Literatura científica selecionada sobre o tema "PET imaging data"

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Artigos de revistas sobre o assunto "PET imaging data"

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Izquierdo-Garcia, David, and Ciprian Catana. "MR Imaging–Guided Attenuation Correction of PET Data in PET/MR Imaging." PET Clinics 11, no. 2 (2016): 129–49. http://dx.doi.org/10.1016/j.cpet.2015.10.002.

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Erlandsson, Kjell, John Dickson, Simon Arridge, David Atkinson, Sebastien Ourselin, and Brian F. Hutton. "MR Imaging–Guided Partial Volume Correction of PET Data in PET/MR Imaging." PET Clinics 11, no. 2 (2016): 161–77. http://dx.doi.org/10.1016/j.cpet.2015.09.002.

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Muzi, Mark, Finbarr O'Sullivan, David A. Mankoff, et al. "Quantitative assessment of dynamic PET imaging data in cancer imaging." Magnetic Resonance Imaging 30, no. 9 (2012): 1203–15. http://dx.doi.org/10.1016/j.mri.2012.05.008.

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Lassen, Martin Lyngby, Thomas Beyer, Alexander Berger, et al. "Data-driven, projection-based respiratory motion compensation of PET data for cardiac PET/CT and PET/MR imaging." Journal of Nuclear Cardiology 27, no. 6 (2019): 2216–30. http://dx.doi.org/10.1007/s12350-019-01613-2.

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Mazoyer, Bernard M., Ronald H. Huesman, Thomas F. Budinger, and Brian L. Knittel. "Dynamic PET Data Analysis." Journal of Computer Assisted Tomography 10, no. 4 (1986): 645–53. http://dx.doi.org/10.1097/00004728-198607000-00020.

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Guo, Hongbin, Rosemary Renaut, Kewei Chen, and Eric Reiman. "Clustering huge data sets for parametric PET imaging." Biosystems 71, no. 1-2 (2003): 81–92. http://dx.doi.org/10.1016/s0303-2647(03)00112-6.

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Thorwarth, Daniela, Linda-Jacqueline Wack, and David Mönnich. "Hypoxia PET imaging techniques: data acquisition and analysis." Clinical and Translational Imaging 5, no. 6 (2017): 489–96. http://dx.doi.org/10.1007/s40336-017-0250-y.

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Rapoport, Stanley I. "Discriminant Analysis of Brain Imaging Data Identifies Subjects With Early Alzheimer's Disease." International Psychogeriatrics 9, S1 (1997): 229–35. http://dx.doi.org/10.1017/s1041610297004936.

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In vivo functional brain imaging provides an opportunity to quantify and localize functional deficits associated with Alzheimer's disease (AD), in relation to dementia severity and heterogeneous cognitive profiles. Such imaging also provides a basis for distinguishing AD from other causes of dementia and for making an early diagnosis of disease. One imaging modality that can elucidate AD is positron emission tomography (PET), which is used to measure regional cerebral metabolic rates for glucose (rCMRglc) and regional cerebral blood flow (rCBF). Resting-state measurements with PET, when relate
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Kwee, Thomas C., Robert M. Kwee, and Rutger A. J. Nievelstein. "Imaging in staging of malignant lymphoma: a systematic review." Blood 111, no. 2 (2008): 504–16. http://dx.doi.org/10.1182/blood-2007-07-101899.

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Computed tomography (CT) is currently the most commonly used means for staging malignant lymphoma. 18F-fluoro-2-deoxyglucose positron emission tomography (FDG-PET), FDG-PET/CT fusion, and whole-body magnetic resonance imaging (WB-MRI) are potential alternatives. The purpose of this study was to systematically review published data on the diagnostic performance of CT, FDG-PET, FDG-PET/CT fusion, and WB-MRI in staging of malignant lymphoma. In addition, technical aspects, procedures, advantages, and drawbacks of each imaging modality are outlined. Three CT studies, 17 FDG-PET studies, and 4 FDG-
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Schuler, Markus K., Stephan Richter, Bettina Beuthien-Baumann, et al. "PET/MRI Imaging in High-Risk Sarcoma: First Findings and Solving Clinical Problems." Case Reports in Oncological Medicine 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/793927.

