Academic literature on the topic '11C-PiB PET'
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Journal articles on the topic "11C-PiB PET"
Brown, Desmond, Gobinda Sarkar, Teresa Decklever, Geoffry Curran, Ameet Sarkar, Ann Schmeichel, Suresh Swaminathan, et al. "SCIDOT-39. K16ApoE ENHANCES Aβ-ASSOCIATED 11C-PiB DEPOSITION AND PET SIGNAL IN APP/PS1 TRANSGENIC MICE." Neuro-Oncology 21, Supplement_6 (November 2019): vi279—vi280. http://dx.doi.org/10.1093/neuonc/noz175.1175.
Full textFu, Liping, Linwen Liu, Jinming Zhang, Baixuan Xu, Yong Fan, and Jiahe Tian. "Brain Network Alterations in Alzheimer’s Disease Identified by Early-Phase PIB-PET." Contrast Media & Molecular Imaging 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/6830105.
Full textEngler, Henry, Andres Damian, and Cecilia Bentancourt. "PET and the multitracer concept in the study of neurodegenerative diseases." Dementia & Neuropsychologia 9, no. 4 (December 2015): 343–49. http://dx.doi.org/10.1590/1980-57642015dn94000343.
Full textVeronese, Mattia, Benedetta Bodini, Daniel García-Lorenzo, Marco Battaglini, Salvatore Bongarzone, Claude Comtat, Michel Bottlaender, Bruno Stankoff, and Federico E. Turkheimer. "Quantification of [11C]PIB PET for Imaging Myelin in the Human Brain: A Test—Retest Reproducibility Study in High-Resolution Research Tomography." Journal of Cerebral Blood Flow & Metabolism 35, no. 11 (June 10, 2015): 1771–82. http://dx.doi.org/10.1038/jcbfm.2015.120.
Full textKnezevic, Dunja, Nicolaas Paul LG Verhoeff, Sina Hafizi, Antonio P. Strafella, Ariel Graff-Guerrero, Tarek Rajji, Bruce G. Pollock, Sylvain Houle, Pablo M. Rusjan, and Romina Mizrahi. "Imaging microglial activation and amyloid burden in amnestic mild cognitive impairment." Journal of Cerebral Blood Flow & Metabolism 38, no. 11 (November 14, 2017): 1885–95. http://dx.doi.org/10.1177/0271678x17741395.
Full textRodda, J., A. Okello, P. Edison, T. Dannhauser, D. J. Brooks, and Z. Walker. "11C-PIB PET in subjective cognitive impairment." European Psychiatry 25, no. 2 (March 2010): 123–25. http://dx.doi.org/10.1016/j.eurpsy.2009.07.011.
Full textOkazawa, Hidehiko, Masamichi Ikawa, Tetsuya Tsujikawa, Akira Makino, Tetsuya Mori, Yasushi Kiyono, and Hirotaka Kosaka. "Noninvasive Measurement of [11C]PiB Distribution Volume Using Integrated PET/MRI." Diagnostics 10, no. 12 (November 24, 2020): 993. http://dx.doi.org/10.3390/diagnostics10120993.
Full textHosokawa, Chisa, Kazunari Ishii, Tomoko Hyodo, Kenta Sakaguchi, Kimio Usami, Kenji Shimamoto, Yuzuru Yamazoe, et al. "Investigation of 11C-PiB equivocal PET findings." Annals of Nuclear Medicine 29, no. 2 (November 6, 2014): 164–69. http://dx.doi.org/10.1007/s12149-014-0924-8.
Full textGrecchi, Elisabetta, Mattia Veronese, Benedetta Bodini, Daniel García-Lorenzo, Marco Battaglini, Bruno Stankoff, and Federico E. Turkheimer. "Multimodal partial volume correction: Application to [11C]PIB PET/MRI myelin imaging in multiple sclerosis." Journal of Cerebral Blood Flow & Metabolism 37, no. 12 (June 1, 2017): 3803–17. http://dx.doi.org/10.1177/0271678x17712183.
