Academic literature on the topic 'Artificial magnetic resonance'
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Journal articles on the topic "Artificial magnetic resonance"
Belyaev, Boris A., Andrey V. Izotov, Alexander A. Leksikov, Alexey M. Serzhantov, Konstantin V. Lemberg, and Platon N. Solovev. "Thin Magnetic Films with Artificial Texture on Substrate: Microwave Properties." Solid State Phenomena 215 (April 2014): 233–36. http://dx.doi.org/10.4028/www.scientific.net/ssp.215.233.
Full textHill, Charles E., Luca Biasiolli, Matthew D. Robson, Vicente Grau, and Michael Pavlides. "Emerging artificial intelligence applications in liver magnetic resonance imaging." World Journal of Gastroenterology 27, no. 40 (October 28, 2021): 6825–43. http://dx.doi.org/10.3748/wjg.v27.i40.6825.
Full textSolomou, Aikaterini, Anastasios Apostolos, and Nikolaos Ntoulias. "Artificial Intelligence in Magnetic Resonance Imaging: A Feasible Practice?" Journal of Medical Imaging and Radiation Sciences 51, no. 3 (September 2020): 501–2. http://dx.doi.org/10.1016/j.jmir.2020.04.010.
Full textSeetharam, Karthik, and Stamatios Lerakis. "Cardiac magnetic resonance imaging: the future is bright." F1000Research 8 (September 13, 2019): 1636. http://dx.doi.org/10.12688/f1000research.19721.1.
Full textSzarf, Gilberto, and Cesar H. Nomura. "APLICAÇÃO DA INTELIGÊNCIA ARTIFICIAL EM IMAGEM CARDIOVASCULAR: EM TOMOGRAFIA COMPUTADORIZADA E RMN." Revista da Sociedade de Cardiologia do Estado de São Paulo 32, no. 1 (January 15, 2022): 27–30. http://dx.doi.org/10.29381/0103-8559/2022320127-30.
Full textIonescu, Daniela, and Gabriela Apreotesei. "Wave absorption control in the new designed photonic metamaterials with artificial opal." MATEC Web of Conferences 178 (2018): 04004. http://dx.doi.org/10.1051/matecconf/201817804004.
Full textCau, Riccardo, Valeria Cherchi, Giulio Micheletti, Michele Porcu, Lorenzo Mannelli, Pierpaolo Bassareo, Jasjit S. Suri, and Luca Saba. "Potential Role of Artificial Intelligence in Cardiac Magnetic Resonance Imaging." Journal of Thoracic Imaging 36, no. 3 (March 24, 2021): 142–48. http://dx.doi.org/10.1097/rti.0000000000000584.
Full textCalivà, Francesco, Nikan K. Namiri, Maureen Dubreuil, Valentina Pedoia, Eugene Ozhinsky, and Sharmila Majumdar. "Studying osteoarthritis with artificial intelligence applied to magnetic resonance imaging." Nature Reviews Rheumatology 18, no. 2 (November 30, 2021): 112–21. http://dx.doi.org/10.1038/s41584-021-00719-7.
Full textMurphy, Matthew C., Armando Manduca, Joshua D. Trzasko, Kevin J. Glaser, John Huston, and Richard L. Ehman. "Artificial neural networks for stiffness estimation in magnetic resonance elastography." Magnetic Resonance in Medicine 80, no. 1 (November 28, 2017): 351–60. http://dx.doi.org/10.1002/mrm.27019.
Full textRajini N, Hema. "Automatic classification of MR brain tumor images using KNN, ANN, SVM and CNN." International research journal of engineering, IT & scientific research 3, no. 1 (January 31, 2017): 36–44. http://dx.doi.org/10.21744/irjeis.v3n1.895.
Full textDissertations / Theses on the topic "Artificial magnetic resonance"
Middleton, Ian. "Segmentation of magnetic resonance images using artificial neural networks." Thesis, University of Southampton, 1998. https://eprints.soton.ac.uk/256267/.
Full textClark, Matthew C. "Knowledge-Guided Processing of Magnetic Resonance Images of the Brain." Scholar Commons, 1998. http://purl.fcla.edu/fcla/etd/SFE0000001.
