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Статті в журналах з теми "Magnetic Resonance Imaging data"

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Lowe, Mark J., and James A. Sorenson. "Spatially filtering functional magnetic resonance imaging data." Magnetic Resonance in Medicine 37, no. 5 (May 1997): 723–29. http://dx.doi.org/10.1002/mrm.1910370514.

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Nguyen, Hoai-Thu, Sylvain Grange, Benjamin Leporq, Magalie Viallon, Pierre Croisille, and Thomas Grenier. "Impact of Distortion on Local Radiomic Analysis of Quadriceps Based on Quantitative Magnetic Resonance Imaging Data." International Journal of Pharma Medicine and Biological Sciences 10, no. 2 (April 2021): 49–54. http://dx.doi.org/10.18178/ijpmbs.10.2.49-54.

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Tsirmpas, Charalampos, Kostas Giokas, Dimitra Iliopoulou, and Dimitris Koutsouris. "Magnetic Resonance Imaging and Magnetic Resonance Spectroscopy Cloud Computing Framework." International Journal of Reliable and Quality E-Healthcare 1, no. 4 (October 2012): 1–12. http://dx.doi.org/10.4018/ijrqeh.2012100101.

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Magnetic Resonance Imaging (MRI) and Magnetic Resonance Spectroscopy (MRS) are two non-invasive techniques that are increasingly being used to identify and quantify biochemical markers associated with certain diseases, e.g., choline in the case of cancer. The associating of MRI/MRS images, patient’s electronic health record, genome information, and environmental factors increase the precision of diagnosis and treatment. The authors present a collaboration framework based on Cloud Computing which allows analysis of MRI/MRS data based on advanced mathematical tools, advanced combination, and link discovery between different data types, so as to increase the precision and consequently avoid non-appropriate therapy and treatment plans.
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Tardivon, Anne A., Alexandra Athanasiou, Fabienne Thibault, and Carl El Khoury. "Breast imaging and reporting data system (BIRADS): Magnetic resonance imaging." European Journal of Radiology 61, no. 2 (February 2007): 212–15. http://dx.doi.org/10.1016/j.ejrad.2006.08.036.

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Kent, Daniel. "Correction: Data in Review on Magnetic Resonance Imaging." Annals of Internal Medicine 109, no. 5 (September 1, 1988): 438. http://dx.doi.org/10.7326/0003-4819-109-5-438_2.

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Pei, Haonan, Dong Cui, Weifang Cao, Yongxin Guo, and Qing Jiao. "Development of Magnetic Resonance Imaging Data Arrangement Toolbox." Journal of Medical Imaging and Health Informatics 7, no. 7 (November 1, 2017): 1607–10. http://dx.doi.org/10.1166/jmihi.2017.2173.

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Ney, Derek, Elliot K. Fishman, and Leonard Dickens. "Interactive multidimensional display of magnetic resonance imaging data." Journal of Digital Imaging 3, no. 4 (November 1990): 254–60. http://dx.doi.org/10.1007/bf03168123.

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Frank, Lawrence R., Richard B. Buxton, and Eric C. Wong. "Probabilistic analysis of functional magnetic resonance imaging data." Magnetic Resonance in Medicine 39, no. 1 (January 1998): 132–48. http://dx.doi.org/10.1002/mrm.1910390120.

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Hoda, Syed A., and Alexander J. Swistel. "“Data! Data! Data!”: Charting the Course for Mammary Magnetic Resonance Imaging." Breast Journal 20, no. 5 (September 2014): 451–52. http://dx.doi.org/10.1111/tbj.12325.

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Chen, Yongsheng, E. Mark Haacke, and Jun Li. "Peripheral nerve magnetic resonance imaging." F1000Research 8 (October 28, 2019): 1803. http://dx.doi.org/10.12688/f1000research.19695.1.