Full textBaron, Jean-Claude, Karim Farid, Eamon Dolan, Guillaume Turc, Siva T. Marrapu, Eoin O'Brien, Franklin I. Aigbirhio, et al. "Diagnostic Utility of Amyloid PET in Cerebral Amyloid Angiopathy-Related Symptomatic Intracerebral Hemorrhage." Journal of Cerebral Blood Flow & Metabolism 34, no. 5 (March 12, 2014): 753–58. http://dx.doi.org/10.1038/jcbfm.2014.43.
Full textDissertations / Theses on the topic "11C-PiB PET"
Manook, André [Verfasser], Markus [Akademischer Betreuer] Schwaiger, and Johann [Akademischer Betreuer] Förstl. "Preclinical PET as Translational Tool for Imaging Alzheimer's Disease : Small-Animal PET Imaging of Beta-Amyloid Plaques with [11C]PiB, its Multi-Modal Validation and Application to the Evaluation and Ranking of New AD Tracers / André Manook. Gutachter: Markus Schwaiger ; Johann Förstl. Betreuer: Markus Schwaiger." München : Universitätsbibliothek der TU München, 2012. http://d-nb.info/1047883465/34.
Full textReutern, Boris Gerhard Jaroslav von [Verfasser], Alexander [Akademischer Betreuer] Drzezga, Sibylle [Akademischer Betreuer] Ziegler, and Ambros [Akademischer Betreuer] Beer. "Relationship between in-vivo [11C]PiB PET Signal and Amyloid-β Plaque Pathology in different transgenic Mouse Models of Alzheimer's Disease / Boris Gerhard Jaroslav von Reutern. Gutachter: Alexander Drzezga ; Sibylle Ziegler ; Ambros Beer. Betreuer: Alexander Drzezga." München : Universitätsbibliothek der TU München, 2012. http://d-nb.info/103107516X/34.
Full textBERTI, VALENTINA. "Neuroimaging studies in subjects at genetic risk of developing Alzheimer's disease: the role of neuroimaging to reveal the endophenotype." Doctoral thesis, 2013. http://hdl.handle.net/2158/794649.
Full textBarroca, Dalila. "Estudo combinado de PET com [11C]PiB e [18F]FDG na avaliação de doença de Alzheimer." Master's thesis, 2014. http://hdl.handle.net/10400.26/14488.
Full textRodrigues, Iolanda Beatriz Albuquerque. "Synthesis Optimisation of High Specific Activity Carbon-11 Radiopharmaceuticals for Brain PET Studies." Master's thesis, 2020. http://hdl.handle.net/10316/90183.
Full textA radioquímica e a engenharia aplicada à síntese são as fundações da produção radiofarmacêutica e o seu posterior uso em técnicas de diagnóstico por imagem como a tomografia por emissão de positrões. A introdução do [11C]iodeto de metilo e do [11C]metil triflato como moléculas marcadoras de compostos biologicamente ativos impulsionou não só o desenvolvimento de radiofármacos marcados com carbono-11 como também de soluções automatizadas para a produção destes agentes de radiomarcação.Neste trabalho, estudámos a produção automatizada de [11C]metil triflato, partindo de [11C]CO2 produzido em ciclotrão através da reação nuclear 14N(p,α)11C, através de duas vias de síntese: o tão chamado método de fase líquida e a processo em fase gasosa. A partir deste precursor radioativo, radiofármacos no estado da arte como [11C]PiB e [11C]β-CITFE podem ser produzidos, usando o método de captura de solvente, para o estudo de doenças neurodegenerativas como Alzheimer e Parkinson. Após purificação por cromatografia líquida de alta performance, estes produtos foram reformulados e esterilizados para se obter uma solução injetável pronta para uso humano, após apropriados testes de controlo de qualidade. Foi nosso objetivo avaliar qual das técnicas de síntese era mais apropriada num contexto de rotina ativa bem como otimizar o método de fase gasosa e estabelecer procedimentos de manutenção de modo a ter um equipamento funcional e fiável.Foram obtidas atividades molares de 128.65 ± 56.73 GBq/µmol e 93.39 ± 48.83 GBq/µmol e atividades de 81.24 ± 29.83 mCi e 90.66 ± 41.47 mCi no fim de síntese para [11C]PiB e [11C]β-CITFE, respetivamente. As altas atividades molares alcançadas tornam a fase gasosa a técnica de eleição para a síntese de para [11C]PiB e [11C]β-CITFE, sendo que estes resultados devem ser aplicados a outros radiofármacos marcados com carbono-11. Além disso, este processo provou ser-se muito adequado numa ativa rotina de produção pela possibilidade da realização de várias sínteses sucessivas por dia, seguido uma simples rotina diária de manutenção. As manutenções preventivas adotadas refletiram-se num processo altamente reprodutível e fiável com uma taxa de sucesso de sínteses muito elevada (aproximadamente 100%).
