Artigos de revistas sobre o tema "Images PET"
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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 fonteODERO, D. O., J. R. HARTLEY e D. S. SHIMM. "POSITRON EMISSION TOMOGRAPHY AND RADIATION THERAPY COMPUTERIZED TREATMENT PLANNING SYSTEMS". Journal of Mechanics in Medicine and Biology 08, n.º 02 (junho de 2008): 235–50. http://dx.doi.org/10.1142/s0219519408002619.
Texto completo da fonteFriston, Karl J., Christopher D. Frith, Peter F. Liddle e Richard S. J. Frackowiak. "Plastic Transformation of PET Images". Journal of Computer Assisted Tomography 15, n.º 4 (julho de 1991): 634–39. http://dx.doi.org/10.1097/00004728-199107000-00020.
Texto completo da fonteVizza, Patrizia, Pierangelo Veltri e Giuseppe L. Cascini. "Statistical analysis of PET images". ACM SIGHIT Record 2, n.º 1 (março de 2012): 15. http://dx.doi.org/10.1145/2180796.2180807.
Texto completo da fonteTsotsos, John K. "Computation, PET images, and attention". Behavioral and Brain Sciences 18, n.º 2 (junho de 1995): 372. http://dx.doi.org/10.1017/s0140525x00038978.
Texto completo da fonteKoyama, Masamichi, e Mitsuru Koizumi. "FDG-PET Images of Acrometastases". Clinical Nuclear Medicine 39, n.º 3 (março de 2014): 298–300. http://dx.doi.org/10.1097/rlu.0000000000000350.
Texto completo da fonteÜstündağ, D. "Recovering Images from PET Camera". Acta Physica Polonica A 132, n.º 3-II (setembro de 2017): 963–66. http://dx.doi.org/10.12693/aphyspola.132.963.
Texto completo da fonteXiaolong Ouyang, W. H. Wong, V. E. Johnson, Xiaoping Hu e Chin-Tu Chen. "Incorporation of correlated structural images in PET image reconstruction". IEEE Transactions on Medical Imaging 13, n.º 4 (1994): 627–40. http://dx.doi.org/10.1109/42.363105.
Texto completo da fonteLai, Yung-Chi, Kuo-Chen Wu, Chao-Jen Chang, Yi-Jin Chen, Kuan-Pin Wang, Long-Bin Jeng e Chia-Hung Kao. "Predicting Overall Survival with Deep Learning from 18F-FDG PET-CT Images in Patients with Hepatocellular Carcinoma before Liver Transplantation". Diagnostics 13, n.º 5 (4 de março de 2023): 981. http://dx.doi.org/10.3390/diagnostics13050981.
Texto completo da fonteWongsa, Paramest, Witaya Sungkarat e Supattana Auethavekiat. "Developing a PET normal brain template using diffusion tensor imaging images: A proof of concept". Journal of Associated Medical Sciences 56, n.º 1 (3 de janeiro de 2023): 159–65. http://dx.doi.org/10.12982/jams.2023.019.
Texto completo da fonteWongsa, Paramest, Witaya Sungkarat e Supattana Auethavekiat. "Developing a PET normal brain template using diffusion tensor imaging images: A proof of concept". Journal of Associated Medical Sciences 56, n.º 1 (4 de janeiro de 2023): 159–66. http://dx.doi.org/10.12982/jams.2023.031.
Texto completo da fonteHaneishi, Hideaki, Masayuki Kanai, Yoshitaka Tamai, Atsushi Sakohira e Kazuyoshi Suga. "Registration and Summation of Respiratory-Gated or Breath-Hold PET Images Based on Deformation Estimation of Lung from CT Image". Computational and Mathematical Methods in Medicine 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/9713280.
Texto completo da fonteRossi, Farli, e Ashrani Aizzuddin Abd Rahni. "Joint Segmentation Methods of Tumor Delineation in PET – CT Images: A Review". International Journal of Engineering & Technology 7, n.º 3.32 (26 de agosto de 2018): 137. http://dx.doi.org/10.14419/ijet.v7i3.32.18414.
Texto completo da fonteBagci, Ulas, Jayaram K. Udupa, Neil Mendhiratta, Brent Foster, Ziyue Xu, Jianhua Yao, Xinjian Chen e Daniel J. Mollura. "Joint segmentation of anatomical and functional images: Applications in quantification of lesions from PET, PET-CT, MRI-PET, and MRI-PET-CT images". Medical Image Analysis 17, n.º 8 (dezembro de 2013): 929–45. http://dx.doi.org/10.1016/j.media.2013.05.004.
Texto completo da fonteGhezzo, Samuele, Ilaria Neri, Paola Mapelli, Annarita Savi, Ana Maria Samanes Gajate, Giorgio Brembilla, Carolina Bezzi et al. "[68Ga]Ga-PSMA and [68Ga]Ga-RM2 PET/MRI vs. Histopathological Images in Prostate Cancer: A New Workflow for Spatial Co-Registration". Bioengineering 10, n.º 8 (11 de agosto de 2023): 953. http://dx.doi.org/10.3390/bioengineering10080953.
