Literatura científica selecionada sobre o tema "Images PET"
Crie uma referência precisa em APA, MLA, Chicago, Harvard, e outros estilos
Índice
Consulte a lista de atuais artigos, livros, teses, anais de congressos e outras fontes científicas relevantes para o tema "Images PET".
Ao lado de cada fonte na lista de referências, há um botão "Adicionar à bibliografia". Clique e geraremos automaticamente a citação bibliográfica do trabalho escolhido no estilo de citação de que você precisa: APA, MLA, Harvard, Chicago, Vancouver, etc.
Você também pode baixar o texto completo da publicação científica em formato .pdf e ler o resumo do trabalho online se estiver presente nos metadados.
Artigos de revistas sobre o assunto "Images PET"
Muraglia, Lorenzo, Francesco Mattana, Laura Lavinia Travaini, Gennaro Musi, Emilio Bertani, Giuseppe Renne, Eleonora Pisa et al. "First Live-Experience Session with PET/CT Specimen Imager: A Pilot Analysis in Prostate Cancer and Neuroendocrine Tumor". Biomedicines 11, n.º 2 (20 de fevereiro de 2023): 645. http://dx.doi.org/10.3390/biomedicines11020645.
Texto completo da fonteGershon, Nahum D. "Visualizing 3D PET Images". IEEE Computer Graphics and Applications 11, n.º 5 (setembro de 1991): 11–13. http://dx.doi.org/10.1109/mcg.1991.10040.
Texto completo da fonteJiang, Changhui, Xu Zhang, Na Zhang, Qiyang Zhang, Chao Zhou, Jianmin Yuan, Qiang He et al. "Synthesizing PET/MR (T1-weighted) images from non-attenuation-corrected PET images". Physics in Medicine & Biology 66, n.º 13 (24 de junho de 2021): 135006. http://dx.doi.org/10.1088/1361-6560/ac08b2.
Texto completo da fontePietrzyk, U., C. Knoess, S. Vollmar, K. Wienhard, L. Kracht, A. Bockisch, S. Maderwald, H. Kühl, M. Fitzek e T. Beyer. "Multi-modality imaging of uveal melanomas using combined PET/CT, high-resolution PET and MR imaging". Nuklearmedizin 47, n.º 02 (2008): 73–79. http://dx.doi.org/10.3413/nukmed-0125.
Texto completo da fonteSuganuma, Yuta, Atsushi Teramoto, Kuniaki Saito, Hiroshi Fujita, Yuki Suzuki, Noriyuki Tomiyama e Shoji Kido. "Hybrid Multiple-Organ Segmentation Method Using Multiple U-Nets in PET/CT Images". Applied Sciences 13, n.º 19 (27 de setembro de 2023): 10765. http://dx.doi.org/10.3390/app131910765.
Texto completo da fonteSeiffert, Alexander P., Adolfo Gómez-Grande, Alberto Villarejo-Galende, Marta González-Sánchez, Héctor Bueno, Enrique J. Gómez e Patricia Sánchez-González. "High Correlation of Static First-Minute-Frame (FMF) PET Imaging after 18F-Labeled Amyloid Tracer Injection with [18F]FDG PET Imaging". Sensors 21, n.º 15 (30 de julho de 2021): 5182. http://dx.doi.org/10.3390/s21155182.
Texto completo da fonteLee, Giljae, Hwunjae Lee e Gyehwan Jin. "Analysis of Fitting Degree of MRI and PET Images in Simultaneous MRPET Images by Machine Learning Neural Networks". ScholarGen Publishers 3, n.º 1 (28 de dezembro de 2020): 43–61. http://dx.doi.org/10.31916/sjmi2020-01-05.
Texto completo da fonteCouto, Pedro, Telmo Bento, Humberto Bustince e Pedro Melo-Pinto. "Positron Emission Tomography Image Segmentation Based on Atanassov’s Intuitionistic Fuzzy Sets". Applied Sciences 12, n.º 10 (11 de maio de 2022): 4865. http://dx.doi.org/10.3390/app12104865.
Texto completo da fonteLi, Hui, Chao Gao, Yingying Sun, Aojie Li, Wang Lei, Yuming Yang, Ting Guo et al. "Radiomics Analysis to Enhance Precise Identification of Epidermal Growth Factor Receptor Mutation Based on Positron Emission Tomography Images of Lung Cancer Patients". Journal of Biomedical Nanotechnology 17, n.º 4 (1 de abril de 2021): 691–702. http://dx.doi.org/10.1166/jbn.2021.3056.