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Magnetic resonance imaging (MRI) has been used extensively in revealing pathological changes in the central nervous system. However, to date, MRI is very much underutilized in evaluating the peripheral nervous system (PNS). This underutilization is generally due to two perceived weaknesses in MRI: first, the need for very high resolution to image the small structures within the peripheral nerves to visualize morphological changes; second, the lack of normative data in MRI of the PNS and this makes reliable interpretation of the data difficult. This article reviews current state-of-the-art capabilities in in vivo MRI of human peripheral nerves. It aims to identify areas where progress has been made and those that still require further improvement. In particular, with many new therapies on the horizon, this review addresses how MRI can be used to provide non-invasive and objective biomarkers in the evaluation of peripheral neuropathies. Although a number of techniques are available in diagnosing and tracking pathologies in the PNS, those techniques typically target the distal peripheral nerves, and distal nerves may be completely degenerated during the patient’s first clinic visit. These techniques may also not be able to access the proximal nerves deeply embedded in the tissue. Peripheral nerve MRI would be an alternative to circumvent these problems. In order to address the pressing clinical needs, this review closes with a clinical protocol at 3T that will allow high-resolution, high-contrast, quantitative MRI of the proximal peripheral nerves.
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Дисертації з теми "Magnetic Resonance Imaging data"

1

Acosta, Mena Dionisio M. "Statistical classification of magnetic resonance imaging data." Thesis, University of Sussex, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390913.

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Rydell, Joakim. "Advanced MRI Data Processing." Doctoral thesis, Linköping : Department of Biomedical Engineering, Linköpings universitet, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-10038.

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Hotrakool, Wattanit. "Compressed sensing for functional magnetic resonance imaging data." Thesis, University of Sheffield, 2016. http://etheses.whiterose.ac.uk/15704/.

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This thesis addresses the possibility of applying the compressed sensing (CS) framework to Functional Magnetic Resonance Imaging (fMRI) acquisition. The fMRI is one of the non-invasive neuroimaging technique that allows the brain activity to be captured and analysed in a living body. One disadvantage of fMRI is the trade-off between the spatial and temporal resolution of the data. To keep the experiments within a reasonable length of time, the current acquisition technique sacrifices the spatial resolution in favour of the temporal resolution. It is possible to improve this trade-off using compressed sensing. The main contribution of this thesis is to propose a novel reconstruction method, named Referenced Compressed Sensing, which exploits the redundancy between a signal and a correlated reference by using their distance as an objective function. The compressed video sequences reconstructed using Referenced CS have at least 50% higher in terms of Peak Signal-to-Noise Ratio (PSNR) compared to state-of-the-art conventional reconstruction methods. This thesis also addresses two issues related to Referenced CS. Firstly, the relationship between the reference and the reconstruction performance is studied. To maintain the high-quality references, the Running Gaussian Average (RGA) reference estimator is proposed. The reconstructed results have at least 3dB better PSNR performance with the use of RGA references. Secondly, the Referenced CS with Least Squares is proposed. This study shows that by incorporating the correlated reference, it is possible to perform a linear reconstruction as opposed to the iterative reconstruction commonly used in CS. This approach gives at least 19% improvement in PSNR compared to the state of the art, while reduces the computation time by at most 1200 times. The proposed method is applied to the fMRI data. This study shows that, using the same amount of samples, the data reconstructed using Referenced CS has higher resolution than the conventional acquisition technique and has on average 50% higher PSNR than state-of-the-art reconstructions. Lastly, to enhance the feature of interest in the fMRI data, the baseline independent (BI) analysis is proposed. Using the BI analysis shows up to 25% improvement in the accuracy of the Referenced CS feature.
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Wu, Bing. "Exploiting data sparsity in parallel magnetic resonance imaging." Thesis, University of Canterbury. Electrical and Computer Engineering, 2010. http://hdl.handle.net/10092/3914.