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Simultaneous positron emission tomography (PET) and magnetic resonance imaging (MRI) is a new whole-body hybrid PET/MR imaging technique that combines metabolic and cross-sectional diagnostic imaging. Since the use of MRI in imaging of soft-tissue sarcoma is extremely beneficial, investigation of the combined PET/MRI is of great interest. In this paper, we present three cases and first data. Combined PET/MRI technique can support the process of clinical decision-making and give answers to some meaningful questions when treating patients with STS. Therefore, the combined modality of simultaneou
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Teses / dissertações sobre o assunto "PET imaging data"

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ALCHERA, NICOLA. "Data harmonization in PET imaging." Doctoral thesis, Università degli studi di Genova, 2021. http://hdl.handle.net/11567/1049735.

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Medical imaging physics has advanced a lot in recent years, providing clinicians and researchers with increasingly detailed images that are well suited to be analyzed with a quantitative approach typical of hard sciences, based on measurements and analysis of clinical interest quantities extracted from images themselves. Such an approach is placed in the context of quantitative imaging. The possibility of sharing data quickly, the development of machine learning and data mining techniques, the increasing availability of computational power and digital data storage which characterize this age
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Williamitis, Joseph M. "Using fMRI BOLD Imaging to Motion-Correct Associated, Simultaneously Imaged PET Data." Wright State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=wright1620585748146734.

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Chen, Kevin Tze-Hsiang. "MR-assisted PET data optimization for simultaneous dual-modality imaging in dementia." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/111254.

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Thesis: Ph. D. in Medical Engineering and Medical Physics, Harvard-MIT Program in Health Sciences and Technology, 2017.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 147-155).<br>Recent advances have allowed the hardware integration of positron emission tomography (PET) and magnetic resonance imaging (MRI). The spatiotemporally correlated data acquisition opened up opportunities for numer
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Vandervoort, Eric. "Improving attenuation corrections obtained using singles-mode transmission data in small-animal PET." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/608.

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The images in positron emission tomography (PET) represent three dimensional dynamic distributions of biologically interesting molecules labelled with positron emitting radionuclides (radiotracers). Spatial localisation of the radio-tracers is achieved by detecting in coincidence two collinear photons which are emitted when the positron annihilates with an ordinary electron. In order to obtain quantitatively accurate images in PET, it is necessary to correct for the effects of photon attenuation within the subject being imaged. These corrections can be obtained using singles-mode photon transm
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Perrin, Rosalind Lucy. "The application of PET/CT imaging data to external beam radiotherapy planning in lung cancer." Thesis, Institute of Cancer Research (University Of London), 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.538270.

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Rane, Lukas, and Henrik Runeskog. "Calculating Center of Mass Using List Mode Data from PET Biograph128 mCT-1104." Thesis, KTH, Medicinteknik och hälsosystem, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-256084.

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A common problem within positron emission tomography examinations of the brain is the motion of the patient. If the patients ́ head moves during an examination all the data acquired after the movement will not be suited for clinical use. This means that a lot of data recovered from PET is not used at all. Motion tracking during PET acquisitions of the brain is not a well explored issue within medical imaging in relation to the magnitude of the problem. Due to the radiation risks of the examination and the logistics at the hospital, a second acquisition is not preferred. Therefore a method to a
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Yazdan, Panah Arya. "Integrating imaging biomarkers and genetic data to explore the pathophysiology of multiple sclerosis." Electronic Thesis or Diss., Sorbonne université, 2025. http://www.theses.fr/2025SORUS030.