Texto completo da fonteWang, Rui, Jifeng Zhang, Dongxue Wang, Funing Yang e Ping Li. "Clinical value of 18F-fluorodeoxyglucose positron emission tomography/computed tomography combined with computed tomography angiography in large-vessel vasculitis". Radiology of Infectious Diseases 10, n.º 4 (dezembro de 2023): 148–59. http://dx.doi.org/10.4103/rid.rid-d-23-00009.
Texto completo da fonteWisenberg, G., J. D. Thiessen, W. Pavlovsky, J. Butler, B. Wilk e F. S. Prato. "Same day comparison of PET/CT and PET/MR in patients with cardiac sarcoidosis". Journal of Nuclear Cardiology 27, n.º 6 (2 de janeiro de 2019): 2118–29. http://dx.doi.org/10.1007/s12350-018-01578-8.
Texto completo da fonteLee, Min-Hee, Chang-Soo Yun, Kyuseok Kim e Youngjin Lee. "Effect of Denoising and Deblurring 18F-Fluorodeoxyglucose Positron Emission Tomography Images on a Deep Learning Model’s Classification Performance for Alzheimer’s Disease". Metabolites 12, n.º 3 (7 de março de 2022): 231. http://dx.doi.org/10.3390/metabo12030231.
Texto completo da fonteHu, Zhanli, Yongchang Li, Sijuan Zou, Hengzhi Xue, Ziru Sang, Xin Liu, Yongfeng Yang, Xiaohua Zhu, Dong Liang e Hairong Zheng. "Obtaining PET/CT images from non-attenuation corrected PET images in a single PET system using Wasserstein generative adversarial networks". Physics in Medicine & Biology 65, n.º 21 (3 de novembro de 2020): 215010. http://dx.doi.org/10.1088/1361-6560/aba5e9.
Texto completo da fontePang, Wenbo, Siqi Li, Huiyan Jiang e Yu-dong Yao. "MTR-PET: Multi-temporal resolution PET images for lymphoma segmentation". Biomedical Signal Processing and Control 87 (janeiro de 2024): 105529. http://dx.doi.org/10.1016/j.bspc.2023.105529.
Texto completo da fonteWang, Ning, Lingjie Wang, Yixing Yu, Guangzheng Li, Changhao Cao, Rui Xu, Bin Jiang et al. "An Assessment of the Pathological Classification and Postoperative Outcome of Focal Cortical Dysplasia by Simultaneous Hybrid PET/MRI". Brain Sciences 13, n.º 4 (4 de abril de 2023): 611. http://dx.doi.org/10.3390/brainsci13040611.
Texto completo da fonteFerrando, Ornella, Franca Foppiano, Tindaro Scolaro, Chiara Gaeta e Andrea Ciarmiello. "PET/CT images quantification for diagnostics and radiotherapy applications". Journal of Diagnostic Imaging in Therapy 2, n.º 1 (16 de fevereiro de 2015): 18–29. http://dx.doi.org/10.17229/jdit.2015-0216-013.
Texto completo da fonteWei, Qi, e Qi Liu. "Denoise PET Images Based on a Combining Method of EMD and ICA". Advanced Materials Research 981 (julho de 2014): 340–43. http://dx.doi.org/10.4028/www.scientific.net/amr.981.340.
Texto completo da fonteDawood, M., N. Lang, F. Büther, M. Schäfers, O. Schober e K. P. Schäfers. "Motion correction in PET/CT". Nuklearmedizin 44, S 01 (2005): S46—S50. http://dx.doi.org/10.1055/s-0038-1625215.
Texto completo da fonteKang, Seung-Kwan, Si-Young Yie e Jae-Sung Lee. "Noise2Noise Improved by Trainable Wavelet Coefficients for PET Denoising". Electronics 10, n.º 13 (24 de junho de 2021): 1529. http://dx.doi.org/10.3390/electronics10131529.
Texto completo da fonteFilipovic, Marina, Eric Barat, Thomas Dautremer, Claude Comtat e Simon Stute. "PET Reconstruction of the Posterior Image Probability, Including Multimodal Images". IEEE Transactions on Medical Imaging 38, n.º 7 (julho de 2019): 1643–54. http://dx.doi.org/10.1109/tmi.2018.2886050.
Texto completo da fonteFarquhar, T. H., G. Chinn, C. K. Hoh, S. C. Huang e E. J. Hoffman. "A nonlinear, image domain filtering method for cardiac PET images". IEEE Transactions on Nuclear Science 45, n.º 4 (1998): 2073–79. http://dx.doi.org/10.1109/23.708300.