Texto completo da fontePetryakova, A. V., L. A. Chipiga, M. S. Tlostanova, A. A. Ivanova, D. A. Vazhenina, A. A. Stanzhevsky, D. V. Ryzhkova et al. "Method of Experts’ Quality Evaluation of the PET Images of the Patients". MEDICAL RADIOLOGY AND RADIATION SAFETY 68, n.º 1 (fevereiro de 2023): 78–85. http://dx.doi.org/10.33266/1024-6177-2023-68-1-78-85.
Texto completo da fonteTeses / dissertações sobre o assunto "Images PET"
Cruz, Cavalcanti Yanna. "Factor analysis of dynamic PET images". Thesis, Toulouse, INPT, 2018. http://www.theses.fr/2018INPT0078/document.
Texto completo da fonteThanks to its ability to evaluate metabolic functions in tissues from the temporal evolution of a previously injected radiotracer, dynamic positron emission tomography (PET) has become an ubiquitous analysis tool to quantify biological processes. Several quantification techniques from the PET imaging literature require a previous estimation of global time-activity curves (TACs) (herein called \textit{factors}) representing the concentration of tracer in a reference tissue or blood over time. To this end, factor analysis has often appeared as an unsupervised learning solution for the extraction of factors and their respective fractions in each voxel. Inspired by the hyperspectral unmixing literature, this manuscript addresses two main drawbacks of general factor analysis techniques applied to dynamic PET. The first one is the assumption that the elementary response of each tissue to tracer distribution is spatially homogeneous. Even though this homogeneity assumption has proven its effectiveness in several factor analysis studies, it may not always provide a sufficient description of the underlying data, in particular when abnormalities are present. To tackle this limitation, the models herein proposed introduce an additional degree of freedom to the factors related to specific binding. To this end, a spatially-variant perturbation affects a nominal and common TAC representative of the high-uptake tissue. This variation is spatially indexed and constrained with a dictionary that is either previously learned or explicitly modelled with convolutional nonlinearities affecting non-specific binding tissues. The second drawback is related to the noise distribution in PET images. Even though the positron decay process can be described by a Poisson distribution, the actual noise in reconstructed PET images is not expected to be simply described by Poisson or Gaussian distributions. Therefore, we propose to consider a popular and quite general loss function, called the $\beta$-divergence, that is able to generalize conventional loss functions such as the least-square distance, Kullback-Leibler and Itakura-Saito divergences, respectively corresponding to Gaussian, Poisson and Gamma distributions. This loss function is applied to three factor analysis models in order to evaluate its impact on dynamic PET images with different reconstruction characteristics
Batty, Stephen. "Content based retrieval of PET neurological images". Thesis, Middlesex University, 2004. http://eprints.mdx.ac.uk/9770/.
Texto completo da fontePavarin, Alice. "Comparison of textural features in PET images: a phantom study". Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018.
Encontre o texto completo da fonteYu, Chin-Lung. "Methods for automated analysis of small-animal PET images". Diss., Restricted to subscribing institutions, 2008. http://proquest.umi.com/pqdweb?did=1580851181&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.
Texto completo da fonteRAPISARDA, EUGENIO. "Improvements in quality and quantification of 3D PET images". Doctoral thesis, Università degli Studi di Milano-Bicocca, 2012. http://hdl.handle.net/10281/28157.
Texto completo da fonteJonsson, Sofia. "Evaluation of Methods for Obtaining an Image Derived Input Function from Dynamic PET-images". Thesis, Umeå universitet, Institutionen för fysik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-124426.
Texto completo da fonteSims, John Andrew. "Directional analysis of cardiac left ventricular motion from PET images". Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/3/3142/tde-05092017-093020/.