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Magnetic resonance imaging (MRI) is a widely employed imaging modality that allows observation of the interior of human body. Compared to other imaging modalities such as the computed tomography (CT), MRI features a relatively long scan time that gives rise to many potential issues. The advent of parallel MRI, which employs multiple receiver coils, has started a new era in speeding up the scan of MRI by reducing the number of data acquisitions. However, the finally recovered images from under-sampled data sets often suffer degraded image quality. This thesis explores methods that incorporate prior knowledge of the image to be reconstructed to achieve improved image recovery in parallel MRI, following the philosophy that ‘if some prior knowledge of the image to be recovered is known, the image could be recovered better than without’. Specifically, the prior knowledge of image sparsity is utilized. Image sparsity exists in different domains. Image sparsity in the image domain refers to the fact that the imaged object only occupies a portion of the imaging field of view; image sparsity may also exist in a transform domain for which there is a high level of energy concentration in the image transform. The use of both types of sparsity is considered in this thesis. There are three major contributions in this thesis. The first contribution is the development of ‘GUISE’. GUISE employs an adaptive sampling design method that achieves better exploitation of image domain sparsity in parallel MRI. Secondly, the development of ‘PBCS’ and ‘SENSECS’. PBCS achieves better exploitation of transform domain sparsity by incorporating a prior estimate of the image to be recovered. SENSECS is an application of PBCS that achieves better exploitation of transform domain sparsity in parallel MRI. The third contribution is the implementation of GUISE and PBCS in contrast enhanced MR angiography (CE MRA). In their applications in CE MRA, GUISE and PBCS share the common ground of exploiting the high sparsity of the contrast enhanced angiogram. The above developments are assessed in various ways using both simulated and experimental data. The potential extensions of these methods are also suggested.
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Maas, Luis C. (Luis Carlos). "Processing strategies for functional magnetic resonance imaging data sets." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/85262.

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Анотація:
Thesis (Ph.D.)--Harvard--Massachusetts Institute of Technology Division of Health Sciences and Technology, 1999.
Includes bibliographical references (leaves 108-118).
by Luis Carlos Maas, III.
Ph.D.
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Bernier, Jessica Ashley. "A RECONSTRUCTION PROGRAM FOR RADIAL MAGNETIC RESONANCE IMAGING DATA." Thesis, The University of Arizona, 2009. http://hdl.handle.net/10150/192289.

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Hamilton, Lei Hou. "Reduced-data magnetic resonance imaging reconstruction methods: constraints and solutions." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/42707.

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Imaging speed is very important in magnetic resonance imaging (MRI), especially in dynamic cardiac applications, which involve respiratory motion and heart motion. With the introduction of reduced-data MR imaging methods, increasing acquisition speed has become possible without requiring a higher gradient system. But these reduced-data imaging methods carry a price for higher imaging speed. This may be a signal-to-noise ratio (SNR) penalty, reduced resolution, or a combination of both. Many methods sacrifice edge information in favor of SNR gain, which is not preferable for applications which require accurate detection of myocardial boundaries. The central goal of this thesis is to develop novel reduced-data imaging methods to improve reconstructed image performance. This thesis presents a novel reduced-data imaging method, PINOT (Parallel Imaging and NOquist in Tandem), to accelerate MR imaging. As illustrated by a variety of computer simulated and real cardiac MRI data experiments, PINOT preserves the edge details, with flexibility of improving SNR by regularization. Another contribution is to exploit the data redundancy from parallel imaging, rFOV and partial Fourier methods. A Gerchberg Reduced Iterative System (GRIS), implemented with the Gerchberg-Papoulis (GP) iterative algorithm is introduced. Under the GRIS, which utilizes a temporal band-limitation constraint in the image reconstruction, a variant of Noquist called iterative implementation iNoquist (iterative Noquist) is proposed. Utilizing a different source of prior information, first combining iNoquist and Partial Fourier technique (phase-constrained iNoquist) and further integrating with parallel imaging methods (PINOT-GRIS) are presented to achieve additional acceleration gains.
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8

Lin, Qihua. "Bayesian hierarchial spatiotemporal modeling of functional magnetic resonance imaging data." Ann Arbor, Mich. : ProQuest, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3245023.

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Анотація:
Thesis (Ph.D. in Statistical Science)--S.M.U., 2007.
Title from PDF title page (viewed Mar. 18, 2008). Source: Dissertation Abstracts International, Volume: 67-12, Section: B, page: 7154. Adviser: Richard F. Gunst. Includes bibliographical references.
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Takahashi, Atsushi M. (Atsushi Mark). "Rapid data acquisition and selective excitation in magnetic resonance imaging." Thesis, McGill University, 1995. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=28930.