Full textKoivula, A. (Antero). "Magnetic resonance image distortions due to artificial macroscopic objects:an example: correction of image distortion caused by an artificial hip prosthesis." Doctoral thesis, University of Oulu, 2002. http://urn.fi/urn:isbn:951426827X.
Full textKaraman, Turker. "Prediction Of Multiphase Flow Properties From Nuclear Magnetic Resonance Imaging." Phd thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610382/index.pdf.
Full texty Dolostone and Dolomitic Limestone core plugs. Pore network body and throat parameters were obtained from serial computerized tomography scans and thin section images. It was observed that pore body and throat sizes were not statistically correlated. It was also observed that the developed PN model can be used to model different displacement mechanisms. By using the synthetic data obtained from PN model, an Artificial Neural Network (ANN) model was developed and tested. It has been observed that the developed ANN tool can be used to estimate oil &ndash
water relative permeability data very well (with less than 0.05 mean square error) given a T2 signal. It was finally concluded that the developed tools can be used to obtain multiphase flow functions directly from an NMR well log such as Combinable Magnetic Resonance (CMR).
Kandasamy, Sivakumar P. "In vivo monitoring of collagen-sponge remodeling using MRI." Worcester, Mass. : Worcester Polytechnic Institute, 2007. http://www.wpi.edu/Pubs/ETD/Available/etd-032607-091929/.
Full textLi, Chao. "Characterising heterogeneity of glioblastoma using multi-parametric magnetic resonance imaging." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/287475.
Full textHahn, Artur [Verfasser], and Jürgen P. [Akademischer Betreuer] Debus. "Artificial magnetic resonance contrasts based on microvascular geometry: A numerical basis / Artur Hahn ; Betreuer: Jürgen P. Debus." Heidelberg : Universitätsbibliothek Heidelberg, 2021. http://d-nb.info/1237324033/34.
Full textClark, Matthew C. "Knowledge guided processing of magnetic resonance images of the brain [electronic resource] / by Matthew C. Clark." University of South Florida, 2001. http://purl.fcla.edu/fcla/etd/SFE0000001.
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ABSTRACT: This dissertation presents a knowledge-guided expert system that is capable of applying routinesfor multispectral analysis, (un)supervised clustering, and basic image processing to automatically detect and segment brain tissue abnormalities, and then label glioblastoma-multiforme brain tumors in magnetic resonance volumes of the human brain. The magnetic resonance images used here consist of three feature images (T1-weighted, proton density, T2-weighted) and the system is designed to be independent of a particular scanning protocol. Separate, but contiguous 2D slices in the transaxial plane form a brain volume. This allows complete tumor volumes to be measured and if repeat scans are taken over time, the system may be used to monitor tumor response to past treatments and aid in the planning of future treatment. Furthermore, once processing begins, the system is completely unsupervised, thus avoiding the problems of human variability found in supervised segmentation efforts.Each slice is initially segmented by an unsupervised fuzzy c-means algorithm. The segmented image, along with its respective cluster centers, is then analyzed by a rule-based expert system which iteratively locates tissues of interest based on the hierarchy of cluster centers in feature space. Model-based recognition techniques analyze tissues of interest by searching for expected characteristics and comparing those found with previously defined qualitative models. Normal/abnormal classification is performed through a default reasoning method: if a significant model deviation is found, the slice is considered abnormal. Otherwise, the slice is considered normal. Tumor segmentation in abnormal slices begins with multispectral histogram analysis and thresholding to separate suspected tumor from the rest of the intra-cranial region. The tumor is then refined with a variant of seed growing, followed by spatial component analysis and a final thresholding step to remove non-tumor pixels.The knowledge used in this system was extracted from general principles of magnetic resonance imaging, the distributions of individual voxels and cluster centers in feature space, and anatomical information. Knowledge is used both for single slice processing and information propagation between slices. A standard rule-based expert system shell (CLIPS) was modified to include the multispectral analysis, clustering, and image processing tools.A total of sixty-three volume data sets from eight patients and seventeen volunteers (four with and thirteen without gadolinium enhancement) were acquired from a single magnetic resonance imaging system with slightly varying scanning protocols were available for processing. All volumes were processed for normal/abnormal classification. Tumor segmentation was performed on the abnormal slices and the results were compared with a radiologist-labeled ground truth' tumor volume and tumor segmentations created by applying supervised k-nearest neighbors, a partially supervised variant of the fuzzy c-means clustering algorithm, and a commercially available seed growing package. The results of the developed automatic system generally correspond well to ground truth, both on a per slice basis and more importantly in tracking total tumor volume during treatment over time.