Texto completo da fonteA quantificação do movimento cardíaco do ventrículo esquerdo (VE) a partir de imagens médicas fornece um método não invasivo para o diagnóstico de doenças cardiovasculares (DCV). O estudo aqui proposto continua na mesma linha de pesquisa do nosso grupo sobre quantificação do movimento do VE por meio de técnicas de fluxo óptico (FO), aplicando estes métodos para quantificar o movimento do VE em sequências de imagens associadas às substâncias de cloreto de rubídio-82Rb (82Rb) e fluorodeoxiglucose-18F (FDG) PET. Com a extração dos campos vetoriais surgiram os seguintes desafios: (i) o campo vetorial de movimento (motion vector field, MVF) deve ser feito da forma mais precisa possível para maximizar a sensibilidade e especificidade; (ii) o MVF é extenso e composto de vetores 3D no espaço 3D, dificultando a análise visual de informações por observadores humanos para o diagnóstico médico. Foram desenvolvidas abordagens para melhorar a precisão da quantificação de movimento, considerando que o volume de interesse seja a região do MVF correspondente ao miocárdio do VE, em que valores de movimento não nulos existem fora deste volume devido aos artefatos do método de detecção de movimento ou de estruturas vizinhas, como o ventrículo direito. As melhorias na precisão foram obtidas segmentando o VE e ajustando os valores de MVF para zero fora do VE. O miocárdio VE foi segmentado automaticamente em fatias de eixo curto usando a Transformada de Hough na detecção de círculos para fornecer uma inicialização ao algoritmo de curvas de nível, um tipo de modelo deformável. A segmentação automática do VE atingiu 93,43% de medida de similaridade Dice, quando foi testado em 395 fatias de eixo menor de FDG, comparado com a segmentação manual. Estratégias para melhorar o desempenho do algoritmo OF nas bordas de movimento foram investigadas usando spatially varying averaging filters, aplicados em seqüências de imagens sintéticas. Os resultados mostraram melhorias na precisão de quantificação de movimento utilizando estes métodos. O Índice de Energia Cinética (KEf), um indicador de motilidade cardíaca, foi utilizado para avaliar 63 sujeitos com função cardíaca normal e alterada / baixa de uma base de dados de imagens PET de 82Rb. Foram realizados testes de sensibilidade e especificidade para avaliar o potencial de KEf para classificar a função cardíaca, utilizando a fração de ejeção do VE como padrão ouro. Foi construída uma curva ROC, que proporcionou uma área sob a curva de 0,906. A análise do movimento do VE pode ser simplificada pela visualização de componentes de campo de movimento direcional, ou seja, radial, rotacional (ou circunferencial) e linear, obtidos por decomposição automatizada. A decomposição discreta de Helmholtz Hodge (DHHD) foi utilizada para gerar estes componentes de forma automatizada, com uma validação utilizando campos de movimento cardíaco sintéticos a partir do conjunto Extended Cardiac Torso Phantom. Finalmente, o método DHHD foi aplicado a campos de FO, criado a partir de imagens FDG, permitindo uma análise de componentes direcionais de um indivíduo com função cardíaca normal e um paciente com baixa função e utilizando um marca-passo. A quantificação do campo de movimento a partir de imagens PET possibilita o desenvolvimento de novos indicadores para diagnosticar DCVs. A capacidade destes indicadores de motilidade depende na precisão da quantificação de movimento que, por sua vez, pode ser determinado por características das imagens de entrada como ruído. A análise de movimento fornece um promissor e sem precedente método para o diagnóstico de DCVs.
Farinha, Ricardo Jorge Pires Correia. "Segmentation of striatal brain structures from high resolution pet images". Master's thesis, FCT - UNL, 2008. http://hdl.handle.net/10362/2036.
Texto completo da fonteWe propose and evaluate fully automatic segmentation methods for the extraction of striatal brain surfaces (caudate, putamen, ventral striatum and white matter), from high resolution positron emission tomography (PET) images. In the preprocessing steps, both the right and the left striata were segmented from the high resolution PET images. This segmentation was achieved by delineating the brain surface, finding the plane that maximizes the reflective symmetry of the brain (mid-sagittal plane) and, finally, extracting the right and left striata from both hemisphere images. The delineation of the brain surface and the extraction of the striata were achieved using the DSM-OS (Surface Minimization – Outer Surface) algorithm. The segmentation of striatal brain surfaces from the striatal images can be separated into two sub-processes: the construction of a graph (named “voxel affinity matrix”) and the graph clustering. The voxel affinity matrix was built using a set of image features that accurately informs the clustering method on the relationship between image voxels. The features defining the similarity of pairwise voxels were spatial connectivity, intensity values, and Euclidean distances. The clustering process is treated as a graph partition problem using two methods, a spectral (multiway normalized cuts) and a non-spectral (weighted kernel k-means). The normalized cuts algorithm relies on the computation of the graph eigenvalues to partition the graph into connected regions. However, this method fails when applied to high resolution PET images due to the high computational requirements arising from the image size. On the other hand, the weighted kernel k-means classifies iteratively, with the aid of the image features, a given data set into a predefined number of clusters. The weighted kernel k-means and the normalized cuts algorithm are mathematically similar. After finding the optimal initial parameters for the weighted kernel k-means for this type of images, no further tuning is necessary for subsequent images. Our results showed that the putamen and ventral striatum were accurately segmented, while the caudate and white matter appeared to be merged in the same cluster. The putamen was divided in anterior and posterior areas. All the experiments resulted in the same type of segmentation, validating the reproducibility of our results.