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Many of the problems faced in magnetic resonance imaging and angiography are due to hardware limitations of the scanners used. The use of multidimensional excitation pulses, and new, fast acquisition sequences such as echo-planar and spiral imaging, put demands on the gradient systems and although technology is progressing in these areas, performance must be carefully monitored to ensure artefact-free images. This thesis deals with four distinct aspects of magnetic resonance imaging (MRI).
A simple hardware modification to decrease the minimum achievable echo-time of our MRI scanner was designed, built, programmed and tested and was found to improve the quality of inflow angiograms significantly. Further improvements were demonstrated with the use of on-resonance (binomial) magnetization transfer saturation contrast enhancement pulses.
A method for measuring the k-space trajectories of gradient waveforms was adopted, modified, validated and used to measure the k-space trajectories of gradient waveforms used for selective excitation and spiral image acquisition. Distortion was observed even when gradient waveforms were designed within specifications of the manufacturer.
The literature reports the application of the k-space model, usually associated with image acquisition, to the design of multidimensional selective excitation pulses. This thesis demonstrates theoretically and experimentally the modification of the design procedure to compensate RF envelopes for distortion of the k-space trajectories of the accompanying gradient waveforms by using measured k-space data.
The correction of spiral image reconstruction algorithms to compensate for k-space trajectory distortion was also demonstrated theoretically and experimentally.
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Bell, William L. Jr. "Three-Dimensional Segmentation and Visualization of Magnetic Resonance Imaging Data." UNF Digital Commons, 1996. http://digitalcommons.unf.edu/etd/28.

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In this thesis, I shall study and compare various methods for manipulating two- and three-dimensional image data produced with a nuclear magnetic resonance scanner. In particular, I will examine ways of focusing upon specific structures internal to the object under study (segmentation); and will explore means of rendering realistic images of these structures on a computer screen using depth-cueing, shading, and ray-casting techniques.The 3DHEAD volumetric dataset used for this project was created with the Siemens Magnetom and was provided courtesy of Siemens Medical Systems, Inc., Iselin, NJ. This dataset consists of 109 slices of a human head, with each slice stored consecutively as a 256 x 256 array. Each pixel is represented by two consecutive bytes, which make one binary integer. (A similar dataset of a human knee is also available.) The 3DHEAD dataset requires about 14 Mb of disk space uncompressed. The programs which manipulate this data are MS-DOS-based and were written and compiled using Microsoft QuickC version 2.51. The 2-D programs were executed on a CompuAdd 486DXl2-50 with 8 Mb of RAM, running MS-DOS version 6.22; the 3-D programs were executed on a 133 MHz Pentium clone with 48 Mb of RAM, running the DOS shell of Microsoft Windows 95.Our immediate objectives are to produce pleasing and informative 2-D and 3-D pictures of the internal structure of some component of the human head: for example, the brain.We need to remove from the original dataset all of the data which do not represent the brain. Then, for the 3-D images, we need to render the remaining data in such a way that it possesses depth and realism.
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Книги з теми "Magnetic Resonance Imaging data"

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Vlaardingerbroek, Marinus T. Magnetic Resonance Imaging: Theory and Practice. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999.

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Vlaardingerbroek, Marinus T. Magnetic Resonance Imaging: Theory and Practice. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003.

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3

Poldrack, Russell A. Handbook of functional MRI data analysis. Cambridge: Cambridge University Press, 2011.

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4

Siegfried, Stapf, and Han Song-l, eds. NMR imaging in chemical engineering. Weinheim: Wiley-VCH, 2006.

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5

The statistical analysis of functional MRI data. New York: Springer, 2008.

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F, Aichner, and European Magnetic Resonance Forum, eds. Three-dimensional magnetic resonance imaging: An integrated clinical up-date of 3D-imaging and 3D-postprocessing : proceedings of a joint meeting in Obergurgl, Austria, 23-27 March 1992. Oxford: Blackwell Scientific Publications, 1994.

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7

Faro, Scott H. BOLD fMRI: A guide to functional imaging for neuroscientists. New York: Springer, 2010.

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BOLD fMRI: A guide to functional imaging for neuroscientists. New York: Springer, 2010.