Radiochemistry and engineering applied to synthesis are the foundations of radiopharmaceutical production and its further use in diagnostic imaging techniques such as positron emission tomography. The introduction of [11C]methyl iodide and [11C]methyl triflate as labelling molecules of biologically active compounds motivated not only the development of carbon-11 labelled radiopharmaceuticals but also of automated solutions for the synthesis of these radiolabelling agents.In this work, we studied the automated production of [11C]methyl triflate, starting from [11C]CO2 produced in a cyclotron by the nuclear reaction 14N(p,α)11C, by two different synthetic routes: the so-called “wet” method and the gas phase approach. From this radioactive precursor, state-of-the-art radiopharmaceuticals such as [11C]PiB and [11C]β-CITFE can be produced, using the captive solvent method, to study important neurodegenerative diseases such as Alzheimer’s and Parkinson’s. After purification using high performance liquid chromatography, these products were reformulated and sterilized to obtain an injectable solution ready for human use after the appropriate quality control tests. It was our goal to evaluate which of the synthesis techniques was more suitable in the routine context as well as to optimise the gas phase approach and establish maintenance procedures in order to have a functional and reliable equipment.Molar activities of 128.65 ± 56.73 GBq/µmol and 93.39 ± 48.83 GBq/µmol, and 81.24 ± 29.83 mCi and 90.66 ± 41.47 mCi activities were obtained in the end of synthesis for [11C]PiB and [11C]β-CITFE, respectively. High molar activities attained make the gas phase the technique of choice for [11C]PiB and [11C]β-CITFE synthesis and these results should be applicable to other 11C-radiopharmaceuticals. Additionally, this process proved to be very suitable in a busy production schedule due to the possibility of performing multiple synthesis per day following a very simple daily maintenance routine. The established preventive maintenance procedures have resulted in a highly reproducible and reliable process with a very high synthesis success rate (approximately 100%).
Pais, Marta Silva Lapo. "Machine Learning Classification in Alzheimer’s disease based on 11C-Pittsburgh Compound B (PiB) and 11C-(R)-PK11195 (PK) PET imaging measures and the correlation between these two biomarkers." Master's thesis, 2019. http://hdl.handle.net/10316/87968.
Full textA doença de Alzheimer (AD) é a doença neurodegenerativa responsável pelo maior número de casos de demência. Tomografia por emissão de positrões (PET) com 11C-Pittsburgh Compound B (PiB) e 11C-(R)-PK11195 (PK) são duas modalidades utilizadas na visualização das placas amilóides e da microglia ativada no cérebro humano, respetivamente. Uma vez que as placas amilóides são o principal identificador da AD e que a microglia ativada é também recorrentemente encontrada no cérebro dos doentes de Alzheimer, estes representam dois potenciais biomarcadores imagiológicos que podem ser usados como ferramenta de diagnóstico precoce da doença. Este trabalho teve como objetivo principal a resolução de um problema de classificação binário, entre controlos saudáveis (HC) e pacientes de Alzheimer, através de métodos de machine learning (ML) baseados em dois traçadores imagiológicos de PET: o PiB e o PK. Outro objetivo deste trabalho, incluiu a identificação das regiões cerebrais onde o PiB e o PK apresentam maior correlação, quer a nível do voxel quer a nível regional. O dataset deste estudo, que incluiu 41 indivíduos (20 doentes de Alzheimer e 21 HC), foi dividido em três grupos por forma a melhor compreender o impacto do intervalo de tempo considerado no protocolo