Texto completo da fonteKapur, Narinder. "Looking for images of memory". Behavioral and Brain Sciences 18, n.º 2 (junho de 1995): 364–65. http://dx.doi.org/10.1017/s0140525x00038887.
Texto completo da fonteXiang, Z. "PET/CT fusion in radiotherapy treatment planning for head and neck cancer". Journal of Clinical Oncology 27, n.º 15_suppl (20 de maio de 2009): e17046-e17046. http://dx.doi.org/10.1200/jco.2009.27.15_suppl.e17046.
Texto completo da fonteLe, Quoc Cuong, Hidetaka Arimura, Kenta Ninomiya, Takumi Kodama e Tetsuhiro Moriyama. "Can Persistent Homology Features Capture More Intrinsic Information about Tumors from 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography Images of Head and Neck Cancer Patients?" Metabolites 12, n.º 10 (14 de outubro de 2022): 972. http://dx.doi.org/10.3390/metabo12100972.
Texto completo da fonteSeitz, R. J., C. Bohm, T. Greitz, P. E. Roland, L. Eriksson, G. Blomqvist, G. Rosenqvist e B. Nordell. "Accuracy and Precision of the Computerized Brain Atlas Programme for Localization and Quantification in Positron Emission Tomography". Journal of Cerebral Blood Flow & Metabolism 10, n.º 4 (julho de 1990): 443–57. http://dx.doi.org/10.1038/jcbfm.1990.87.
Texto completo da fonteHuang, Xinrui, Yun Zhou, Shangliang Bao e Sung-Cheng Huang. "Clustering-Based Linear Least Square Fitting Method for Generation of Parametric Images in Dynamic FDG PET Studies". International Journal of Biomedical Imaging 2007 (2007): 1–8. http://dx.doi.org/10.1155/2007/65641.
Texto completo da fonteLindgren Belal, Sarah, May Sadik, Reza Kaboteh, Nezar Hasani, Olof Enqvist, Linus Svärm, Fredrik Kahl et al. "Association of PET index quantifying skeletal uptake in NaF PET/CT images with overall survival in prostate cancer patients." Journal of Clinical Oncology 35, n.º 6_suppl (20 de fevereiro de 2017): 178. http://dx.doi.org/10.1200/jco.2017.35.6_suppl.178.
Texto completo da fonteSong, Tzu-An, Fan Yang e Joyita Dutta. "Noise2Void: unsupervised denoising of PET images". Physics in Medicine & Biology 66, n.º 21 (1 de novembro de 2021): 214002. http://dx.doi.org/10.1088/1361-6560/ac30a0.
Texto completo da fonteVega-González, Ivan F., Ernesto Roldán-Valadez e Guillermo Valdiviezo-Cárdenas. "Fused PET/CT Images in Hepatocarcinoma". Annals of Hepatology 5, n.º 3 (julho de 2006): 164–65. http://dx.doi.org/10.1016/s1665-2681(19)32001-0.
Texto completo da fonteMykkänen, Jouni M., Martti Juhola e Ulla Ruotsalainen. "Extracting VOIs from brain PET images". International Journal of Medical Informatics 58-59 (setembro de 2000): 51–57. http://dx.doi.org/10.1016/s1386-5056(00)00075-7.
Texto completo da fonteMeyer, J. H., R. N. Gunn, R. Myers e P. M. Grasby. "Spatial Normalization of PET Ligand Images". NeuroImage 7, n.º 4 (maio de 1998): A27. http://dx.doi.org/10.1016/s1053-8119(18)31896-2.
Texto completo da fonteHsu, Chih-Yu, Chun-You Liu e Chung-Ming Chen. "Automatic segmentation of liver PET images". Computerized Medical Imaging and Graphics 32, n.º 7 (outubro de 2008): 601–10. http://dx.doi.org/10.1016/j.compmedimag.2008.07.001.
Texto completo da fonteGershon, N. D. "Visualization Blackboard-visualizing 3D PET images". IEEE Computer Graphics and Applications 11, n.º 5 (setembro de 1991): 11–13. http://dx.doi.org/10.1109/38.90562.
Texto completo da fonteJaakkola, Maria K., Maria Rantala, Anna Jalo, Teemu Saari, Jaakko Hentilä, Jatta S. Helin, Tuuli A. Nissinen et al. "Segmentation of Dynamic Total-Body [18F]-FDG PET Images Using Unsupervised Clustering". International Journal of Biomedical Imaging 2023 (5 de dezembro de 2023): 1–13. http://dx.doi.org/10.1155/2023/3819587.
Texto completo da fonteYe, Shiping, Chaoxiang Chen, Zhican Bai, Jinming Wang, Xiaoxaio Yao e Olga Nedzvedz. "Intelligent Labeling of Tumor Lesions Based on Positron Emission Tomography/Computed Tomography". Sensors 22, n.º 14 (10 de julho de 2022): 5171. http://dx.doi.org/10.3390/s22145171.
Texto completo da fonte