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Kuni, Christopher C. Introduction to computers and digital processing in medical imaging. Chicago: Year Book Medical Publishers, 1988.

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10

José, Hanson Stephen, and Bunzl Martin, eds. Foundational issues of human brain mapping. Cambridge, Mass: MIT Press, 2010.

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Частини книг з теми "Magnetic Resonance Imaging data"

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Li, Xingfeng. "First-Level fMRI Data Analysis for Activation Detection." In Functional Magnetic Resonance Imaging Processing, 39–71. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-7302-8_2.

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Li, Xingfeng. "Second-Level fMRI Data Analysis Using Mixed Model." In Functional Magnetic Resonance Imaging Processing, 73–111. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-7302-8_3.

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Bernhardt, J. H. "5.4 Magnetic Resonance Imaging, Diagnostic Ultrasound." In Fundamentals and Data in Radiobiology, Radiation Biophysics, Dosimetry and Medical Radiological Protection, 288–301. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-23684-6_25.

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Hernández, Juan A., Martha L. Mora, Emanuele Schiavi, and Pablo Toharia. "RF Inhomogeneity Correction Algorithm in Magnetic Resonance Imaging." In Biological and Medical Data Analysis, 1–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30547-7_1.

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Zeng, Weiyi, Sanyong Zou, and Hao Zuo. "Functional Magnetic Resonance Imaging Based on Large Data." In Proceedings of the Second International Conference on Mechatronics and Automatic Control, 987–94. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13707-0_108.

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Soares, J., and V. Alves. "Functional Magnetic Resonance Imaging Data Manipulation - A new approach." In IFMBE Proceedings, 36–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03904-1_10.

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Tajini, Badr, Hugo Richard, and Bertrand Thirion. "Functional Magnetic Resonance Imaging Data Augmentation Through Conditional ICA." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, 491–500. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87196-3_46.

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Zhang, Lei, Dimitris Samaras, Dardo Tomasi, Nelly Alia-Klein, Lisa Cottone, Andreana Leskovjan, Nora Volkow, and Rita Goldstein. "Exploiting Temporal Information in Functional Magnetic Resonance Imaging Brain Data." In Lecture Notes in Computer Science, 679–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11566465_84.

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Zhao, Qun, Jose Principe, Jeffery Fitzsimmons, Margaret Bradley, and Peter Lang. "Functional Magnetic Resonance Imaging Data Analysis with Information-Theoretic Approaches." In Biocomputing, 159–73. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4613-0259-9_9.

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Bezener, Martin, Lynn E. Eberly, John Hughes, Galin Jones, and Donald R. Musgrove. "Bayesian Spatiotemporal Modeling for Detecting Neuronal Activation via Functional Magnetic Resonance Imaging." In Handbook of Big Data Analytics, 485–501. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-18284-1_19.

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Тези доповідей конференцій з теми "Magnetic Resonance Imaging data"

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Koenig, H. A., and G. Laub. "Tissue Discrimination In Magnetic Resonance 3D Data Sets." In Medical Imaging II, edited by Roger H. Schneider and Samuel J. Dwyer III. SPIE, 1988. http://dx.doi.org/10.1117/12.968697.

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McColl, Roderick W., Geoffrey D. Clarke, and Ronald M. Peshock. "Reconstruction of magnetic resonance images from EPI data." In Medical Imaging 1993, edited by Rodney Shaw. SPIE, 1993. http://dx.doi.org/10.1117/12.154586.

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Ro, Yong M., Ralph A. Neff, and Avideh Zakhor. "Matching pursuit data acquisition in magnetic resonance imaging." In Medical Imaging 1997, edited by Richard L. Van Metter and Jacob Beutel. SPIE, 1997. http://dx.doi.org/10.1117/12.274025.

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Ouda, Bassem K., Bassel S. Tawfik, and Abou-Bakr M. Youssef. "Simple mathematical model for functional magnetic resonance imaging data." In Medical Imaging 2002, edited by Anne V. Clough and Chin-Tu Chen. SPIE, 2002. http://dx.doi.org/10.1117/12.463605.