Bieth, Marie. "Kinetic analysis and inter-subject registration of brain PET images". Thesis, McGill University, 2013. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=119738.
Texto completo da fonteL'imagerie à émission de positrons est de plus en plus utilisée pour comprendre le fonctionnement du cerveau. Ce mémoire aborde deux sujets liés àces images: le calcul du potentiel de liaison et l'alignement de deux images. Nous étudions tout d'abord l'influence de certains choix d'implémentation sur les estimations de potentiel de liaison. Ces travaux effectués sur des données simulées nous permettent de donner des points de repère concernant les choix à faire pour calculer le potentiel de liaison, ce qui constitue un pas important vers un calcul du potentiel de liaison entièrement automatisé etindépendant d'images à résonance magnétique. Nous introduisons ensuite une nouvelle méthode pour l'alignement de deux images de tomographie à émission de positrons. Cette méthode est adaptée de l'algorithme des log-démons difféomorphiques 3D. Nous montrons que notre méthode donne de meilleurs résultats que des méthodes existantes. Nous présentons aussi un modèle de haute résolution pour l'imagerie à émissionde positrons utilisant la [11C]raclopride. Ce modèle est construit à partir de 35sujets scannés sur le tomographe de recherche à haute résolution (High Resolution Research Tomograph). Comme il s'agit du tomographe de plus haute résolution disponible à ce jour, à notre connaissance, notre modèle est l'image de raclopride de plus haute résolution jamais produite.
Wang, Jiali. "Motion Correction Algorithm of Lung Tumors for Respiratory Gated PET Images". FIU Digital Commons, 2009. http://digitalcommons.fiu.edu/etd/96.
Texto completo da fonteLivros sobre o assunto "Images PET"
Panetta, Daniele, e Niccoló Camarlinghi. 3D Image Reconstruction for CT and PET. Boca Raton : CRC Press, 2020. | Series: Focus series in medical physics and biomedical engineering: CRC Press, 2020. http://dx.doi.org/10.1201/9780429270239.
Texto completo da fonteWaroquier, Henry de. Henry de Waroquier, images de Bretagne. Paris: Somogy, 2000.
Encontre o texto completo da fonteSarsanedas, Jordi. Paraules per a unes imatges. Barcelona [Spain]: Publicacions de l'Abadia de Montserrat, 2004.
Encontre o texto completo da fonteRiera, Carme. Mallorca, imatges per la felicitat. Palma de Mallorca: Edicions de Turisme Cultural, Illes Balears, 2000.
Encontre o texto completo da fonteRuss, Nadia. How to draw NeoPopRealism ink images: Basics. New York: NeoPopRealism, 2011.
Encontre o texto completo da fonteMoretti, Giampiero. Per immagini: Esercizi di ermeneutica sensibile. Bergamo: Moretti & Vitali, 2012.
Encontre o texto completo da fonteGuttuso, Fabio Carapezza. Renato Guttuso: Biografia per immagini = biography through images. Troina: Città aperta edizioni, 2009.
Encontre o texto completo da fonteL'insieme vuoto: Per una pragmatica dell'immagine. Monza: Johan & Levi, 2013.
Encontre o texto completo da fonteFanti, Stefano, Mohsen Farsad e Luigi Mansi, eds. PET-CT Beyond FDG A Quick Guide to Image Interpretation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-93909-2.
Texto completo da fonteFanti, Stefano. PET-CT beyond FDG: A quick guide to image interpretation. Berlin: Springer, 2010.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Images PET"
Knorr, U., Y. Huang, G. Schlaug, R. J. Seitz e H. Steinmetz. "High Resolution PET Images through REDISTRIBUTION". In Computer Assisted Radiology / Computergestützte Radiologie, 517–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-49351-5_85.