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Juan, Albarracín Javier. "Unsupervised learning for vascular heterogeneity assessment of glioblastoma based on magnetic resonance imaging: The Hemodynamic Tissue Signature." Doctoral thesis, Universitat Politècnica de València, 2020. http://hdl.handle.net/10251/149560.
Full text[EN] The future of medical imaging is linked to Artificial Intelligence (AI). The manual analysis of medical images is nowadays an arduous, error-prone and often unaffordable task for humans, which has caught the attention of the Machine Learning (ML) community. Magnetic Resonance Imaging (MRI) provides us with a wide variety of rich representations of the morphology and behavior of lesions completely inaccessible without a risky invasive intervention. Nevertheless, harnessing the powerful but often latent information contained in MRI acquisitions is a very complicated task, which requires computational intelligent analysis techniques. Central nervous system tumors are one of the most critical diseases studied through MRI. Specifically, glioblastoma represents a major challenge, as it remains a lethal cancer that, to date, lacks a satisfactory therapy. Of the entire set of characteristics that make glioblastoma so aggressive, a particular aspect that has been widely studied is its vascular heterogeneity. The strong vascular proliferation of glioblastomas, as well as their robust angiogenesis and extensive microvasculature heterogeneity have been claimed responsible for the high lethality of the neoplasm. This thesis focuses on the research and development of the Hemodynamic Tissue Signature (HTS) method: an unsupervised ML approach to describe the vascular heterogeneity of glioblastomas by means of perfusion MRI analysis. The HTS builds on the concept of habitats. A habitat is defined as a sub-region of the lesion with a particular MRI profile describing a specific physiological behavior. The HTS method delineates four habitats within the glioblastoma: the HAT habitat, as the most perfused region of the enhancing tumor; the LAT habitat, as the region of the enhancing tumor with a lower angiogenic profile; the potentially IPE habitat, as the non-enhancing region adjacent to the tumor with elevated perfusion indexes; and the VPE habitat, as the remaining edema of the lesion with the lowest perfusion profile. The research and development of the HTS method has generated a number of contributions to this thesis. First, in order to verify that unsupervised learning methods are reliable to extract MRI patterns to describe the heterogeneity of a lesion, a comparison among several unsupervised learning methods was conducted for the task of high grade glioma segmentation. Second, a Bayesian unsupervised learning algorithm from the family of Spatially Varying Finite Mixture Models is proposed. The algorithm integrates a Markov Random Field prior density weighted by the probabilistic Non-Local Means function, to codify the idea that neighboring pixels tend to belong to the same semantic object. Third, the HTS method to describe the vascular heterogeneity of glioblastomas is presented. The HTS method has been applied to real cases, both in a local single-center cohort of patients, and in an international retrospective cohort of more than 180 patients from 7 European centers. A comprehensive evaluation of the method was conducted to measure the prognostic potential of the HTS habitats. Finally, the technology developed in this thesis has been integrated into an online open-access platform for its academic use. The ONCOhabitats platform is hosted at https://www.oncohabitats.upv.es, and provides two main services: 1) glioblastoma tissue segmentation, and 2) vascular heterogeneity assessment of glioblastomas by means of the HTS method. The results of this thesis have been published in ten scientific contributions, including top-ranked journals and conferences in the areas of Medical Informatics, Statistics and Probability, Radiology & Nuclear Medicine and Machine Learning. An industrial patent registered in Spain, Europe and EEUU was also issued. Finally, the original ideas conceived in this thesis led to the foundation of ONCOANALYTICS CDX, a company framed into the business model of companion diagnostics for pharmaceutical compounds.