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Kadah, Yasser M., Xiangyang Ma, Stephen LaConte, Inas Yassine, and Xiaoping Hu. "Robust multi-component modeling of diffusion tensor magnetic resonance imaging data." In Medical Imaging, edited by Amir A. Amini and Armando Manduca. SPIE, 2005. http://dx.doi.org/10.1117/12.596155.

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Noll, Douglas C., John M. Pauly, Dwight G. Nishimura, and Albert Macovski. "Magnetic resonance reconstruction from projections using half the data." In Medical Imaging '91, San Jose, CA, edited by Roger H. Schneider. SPIE, 1991. http://dx.doi.org/10.1117/12.43428.

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Ahmad, Fayyaz, Bushra Talib, and Safee Ullah Ch. "Perception of Difficulty Using Functional Magnetic Resonance Imaging Data." In the 2019 11th International Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3313991.3313992.

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Bolton, Thomas A. W., Younes Farouj, Mert Inan, and Dimitri Van De Ville. "Structurally-Informed Deconvolution of Functional Magnetic Resonance Imaging Data." In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI). IEEE, 2019. http://dx.doi.org/10.1109/isbi.2019.8759218.

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Cebral, Juan R., Marcelo Castro, Orlando Soto, Rainald Loehner, Peter J. Yim, and Noam Alperin. "Finite element modeling of the Circle of Willis from magnetic resonance data." In Medical Imaging 2003, edited by Anne V. Clough and Amir A. Amini. SPIE, 2003. http://dx.doi.org/10.1117/12.480317.

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10

Chandra, Ramesh, and Henry Rusinek. "New method of data acquisition in k-space in magnetic resonance imaging." In Medical Imaging 1994, edited by Rodney Shaw. SPIE, 1994. http://dx.doi.org/10.1117/12.174278.

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Звіти організацій з теми "Magnetic Resonance Imaging data"

1

Russek, Stephen E. Magnetic Resonance Imaging Biomarker Calibration Service:. Gaithersburg, MD: National Institute of Standards and Technology, 2022. http://dx.doi.org/10.6028/nist.sp.250-100.

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2

Schweizer, M. Developments in boron magnetic resonance imaging (MRI). Office of Scientific and Technical Information (OSTI), November 1995. http://dx.doi.org/10.2172/421332.

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3

Schmidt, D. M., and M. A. Espy. Low-field magnetic resonance imaging of gases. Office of Scientific and Technical Information (OSTI), November 1998. http://dx.doi.org/10.2172/674672.

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4

Bronskill, Michael J., Paul L. Carson, Steve Einstein, Michael Koshinen, Margit Lassen, Seong Ki Mun, William Pavlicek, et al. Site Planning for Magnetic Resonance Imaging Systems. AAPM, 1986. http://dx.doi.org/10.37206/19.

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5

Budakian, Raffi. Nanometer-Scale Force Detected Nuclear Magnetic Resonance Imaging. Fort Belvoir, VA: Defense Technical Information Center, January 2013. http://dx.doi.org/10.21236/ada591583.

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6

Haslam, Philip. Multiparametric magnetic resonance imaging of the prostate gland. BJUI Knowledge, March 2021. http://dx.doi.org/10.18591/bjuik.0731.

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7

Schmidt, D. M., J. S. George, S. I. Penttila, and A. Caprihan. Nuclear magnetic resonance imaging with hyper-polarized noble gases. Office of Scientific and Technical Information (OSTI), October 1997. http://dx.doi.org/10.2172/534499.

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8

Botto, R. E., and G. D. Cody. Magnetic resonance imaging of solvent transport in polymer networks. Office of Scientific and Technical Information (OSTI), February 1995. http://dx.doi.org/10.2172/26588.

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9

Diegert, C. Innovative computing for diagnoses from medical, magnetic-resonance imaging. Office of Scientific and Technical Information (OSTI), January 1997. http://dx.doi.org/10.2172/477671.

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10

Ivankov, A. P., and P. V. Selivyerstov. Magnetic resonance imaging for subchondral insufficiency fracture of knee. OFERNIO, February 2022. http://dx.doi.org/10.12731/ofernio.2022.24949.

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