Texto completo da fonteKang, Jiayin, Yaozong Gao, Yao Wu, Guangkai Ma, Feng Shi, Weili Lin e Dinggang Shen. "Prediction of Standard-Dose PET Image by Low-Dose PET and MRI Images". In Machine Learning in Medical Imaging, 280–88. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10581-9_35.
Texto completo da fonteRunge, Val M., e Johannes T. Heverhagen. "Integrated Whole-Body MR-PET". In The Physics of Clinical MR Taught Through Images, 300–303. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85413-3_137.
Texto completo da fonteBastiaens, Koen, Paul Desmedt e Ignace Lemahieu. "Parallel Maximum Entropy Reconstruction of PET Images". In Maximum Entropy and Bayesian Methods, 213–19. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-015-8729-7_17.
Texto completo da fonteHuang, Y., U. Knorr, H. Steinmetz e R. J. Seitz. "Accurate Alignment and Reslicing of PET Images". In Computer Assisted Radiology / Computergestützte Radiologie, 788. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-49351-5_163.
Texto completo da fontePlets, P., J. Nuyts, P. Dupont e P. Suetens. "Registration of PET-Images Using Template Matching". In Computer Assisted Radiology / Computergestützte Radiologie, 509–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-49351-5_84.
Texto completo da fonteHuang, Ling, Su Ruan, Pierre Decazes e Thierry Denœux. "Evidential Segmentation of 3D PET/CT Images". In Belief Functions: Theory and Applications, 159–67. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88601-1_16.
Texto completo da fonteOldan, Jorge Daniel. "Review of PET/CT Images in Melanoma and Sarcoma: False Positives, False Negatives, and Pitfalls". In PET/CT and PET/MR in Melanoma and Sarcoma, 107–29. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60429-5_5.
Texto completo da fonteCataldo, Sol A., Florencia Sarmiento Laspiur e Martín A. Belzunce. "Automated PET Quantification of [18F]FDG PET Images for Neurodegenerative Disorders Research". In IFMBE Proceedings, 395–403. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-61973-1_37.
Texto completo da fonteXu, Guofan. "18F-Fluoride Imaging: Atlas of Interesting Images (Images with Specific Teaching Points, Tracer, Technique, and Pitfalls)". In Sodium Fluoride PET/CT in Clinical Use, 61–68. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23577-2_8.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Images PET"
Davis, P. B., e M. A. Abidi. "Enhancement of PET Images". In 1989 Medical Imaging, editado por Samuel J. Dwyer III, R. Gilbert Jost e Roger H. Schneider. SPIE, 1989. http://dx.doi.org/10.1117/12.953301.
Texto completo da fonteSong, Tzu-An, e Joyita Dutta. "Noise2Void Denoising of PET Images". In 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). IEEE, 2020. http://dx.doi.org/10.1109/nss/mic42677.2020.9507875.
Texto completo da fonte"Image Segmentation Guidance using Pet Information on CT Images in PET/CT Dual Modality". In 2nd International Workshop on Medical Image Analysis and Description for Diagnosis Systems. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003302700750081.
Texto completo da fonteThielemans, Kris, Evren Asma, Ravindra M. Manjeshwar, Alex Ganin e Terence J. Spinks. "Image-based correction for mismatched attenuation in PET images". In 2008 IEEE Nuclear Science Symposium and Medical Imaging conference (2008 NSS/MIC). IEEE, 2008. http://dx.doi.org/10.1109/nssmic.2008.4774427.
Texto completo da fonteArabi, Hossein, e Habib Zaidi. "Multiple PET Reconstruction Assisted Non-local Mean Denoising of PET Images". In 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). IEEE, 2020. http://dx.doi.org/10.1109/nss/mic42677.2020.9507772.
Texto completo da fonteGrant, Alexander M., Brian J. Lee, Chen-Ming Chang e Craig S. Levin. "Simultaneous PET/MRI images acquired with an RF-transmissive PET insert". In 2014 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). IEEE, 2014. http://dx.doi.org/10.1109/nssmic.2014.7431007.
Texto completo da fonteGrant, Alexander M., Brian J. Lee, Chen-Ming Chang e Craig S. Levin. "Simultaneous PET/MR images acquired with an RF-penetrable PET insert". In 2015 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). IEEE, 2015. http://dx.doi.org/10.1109/nssmic.2015.7582026.