de aquisição da PET. O grupo TOT, composto pelas imagens PET adquiridas durante o tempo total de biodistribuição do PiB, e os grupos 4070 e 4060, compostos por imagens PET adquiridas durante o intervalo de tempo caraterístico de acumulação de cada um destes radiofármacos. Após quantificação, pré-processamento, extração e seleção das features, as features selecionadas das imagens PET, com PiB e com PK, foram utilizadas como variáveis preditoras em classificadores baseados em support vector machines (SVM). Para estudar o impacto das diferentes regiões de referência utilizadas na normalização de imagens PET com PK, e a influência do método de quantificação escolhido, os grupos de AD e HC de diferentes formas de quantificação de imagens PET com PK foram comparados a nível do voxel. Adicionalmente, calculou-se para diferentes regiões cerebrais a correlação existente entre imagens PET com PiB em termos da taxa do valor de captação padronizado (SUVr) e as imagens PET com diferentes formas de quantificação PK.O classificador com o melhor desempenho foi construído com features extraídas de imagens PET com PiB do grupo 4070 normalizadas pelo cerebelo (exatidão – 0.925, sensibilidade-1.000, especificidade-0.857). Por conseguinte, para imagens PET com PiB, o cerebelo foi a região cerebral onde a diferença na acumulação de amilóides entre os grupos de AD e HC foi a menos significativa, isto é, foi a melhor região de referência. De referir que quando o cerebelo é utilizado como região de referência em imagens PET com PiB, é verificada uma maior correlação a nível regional para com as imagens PET com PK, comparativamente à normalização realizada através da matéria branca. As features extraídas a nível regional de imagens PET com PK não melhoraram nem a exatidão nem a sensibilidade do classificador apenas baseado em features extraídas de imagens PET com PiB. A correlação a nível regional entre imagens PET com PiB e com PK sugere que o cerebelo apresenta uma ligação específica ao PK; consequentemente, o método supervised cluster analysis algorithm based on four kinetic classes (SVCA4) relevou ser a melhor abordagem para a normalização de imagens PET com PK. As duas formas de quantificação de imagens PET com PK apresentaram diferenças muito pouco significativas entre os grupos AD e HC a nível do voxel, o que sugere que a biodistribuição do PK no cérebro não permite diferenciar grupos. Esta afirmação apoia a associação que se tem vindo a estabelecer entre a microglia ativada e a neuroinflamação. Como a neuroinflamação é característica de cada indivíduo, isto é, é aleatoriamente distribuída no cérebro dos doentes de Alzheimer, o esperado é a não diferenciação de grupos por parte do PK. Foram encontradas cinco regiões cerebrais onde a correlação a nível do voxel se relevou elevada para quase todas as regiões de referência consideradas, córtex motor primário, córtex visual primário, córtex de associação somatossensorial, córtex visual associativo e córtex pré-motor. Tanto o precuneus (P) como o lóbulo parietal inferior (PI) desempenham funções importantes no processamento visual e espacial. Por conseguinte, é natural que os resultados da correlação a nível regional estejam associados com os obtidos a nível do voxel.Em suma, de acordo com o estudo realizado, o classificador construído apenas com features extraídas de imagens PET com PiB do grupo 4070, usando o cerebelo como região de referência, foi o classificador que apresentou uma melhor resposta ao problema proposto, classificação binária de indivíduos como AD ou HC. Adicionalmente, também foi descoberta uma correlação positiva entre o PK e o PiB em regiões cerebrais responsáveis pela função motora e pelo processamento visual.
