are described which are intended to improve our understanding of the primary measurements made in non-invasive neural imaging. The blood oxygenation level-dependent signal used in functional magnetic resonance imaging (fMRI) reflects changes in deoxygenated haemoglobin. Tissue oxygen concentration, along with blood flow, changes during neural activation. Therefore, measurements of tissue oxygen together with the use of a neural sensor can provide direct estimates of neural–metabolic interactions. We have used this relationship in a series of studies in which a neural microelectrode is combined with an oxygen micro-sensor to make simultaneous co-localized measurements in the central visual pathway. Oxygen responses are typically biphasic with small initial dips followed by large secondary peaks during neural activation. By the use of established visual response characteristics, we have determined that the oxygen initial dip provides a better estimate of local neural function than the positive peak. This contrasts sharply with fMRI for which the initial dip is unreliable. To extend these studies, we have examined the relationship between the primary metabolic agents, glucose and lactate, and associated neural activity. For this work, we also use a Doppler technique to measure cerebral blood flow (CBF) together with neural activity. Results show consistent synchronously timed changes such that increases in neural activity are accompanied by decreases in glucose and simultaneous increases in lactate. Measurements of CBF show clear delays with respect to neural response. This is consistent with a slight delay in blood flow with respect to oxygen delivery during neural activation. This article is part of the themed issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’.

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Park, Jung-Hoon, Wei Sun, and Meng Cui. "High-resolution in vivo imaging of mouse brain through the intact skull." Proceedings of the National Academy of Sciences 112, no. 30 (July 13, 2015): 9236–41. http://dx.doi.org/10.1073/pnas.1505939112.

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Multiphoton microscopy is the current method of choice for in vivo deep-tissue imaging. The long laser wavelength suffers less scattering, and the 3D-confined excitation permits the use of scattered signal light. However, the imaging depth is still limited because of the complex refractive index distribution of biological tissue, which scrambles the incident light and destroys the optical focus needed for high resolution imaging. Here, we demonstrate a wavefront-shaping scheme that allows clear imaging through extremely turbid biological tissue, such as the skull, over an extended corrected field of view (FOV). The complex wavefront correction is obtained and directly conjugated to the turbid layer in a noninvasive manner. Using this technique, we demonstrate in vivo submicron-resolution imaging of neural dendrites and microglia dynamics through the intact skulls of adult mice. This is the first observation, to our knowledge, of dynamic morphological changes of microglia through the intact skull, allowing truly noninvasive studies of microglial immune activities free from external perturbations.

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Inglis, B. A., E. L. Bossart, D. L. Buckley, E. D. Wirth, and T. H. Mareci. "Visualization of neural tissue water compartments using biexponential diffusion tensor MRI." Magnetic Resonance in Medicine 45, no. 4 (2001): 580–87. http://dx.doi.org/10.1002/mrm.1079.

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Hedges, R. E. M., Z. X. Jiang, C. Bronk Ramsey, A. Cowey, J. D. B. Roberts, and P. Somogyi. "Erratum: Imaging of radiocarbon-labelled tracer molecules in neural tissue using accelerator mass spectrometry." Nature 390, no. 6657 (November 1997): 315. http://dx.doi.org/10.1038/36909.

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EKHOLM, SVEN E., THOMAS W. MORRIS, DAVID FONTE, and LINDA ISAAC. "Iopamidol and Neural Tissue Metabolism A Comparative In Vitro Study." Investigative Radiology 21, no. 10 (October 1986): 798–801. http://dx.doi.org/10.1097/00004424-198610000-00006.

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Ekholm, S. E., T. W. Morris, D. Fonte, J. Simon, G. Harinetti, P. Leakey, and L. Isaac. "112. EFFECTS OF CONTRAST MEDIA ON NEURAL TISSUE GLUCOSE UPTAKE." Investigative Radiology 22, no. 9 (September 1987): S28. http://dx.doi.org/10.1097/00004424-198709000-00127.

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Choukri, Ahmanna Hussein, I. Azzahiri, M. Benzalim, and S. Alj. "Giant Plexiform Neurofibroma of the Thigh: A Case Report." Scholars Journal of Medical Case Reports 11, no. 04 (April 14, 2023): 495–99. http://dx.doi.org/10.36347/sjmcr.2023.v11i04.019.