Texto completo da fonteDutta, Kaushik, Ziping Liu, Richard Laforest, Abhinav Jha e Kooresh I. Shoghi. "Deep learning framework to synthesize high-count preclinical PET images from low-count preclinical PET images". In Physics of Medical Imaging, editado por Wei Zhao e Lifeng Yu. SPIE, 2022. http://dx.doi.org/10.1117/12.2612729.
Texto completo da fonteRota Kops, Elena, e Hans Herzog. "Alternative methods for attenuation correction for PET images in MR-PET scanners". In 2007 IEEE Nuclear Science Symposium Conference Record. IEEE, 2007. http://dx.doi.org/10.1109/nssmic.2007.4437073.
Texto completo da fonteLemmens, Catherine, e Johan Nuyts. "Metals in PET/CT: Causes and reduction of artifacts in PET images". In 2008 IEEE Nuclear Science Symposium and Medical Imaging conference (2008 NSS/MIC). IEEE, 2008. http://dx.doi.org/10.1109/nssmic.2008.4774204.
Texto completo da fonteRelatórios de organizações sobre o assunto "Images PET"
FDG-PET/CT SUV for Response to Cancer Therapy, Clinically Feasible Profile. Chair Nathan Hall e Jeffrey Yap. Radiological Society of North America (RSNA) / Quantitative Imaging Biomarkers Alliance (QIBA), junho de 2023. http://dx.doi.org/10.1148/qiba/20230615.
Texto completo da fonteGil Benítez, Alejandro, e María Pascual Mora. LA TOMOGRAFÍA POR EMISIÓN DE POSITRONES (PET); FUNDAMENTOS, DESARROLLO Y APLICACIONES. Fundación Avanza, maio de 2024. http://dx.doi.org/10.60096/fundacionavanza/2322024.
Texto completo da fonteKaufeld, Kimberly, James Wendelberger e Elizabeth Kelly. Montage (stitched 20X images) Pit Assessment of FY16 DE05. Office of Scientific and Technical Information (OSTI), fevereiro de 2021. http://dx.doi.org/10.2172/1766986.
Texto completo da fonteAlhasan, Ahmad, Brian Moon, Doug Steele, Hyung Lee e Abu Sufian. Chip Seal Quality Assurance Using Percent Embedment. Illinois Center for Transportation, dezembro de 2023. http://dx.doi.org/10.36501/0197-9191/23-029.
Texto completo da fonteTao, Yang, Amos Mizrach, Victor Alchanatis, Nachshon Shamir e Tom Porter. Automated imaging broiler chicksexing for gender-specific and efficient production. United States Department of Agriculture, dezembro de 2014. http://dx.doi.org/10.32747/2014.7594391.bard.
Texto completo da fonteKenes, Bulent. Per Jimmie Åkesson: A Smiling Wolf in Sheep’s Clothing? European Center for Populism Studies (ECPS), agosto de 2020. http://dx.doi.org/10.55271/lp0002.
Texto completo da fonteTang, H., Ed X. Wu, D. Gallagher e S. B. Heymsfield. Monochrome Image Presentation and Segmentation Based on the Pseudo-Color and PCT Transformations. Fort Belvoir, VA: Defense Technical Information Center, outubro de 2001. http://dx.doi.org/10.21236/ada412412.
Texto completo da fonteKurdziel, Karen, Michael Hagan, Jeffrey Williamson, Donna McClish, Panos Fatouros, Jerry Hirsch, Rhonda Hoyle, Kristin Schmidt, Dorin Tudor e Jie Liu. Multimodality Image-Guided HDR/IMRT in Prostate Cancer: Combined Molecular Targeting Using Nanoparticle MR, 3D MRSI, and 11C Acetate PET Imaging. Fort Belvoir, VA: Defense Technical Information Center, agosto de 2005. http://dx.doi.org/10.21236/ada446542.
Texto completo da fontePiert, Morand. Parametric PET/MR Fusion Imaging to Differentiate Aggressive from Indolent Primary Prostate Cancer with Application for Image-Guided Prostate Cancer Biopsies. Fort Belvoir, VA: Defense Technical Information Center, outubro de 2014. http://dx.doi.org/10.21236/ada612753.
Texto completo da fontePiert, Morand. Parametric PET/MR Fusion Imaging to Differentiate Aggressive from Indolent Primary Prostate Cancer with Application for Image-Guided Prostate Cancer Biopsies. Fort Belvoir, VA: Defense Technical Information Center, outubro de 2013. http://dx.doi.org/10.21236/ada598223.
Texto completo da fonte