Alzheimer’s disease (AD) is one of the main neurodegenerative disorders causing dementia. Positron emission tomography (PET) neuroimaging with 11C-Pittsburgh Compound B (PiB) and 11C-(R)-PK11195 (PK) are two of the existing modalities to assess amyloid plaque and activated microglia in human brain, respectively. Since amyloid plaque is the main hallmark of AD and activated microglia is currently found in the brain of AD patients, these imaging biomarkers can be used in diagnostic workup and to achieve early AD diagnosis.The main goal of the present study is to solve a binary classification problem between healthy controls (HC) and AD patients, by using machine learning (ML) methods based on two PET imaging biomarkers, PiB and PK. Another important goal of this work includes the identification of the brain regions where PiB and PK are most correlated, at both regional and voxel level.In the present study it was included 41 subjects (20 AD and 21 HC). To understand the impact of the time interval considered in PET image acquisition, the dataset was split in three different groups. Group TOT composed by PiB PET images acquired during the total time of PiB biodistribution, and groups 4070 and 4060, acquired during the characteristic accumulation time of PiB and PK, between minute 40 and 70, and 40 and 60, after administration, respectively. After quantification, pre-processing, feature extraction and selection, PiB and PK PET images were submitted to classification using a support vector machines (SVM) approach. Voxel-wise comparison between AD and HC groups of different quantified PK PET images were performed to understand the impact of distinct reference regions in the normalization of PK PET images and the influence of the quantification method used. Also, voxel-wise and region of interest (ROI) based correlation between standard uptake value ratio (SUVr) PiB and different quantified PK PET images were calculated.Normalization by cerebellum of PiB PET images of group 4070 yielded the best classification accuracy of AD (accuracy-0.925, sensitivity-1.000, specificity-0.857). Thus, for PiB PET images, cerebellum appears to be the brain region where amyloid accumulation bears the least significant differences between HC and AD patients, i.e., the best reference region to do the normalization. Also, when using the cerebellum as reference region of PiB PET images, stronger ROI-based correlation with binding potential (BP) PK PET images in several brain regions was found, compared to normalization based on white matter. Features extracted at regional level from PK PET images did not show improvement, neither in accuracy nor in sensitivity, of the classifier only based on features extracted from PiB PET images. ROI-based correlation results suggest specific binding of PK to cerebellum; thus, supervised cluster analysis algorithm based on four kinetic classes (SVCA4) showed to be the best approach to do the normalization of PK PET images. Both types of quantified PK PET images did not show significant differences between groups at voxel level. This suggests that PK biodistribution in the brain is not relevant for group differentiation. The reason why is probably related to the fact that activated microglia is associated with neuroinflammation, and this process is quite variable across participants, i.e., it is randomly distributed across brains of AD patients. There were five brain regions where the correlation at voxel level between PK and SUVr PiB PET images agreed the most for all reference regions considered, primary motor cortex, primary visual cortex, somatosensory association cortex, associative visual cortex and premotor cortex. Since, both precuneus (P) and parietal inferior (PI) have important roles in visuospatial processing, ROI-based correlation results are consistent with the ones obtained at voxel level.Overall, according with the present study, the classifier only based on features extracted from PiB PET images of group 4070, using cerebellum as reference region, was the classifier who solved more accurately the problem proposed, binary classification in AD. Additionally it was also found a positive correlation between PK and PiB in particular in brain regions responsible for motor function and visual processing.
FCT
Tang, Yu-Ning, and 唐于甯. "Longitudinal coupled-imaging using [11C]PIB and [18F]FDG PET on Alzheimer's disease mouse model:correlated with cognitive function and histopathology." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/73943605061747154244.
Full text國立陽明大學
生物醫學影像暨放射科學系
101
Objectives: Alzheimer’ disease (AD) is typified by deposition of β-amyloid (Aβ) within the brain accompany with cognitive decline. Imaging Aβ plaque could be useful for the development of the therapeutic and diagnosis target in AD. In this study, we aimed to access the change of Aβ and neuronal metabolism in the brain in J20 AD mice over age by using [11C]PIB and [18F]FDG PET. Methods: The human amyloid precursor protein (hAPP) transgenic mouse lines J20 were bred and reared in our colony; littermates without hAPP were used as wild-type (wt) mice. The behavior test was applied to evaluate the change of memory and learning in APP mice. The animals (N=6/group) were imaged monthly from month 4 to month 12. The 20 min static imaging were performed after i.v. injection of [11C]PIB or [18F]FDG at 30 min. Thereafter, the brains of animals were removed for immunehistochemical staining for Aβ. Results: The memory and learning performance was significantly deficit in APP mice after 5-6 months. The accumulation of [11C]PIB tended higher in APP mice than the that of wt mice since month 5. The age-dependent increased accumulation of [11C]PIB in APP mice appeared from month 5 to month 12. No significant difference of [18F]FDG uptake was observed between APP and wt mice in whole study period. Conclusions: [11C]PIB PET could be used to distinguish APP mice from the control ones before onset at month 5 when pathological change of Aβ in hippocampus appears. Due to the limitation of micro PET imaging, the subtle change in the brain in earlier stage (
Book chapters on the topic "11C-PiB PET"
Ballinger, James R. "11C-PiB." In PET Radiopharmaceuticals, 120–21. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10271-4_53.