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Neurofibromas are known to manifest most frequently as localized lesions, less frequently as a diffuse form, and rarely as a plexiform variety We report the clinical and imaging features of a young male patient with biopsy proven plexiform neurofibroma (PNF). A 23 years old male, followed for neurofibromatosis type 1, plexiform lesions of the left lower limb progressively increasing in volume with scrotal extension. The MRI showed thickening of the cutaneous and subcutaneous soft tissues on the medial and posterior aspect of the left thigh, extending to the perineo-scrotal region. The role of imaging is important for a variety of reasons, including delineating the extent of involvement and effect on adjacent structures, exposing associated anomalies and last but not least, for predicting possible malignant transformation. MRI is the reference standard modality for evaluating neural tissues and also for delineating the parent nerve in cases of tumors of neural origin. Therapy of plexiform neurofibromas is usually surgical, aiming at resecting deforming masses and cancerous tissue when malignant transformation occurs.

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Chande, Ruchi D., Rosalyn Hobson Hargraves, Norma Ortiz-Robinson, and Jennifer S. Wayne. "Predictive Behavior of a Computational Foot/Ankle Model through Artificial Neural Networks." Computational and Mathematical Methods in Medicine 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/3602928.

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Computational models are useful tools to study the biomechanics of human joints. Their predictive performance is heavily dependent on bony anatomy and soft tissue properties. Imaging data provides anatomical requirements while approximate tissue properties are implemented from literature data, when available. We sought to improve the predictive capability of a computational foot/ankle model by optimizing its ligament stiffness inputs using feedforward and radial basis function neural networks. While the former demonstrated better performance than the latter per mean square error, both networks provided reasonable stiffness predictions for implementation into the computational model.

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Khoshdel, Vahab, Ahmed Ashraf, and Joe LoVetri. "Enhancement of Multimodal Microwave-Ultrasound Breast Imaging Using a Deep-Learning Technique." Sensors 19, no. 18 (September 19, 2019): 4050. http://dx.doi.org/10.3390/s19184050.

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We present a deep learning method used in conjunction with dual-modal microwave-ultrasound imaging to produce tomographic reconstructions of the complex-valued permittivity of numerical breast phantoms. We also assess tumor segmentation performance using the reconstructed permittivity as a feature. The contrast source inversion (CSI) technique is used to create the complex-permittivity images of the breast with ultrasound-derived tissue regions utilized as prior information. However, imaging artifacts make the detection of tumors difficult. To overcome this issue we train a convolutional neural network (CNN) that takes in, as input, the dual-modal CSI reconstruction and attempts to produce the true image of the complex tissue permittivity. The neural network consists of successive convolutional and downsampling layers, followed by successive deconvolutional and upsampling layers based on the U-Net architecture. To train the neural network, the input-output pairs consist of CSI’s dual-modal reconstructions, along with the true numerical phantom images from which the microwave scattered field was synthetically generated. The reconstructed permittivity images produced by the CNN show that the network is not only able to remove the artifacts that are typical of CSI reconstructions, but can also improve the detectability of tumors. The performance of the CNN is assessed using a four-fold cross-validation on our dataset that shows improvement over CSI both in terms of reconstruction error and tumor segmentation performance.

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Ley, Charles, Kerstin Hansson, Lennart Sjöström, and Martin Rapp. "Postoperative computed tomography and low-field magnetic resonance imaging findings in dogs with degenerative lumbosacral stenosis treated by dorsal laminectomy." Veterinary and Comparative Orthopaedics and Traumatology 30, no. 02 (2017): 143–52. http://dx.doi.org/10.3415/vcot-16-06-0096.