[CA] El futur de la imatge mèdica està lligat a la intel·ligència artificial. L'anàlisi manual d'imatges mèdiques és hui dia una tasca àrdua, propensa a errors i sovint inassequible per als humans, que ha cridat l'atenció de la comunitat d'Aprenentatge Automàtic (AA). La Imatge per Ressonància Magnètica (IRM) ens proporciona una àmplia varietat de representacions de la morfologia i el comportament de lesions inaccessibles sense una intervenció invasiva arriscada. Tanmateix, explotar la potent però sovint latent informació continguda a les adquisicions de IRM esdevé una tasca molt complicada, que requereix tècniques d'anàlisi computacional intel·ligent. Els tumors del sistema nerviós central són una de les malalties més crítiques estudiades a través de IRM. Específicament, el glioblastoma representa un gran repte, ja que, fins hui, continua siguent un càncer letal que manca d'una teràpia satisfactòria. Del conjunt de característiques que fan del glioblastoma un tumor tan agressiu, un aspecte particular que ha sigut àmpliament estudiat és la seua heterogeneïtat vascular. La forta proliferació vascular dels glioblastomes, així com la seua robusta angiogènesi han sigut considerades responsables de l'alta letalitat d'aquesta neoplàsia. Aquesta tesi es centra en la recerca i desenvolupament del mètode Hemodynamic Tissue Signature (HTS): un mètode d'AA no supervisat per descriure l'heterogeneïtat vascular dels glioblastomas mitjançant l'anàlisi de perfusió per IRM. El mètode HTS es basa en el concepte d'hàbitat, que es defineix com una subregió de la lesió amb un perfil particular d'IRM, que descriu un comportament fisiològic concret. El mètode HTS delinea quatre hàbitats dins del glioblastoma: l'hàbitat HAT, com la regió més perfosa del tumor amb captació de contrast; l'hàbitat LAT, com la regió del tumor amb un perfil angiogènic més baix; l'hàbitat IPE, com la regió adjacent al tumor amb índexs de perfusió elevats, i l'hàbitat VPE, com l'edema restant de la lesió amb el perfil de perfusió més baix. La recerca i desenvolupament del mètode HTS ha originat una sèrie de contribucions emmarcades a aquesta tesi. Primer, per verificar la fiabilitat dels mètodes d'AA no supervisats en l'extracció de patrons d'IRM, es va realitzar una comparativa en la tasca de segmentació de gliomes de grau alt. Segon, s'ha proposat un algorisme d'AA no supervisat dintre de la família dels Spatially Varying Finite Mixture Models. L'algorisme proposa un densitat a priori basada en un Markov Random Field combinat amb la funció probabilística Non-Local Means, per a codificar la idea que els píxels veïns tendeixen a pertànyer al mateix objecte semàntic. Tercer, es presenta el mètode HTS per descriure l'heterogeneïtat vascular dels glioblastomas. El mètode HTS s'ha aplicat a casos reals en una cohort local d'un sol centre i en una cohort internacional de més de 180 pacients de 7 centres europeus. Es va dur a terme una avaluació exhaustiva del mètode per mesurar el potencial pronòstic dels hàbitats HTS. Finalment, la tecnologia desenvolupada en aquesta tesi s'ha integrat en una plataforma online ONCOhabitats (https://www.oncohabitats.upv.es). La plataforma ofereix dos serveis: 1) segmentació dels teixits del glioblastoma, i 2) avaluació de l'heterogeneïtat vascular dels glioblastomes mitjançant el mètode HTS. Els resultats d'aquesta tesi han sigut publicats en deu contribucions científiques, incloent revistes i conferències de primer nivell a les àrees d'Informàtica Mèdica, Estadística i Probabilitat, Radiologia i Medicina Nuclear i Aprenentatge Automàtic. També es va emetre una patent industrial registrada a Espanya, Europa i els EEUU. Finalment, les idees originals concebudes en aquesta tesi van donar lloc a la creació d'ONCOANALYTICS CDX, una empresa emmarcada en el model de negoci dels companion diagnostics de compostos farmacèutics.
En este sentido quiero agradecer a las diferentes instituciones y estructuras de financiación de investigación que han contribuido al desarrollo de esta tesis. En especial quiero agradecer a la Universitat Politècnica de València, donde he desarrollado toda mi carrera acadèmica y científica, así como al Ministerio de Ciencia e Innovación, al Ministerio de Economía y Competitividad, a la Comisión Europea, al EIT Health Programme y a la fundación Caixa Impulse
Juan Albarracín, J. (2020). Unsupervised learning for vascular heterogeneity assessment of glioblastoma based on magnetic resonance imaging: The Hemodynamic Tissue Signature [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/149560
TESIS
Silva, Cíntia Beatriz de Souza. "Processamento de sinais de ressonância magnética nuclear usando classificador neural para reconhecimento de carne bovina." Universidade de São Paulo, 2007. http://www.teses.usp.br/teses/disponiveis/18/18153/tde-30102007-141411/.