Full textFripp, Jurgen, Pierrick Bourgeat, Parnesh Raniga, Oscar Acosta, Victor Villemagne, Gareth Jones, Graeme O’keefe, Christopher Rowe, Sébastien Ourselin, and Olivier Salvado. "MR-Less High Dimensional Spatial Normalization of 11C PiB PET Images on a Population of Elderly, Mild Cognitive Impaired and Alzheimer Disease Patients." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008, 442–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85988-8_53.
Full textLee, Jun Ho, Min Soo Byun, Dahyun Yi, Kang Ko, So Yeon Jeon, Bo Kyung Sohn, Jun-Young Lee, Younghwa Lee, Haejung Joung, and Dong Young Lee. "Long-Term Exposure to PM10 and in vivo Alzheimer’s Disease Pathologies." In Advances in Alzheimer’s Disease. IOS Press, 2021. http://dx.doi.org/10.3233/aiad210012.
Full textConference papers on the topic "11C-PiB PET"
Socher, Karen, Douglas Nunes, Deborah Lopes, Artur Coutinho, Daniele Faria, Paula Squarzoni, Geraldo Busatto Filho, Carlos Buchpighel, Ricardo Nitrini,, and Sonia Brucki. "VISUAL MEDIAL TEMPORAL ATROPHY SCALES IN CLINICIAN PRACTICE." In XIII Meeting of Researchers on Alzheimer's Disease and Related Disorders. Zeppelini Editorial e Comunicação, 2021. http://dx.doi.org/10.5327/1980-5764.rpda102.
Full textMiotto, Eliane. "BRAIN ACTIVITY AND CONNECTIVITY IN 11C-PIB PET MCI AND HEALTHY ELDERLY INDIVIDUALS AFTER COGNITIVE TRAINING." In XIII Meeting of Researchers on Alzheimer's Disease and Related Disorders. Zeppelini Editorial e Comunicação, 2021. http://dx.doi.org/10.5327/1980-5764.rpda008.
Full textWu, Wenjun, Janani Venugopalan, and May D. Wang. "11C-PIB PET image analysis for Alzheimer's diagnosis using weighted voting ensembles." In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2017. http://dx.doi.org/10.1109/embc.2017.8037712.
Full textParmera, Jacy, Artur Coutinho, Isabel Almeida, Camila Carneiro, Carla Ono, Adalberto Studart-Neto, Egberto Barbosa, Carlos Buchpiguel, Ricardo Nitrini, and Sonia Brucki. "CORTICOBASAL SYNDROME: A PROSPECTIVE STUDY OF CLINICAL PROFILES AND IMAGING BIOMARKERS." In XIII Meeting of Researchers on Alzheimer's Disease and Related Disorders. Zeppelini Editorial e Comunicação, 2021. http://dx.doi.org/10.5327/1980-5764.rpda010.
Full textSun, Tao. "Peak-clearance-rate as index for detection of Alzheimer’s disease using 11C-PiB PET imaging." In Biomedical Applications in Molecular, Structural, and Functional Imaging, edited by Barjor S. Gimi and Andrzej Krol. SPIE, 2021. http://dx.doi.org/10.1117/12.2580991.
Full textJiang, Jiehui, Xinghui Shu, Xin Liu, and Zhemin Huang. "A Computed Aided Diagnosis tool for Alzheimer's disease based on 11C-PiB PET imaging technique." In 2015 IEEE International Conference on Information and Automation (ICIA). IEEE, 2015. http://dx.doi.org/10.1109/icinfa.2015.7279610.
Full textEl-Gamal, Fatma El-Zahraa A., Mohammed M. Elmogy, Ahmed Atwan, Mohammed Ghazal, Gregory N. Barnes, Hassan Hajjdiab, Robert Keynton, and Ayman S. El-Baz. "Significant Region-Based Framework for Early Diagnosis of Alzheimer's Disease Using 11C PiB-PET Scans." In 2018 24th International Conference on Pattern Recognition (ICPR). IEEE, 2018. http://dx.doi.org/10.1109/icpr.2018.8545196.
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