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SummaryObjectives: To describe postoperative computed tomography (CT) and magnetic resonance imaging (MRI) findings in dogs with degenerative lumbosacral stenosis (DLSS) treated by dorsal laminectomy and partial discectomy.Methods: Prospective clinical case study of dogs diagnosed with and treated for DLSS. Surgical and clinical findings were described. Computed tomography and low field MRI findings pre- and postoperatively were described and graded. Clinical, CT and MRI examinations were performed four to 18 months after surgery.Results: Eleven of 13 dogs were clinically improved and two dogs had unchanged clinical status postoperatively despite imaging signs of neural compression. Vacuum phenomenon, spondylosis, sclerosis of the seventh lumbar (L7) and first sacral (S1) vertebrae endplates and lumbosacral intervertebral joint osteoarthritis became more frequent in postoperative CT images. Postoperative MRI showed mild disc extrusions in five cases, and in all cases contrast enhancing non-discal tissue was present. All cases showed contrast enhancement of the L7 spinal nerves both pre- and postoperatively and seven had contrast enhancement of the lumbosacral intervertebral joints and paraspinal tissue postoperatively. Articular process fractures or fissures were noted in four dogs.Clinical significance: The study indicates that imaging signs of neural compression are common after DLSS surgery, even in dogs that have clinical improvement. Contrast enhancement of spinal nerves and soft tissues around the region of disc herniation is common both pre- and postoperatively and thus are unreliable criteria for identifying complications of the DLSS surgery.Supplementary Material to this article is available online at https://doi.org/10.3415/VCOT-16-06-0096

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Odrobina, Ewa E., Toby Y. J. Lam, Teresa Pun, Rajiv Midha, and Greg J. Stanisz. "MR properties of excised neural tissue following experimentally induced demyelination." NMR in Biomedicine 18, no. 5 (June 10, 2005): 277–84. http://dx.doi.org/10.1002/nbm.951.

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Saxena, Sanjay, Anahita Fathi Kazerooni, Erik Toorens, Spyridon Bakas, Hamed Akbari, Chiharu Sako, Elizabeth Mamourian, et al. "NIMG-73. CAPTURING GLIOBLASTOMA HETEROGENEITY USING IMAGING AND DEEP LEARNING: APPLICATION TO MGMT PROMOTER METHYLATION." Neuro-Oncology 23, Supplement_6 (November 2, 2021): vi146. http://dx.doi.org/10.1093/neuonc/noab196.570.

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Abstract PURPOSE Intratumor heterogeneity is frequent in glioblastoma (GB), giving rise to the tumor’s resistance to standard therapies and, ultimately, poorer clinical outcomes. Yet heterogeneity is often not quantified when assessing the genomic or methylomic profile of a tumor, when a single tissue sample is analyzed. This study proposes a novel approach to non-invasively characterize heterogeneity across glioblastoma using deep learning analysis MRI scans, using MGMT promoter methylation (MGMTpm) as a test-case, and validates the imaging-derived heterogeneity maps with MGMTpm heterogeneity measured via multiple tissue samples. METHODS Multi-parametric MRI (mpMRI) scans (T1, T1-Gd, T2, T2-FLAIR) of 181 patients with newly diagnosed glioblastoma, who underwent surgical tumor resection and had MGMT methylation assessment results, were retrospectively collected. We trained a 5-fold cross-validated deep convolutional neural network with six convolutional layers for a discovery cohort of 137 patients by placing overlapping regional patches over the whole tumor on mpMRI scans to capture spatial heterogeneity of MGMTpm status in different regions within the tumor. Our approach effectively hypothesized that despite heterogeneity in the training examples, dominant imaging patterns would be captured by deep learning. Trained model was independently applied to an unseen replication cohort of 44 patients, with multiple tissue specimens chosen from different spatial regions within the tumor, allowing us to compare imaging- and tissue-based MGMTpm estimates. RESULTS Our model yielded AUC of 0.75 (95% CI: 0.65–0.79) for global MGMT status prediction, which reflected the heterogeneity in MGMTpm, but also that a dominant imaging pattern of MGMT methylation seemed to emerge. In methylated patients with multiple tissue samples, a significant Pearson's correlation coefficient of 0.64 (p< 0.05) was found between imaging-based heterogeneity maps and MGMTpm heterogeneity. CONCLUSION A novel method based on mpMRI and deep neural networks yielded imaging-based heterogeneity maps that strongly associated with intratumor molecular heterogeneity in MGMT promoter methylated tumors.

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

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