Full textGuaranteeing the quality of the bovine meat produced in Brazil has been a concern of the producers because it contributes to increase the export and the domestic consumption of the product. Therefore, new methods have been researched that analyze and guarantee the quality of the meat in a fast, efficient and non destructive way. Nuclear magnetic resonance (NMR) has been highlighted as one of the techniques of meat quality control. In this work study artificial neural networks are being used for pattern recognition from data obtained by the resonance equipment, originating from bovine meat. More specifically, the respective data have been used by a multilayer perceptron network for extraction of bovine meat characteristics, making possible the classification of both genetic group and animal sex starting from a single meat sample. Several results of experimental tests are also presented to illustrate the performance of the proposed approach.
Books on the topic "Artificial magnetic resonance"
Reference manual for magnetic resonance safety, implants, and devices: 2011 edition. 2nd ed. Los Angeles, Calif: Biomedical Research Publishing Group, 2011.
Find full textShellock, Frank G. Reference manual for magnetic resonance safety, implants, and devices: 2014 edition. 2nd ed. Los Angeles, Calif: Biomedical Research Publishing Group, 2014.
Find full text(14th, European Society of Neuroradiology Congress. Computer aided neuroradiology: XIVth Congress of the European Society of Neuroradiology, Udine, Italy September 8-12, 1987. Roma: CIC Edizioni internazionali, 1987.
Find full textRAZMJOOY, Rajinikanth. Frontiers Artificial Intelligence Magnhb: Frontiers of Artificial Intelligence in Magnetic Resonance Imaging. Institute of Physics Publishing, 2024.
Find full textReference Manual for Magnetic Resonance Safety, Implants, and Devices. Biomedical Research Publishing Group, 2004.
Find full textSolymar, L., D. Walsh, and R. R. A. Syms. Artificial materials or metamaterials. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198829942.003.0015.
Full textPocket Guide to MR Procedures and Metallic Objects: Update 1999. Lippincott Williams & Wilkins, 1999.
Find full textPocket Guide to MR Procedures and Metallic Objects: Update 1998. Lippincott Williams & Wilkins, 1998.
Find full textPocket Guide to MR Procedures and Metallic Objects : Update 1994. Raven Pr, 1994.
Find full textBook chapters on the topic "Artificial magnetic resonance"
Fyrdahl, Alexander, Nicole Seiberlich, and Jesse I. Hamilton. "Magnetic Resonance Fingerprinting: The Role of Artificial Intelligence." In Artificial Intelligence in Cardiothoracic Imaging, 201–15. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92087-6_20.
Full textMuscogiuri, Giuseppe, Pablo Garcia-Polo, Marco Guglielmo, Andrea Baggiano, Martin A. Janich, and Gianluca Pontone. "Artificial Intelligence Integration into the Magnetic Resonance System." In Artificial Intelligence in Cardiothoracic Imaging, 195–200. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92087-6_19.
Full textQin, Chen, and Daniel Rueckert. "Artificial Intelligence-Based Image Reconstruction in Cardiac Magnetic Resonance." In Artificial Intelligence in Cardiothoracic Imaging, 139–47. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92087-6_14.
Full textTao, Qian, and Rob J. van der Geest. "Artificial Intelligence-Based Evaluation of Functional Cardiac Magnetic Resonance Imaging." In Artificial Intelligence in Cardiothoracic Imaging, 321–31. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92087-6_33.
Full textDomínguez, Enrique, Domingo López-Rodríguez, Ezequiel López-Rubio, Rosa Maza-Quiroga, Miguel A. Molina-Cabello, and Karl Thurnhofer-Hemsi. "Super-Resolution of 3D Magnetic Resonance Images of the Brain." In Artificial Intelligence in Healthcare and Medicine, 157–76. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003120902-6.
Full textPeper, Eva S., Sebastian Kozerke, and Pim van Ooij. "Magnetic Resonance Imaging-Based 4D Flow: The Role of Artificial Intelligence." In Artificial Intelligence in Cardiothoracic Imaging, 333–48. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92087-6_34.
Full textPasserini, Tiziano, Yitong Yang, Teodora Chitiboi, and John N. Oshinski. "Magnetic Resonance Imaging-Based Coronary Flow: The Role of Artificial Intelligence." In Artificial Intelligence in Cardiothoracic Imaging, 349–63. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92087-6_35.
Full textHammernik, Kerstin, and Mehmet Akçakaya. "Artificial Intelligence for Image Enhancement and Reconstruction in Magnetic Resonance Imaging." In Artificial Intelligence in Cardiothoracic Imaging, 125–38. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92087-6_13.
Full textTaenaka, Yoshiyuki, Hisateru Takano, Hiroyoshi Sekii, Masayuki Kinoshita, Hiroyuki Noda, Takeshi Nakatani, Akihiko Yagura, et al. "Development of a better fit total artificial heart based on magnetic resonance imaging anatomical study." In Artificial Heart 3, 215–20. Tokyo: Springer Japan, 1991. http://dx.doi.org/10.1007/978-4-431-68126-7_25.
Full textGarcía, Cristina, and José Alí Moreno. "Kernel Based Method for Segmentation and Modeling of Magnetic Resonance Images." In Advances in Artificial Intelligence – IBERAMIA 2004, 636–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30498-2_64.
Full textConference papers on the topic "Artificial magnetic resonance"
Rizza, C., E. Palange, A. Galante, and M. Alecci. "Magnetic Localized Surface Plasmons For Magnetic Resonance Imaging Applications." In 2020 Fourteenth International Congress on Artificial Materials for Novel Wave Phenomena (Metamaterials). IEEE, 2020. http://dx.doi.org/10.1109/metamaterials49557.2020.9285034.
Full textFricke, Florian, Safdar Mahmood, Javier Hoffmann, Marcelo Brandalero, Sascha Liehr, Simon Kern, Klas Meyer, et al. "Artificial Intelligence for Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy." In 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2021. http://dx.doi.org/10.23919/date51398.2021.9473958.
Full textLi, Yuwei, Minye Wu, Yuyao Zhang, Lan Xu, and Jingyi Yu. "PIANO: A Parametric Hand Bone Model from Magnetic Resonance Imaging." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/113.
Full textShchelokova, A. V., E. A. Brui, S. B. Glybovski, A. P. Slobozhanyuk, I. V. Melchakova, and P. A. Belov. "Tunability methods for magnetic resonance imaging applications of metasurfaces." In 2018 12th International Congress on Artificial Materials for Novel Wave Phenomena (Metamaterials). IEEE, 2018. http://dx.doi.org/10.1109/metamaterials.2018.8534104.
Full textRizza, C., M. Fantasia, E. Palange, A. Galante, and M. Alecci. "Meta-optics inspired configurations for magnetic resonance imaging applications." In 2019 Thirteenth International Congress on Artificial Materials for Novel Wave Phenomena (Metamaterials). IEEE, 2019. http://dx.doi.org/10.1109/metamaterials.2019.8900828.
Full textSimozo, Fabricio, Marcos Oliveira, and Luiz Murta-Junior. "Brain Tissue Classification to Detect Focal Cortical Dysplasia in Magnetic Resonance Imaging." In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/eniac.2020.12164.
Full text"Application of Self-organizing Maps in Functional Magnetic Resonance Imaging." In 6th International Workshop on Artificial Neural Networks and Intelligent Information Processing. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0002951300720080.
Full textChen, Xingyu, and Yusen Zhang. "Magnetic resonance imaging of adolescent depression based on machine vision." In ISAIMS 2021: 2nd International Symposium on Artificial Intelligence for Medicine Sciences. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3500931.3500996.
Full textUcuzal, Hasan, Ahmet K. Arslan, and Cemil Colak. "Deep learning based-classification of dementia in magnetic resonance imaging scans." In 2019 International Artificial Intelligence and Data Processing Symposium (IDAP). IEEE, 2019. http://dx.doi.org/10.1109/idap.2019.8875961.
Full textYang, Tingzhao, Kenneth Lee Ford, Madhwesha Rao, and James Wild. "A Single Unit Cell Metasurface for Magnetic Resonance Imaging Applications." In 2018 12th International Congress on Artificial Materials for Novel Wave Phenomena (Metamaterials). IEEE, 2018. http://dx.doi.org/10.1109/metamaterials.2018.8534181.
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