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La sclérose en plaques (SEP) est une maladie chronique, inflammatoire et neurodégénérative du système nerveux central, caractérisée par une démyélinisation et des lésions des voies neuronales dans le cerveau et la moelle épinière. Bien que des avancées significatives aient été réalisées dans le traitement de la composante auto-immune de la SEP, l'approche de sa composante neurodégénérative reste un défi majeur. Cette thèse vise à développer de nouveaux biomarqueurs pour explorer la physiopathologie de la SEP, dans le but de mieux comprendre les mécanismes de la maladie et de soutenir le dévelo
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Wang, Chengjia. "Development of registration methods for cardiovascular anatomy and function using advanced 3T MRI, 320-slice CT and PET imaging." Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/22918.

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Different medical imaging modalities provide complementary anatomical and functional information. One increasingly important use of such information is in the clinical management of cardiovascular disease. Multi-modality data is helping improve diagnosis accuracy, and individualize treatment. The Clinical Research Imaging Centre at the University of Edinburgh, has been involved in a number of cardiovascular clinical trials using longitudinal computed tomography (CT) and multi-parametric magnetic resonance (MR) imaging. The critical image processing technique that combines the information from
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Besson, Florent. "Integrating PET-MR data for a multiparametric approach of tumour heterogeneity in non-small-cell lung cancer (NSCLC)." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASS081.

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L'hétérogénéité tumorale est un facteur important de progression et de résistance au traitement. L'imagerie multiparamétrique TEP-IRM offre des opportunités uniques de caractérisation biologique cellulaire, mais n’a jamais été évalué à l’échelle régionale intra-tumorale dans le cancer du poumon non à petites cellules (CBNPC), première cause de décès oncologique. Une approche multiparamétrique dynamique simultanée TEP-IRM au 18F-FDG a été développée en ce sens. Cette approche a nécessité l’implémentation « maison » de la méthode de référence de quantification TEP du métabolisme glucidique (modè
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Martens, Corentin. "Patient-Derived Tumour Growth Modelling from Multi-Parametric Analysis of Combined Dynamic PET/MR Data." Doctoral thesis, Universite Libre de Bruxelles, 2021. https://dipot.ulb.ac.be/dspace/bitstream/2013/320127/5/contratCM.pdf.

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Gliomas are the most common primary brain tumours and are associated with poor prognosis. Among them, diffuse gliomas – which include their most aggressive form glioblastoma (GBM) – are known to be highly infiltrative. The diagnosis and follow-up of gliomas rely on positron emission tomography (PET) and magnetic resonance imaging (MRI). However, these imaging techniques do not currently allow to assess the whole extent of such infiltrative tumours nor to anticipate their preferred invasion patterns, leading to sub-optimal treatment planning. Mathematical tumour growth modelling has been propos
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Livros sobre o assunto "PET imaging data"

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Paolini, Priscilla. Metodi e tecniche integrate di rilevamento per la realizzazione di modelli virtuali dell'architettura della città. Gangemi, 2007.

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Hillmer, Ansel T., Kelly P. Cosgrove, and Richard E. Carson. PET Brain Imaging Methodologies. Edited by Dennis S. Charney, Eric J. Nestler, Pamela Sklar, and Joseph D. Buxbaum. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190681425.003.0009.

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While quantitative and pharmacologically specific aspects distinguish molecular imaging, they also impose the need for considerable expertise to design, conduct, and analyze molecular imaging studies. Positron emission tomography (PET) brain imaging provides a powerful noninvasive tool for quantitative and pharmacologically specific clinical research. This chapter describes basic methodological considerations for PET brain imaging studies. First the physiological interpretation of the most common outcome measures of binding potential (BPND) and volume of distribution (VT) are described. Next,
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Schelbert, Heinrich R., Wolfgang Mohnike, and Gustav Hör. Oncologic and Cardiologic PET/CT-Diagnosis: An Interdisciplinary Atlas and Manual. Springer, 2017.

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Schelbert, Heinrich R., Wolfgang Mohnike, and Gustav Hör. Oncologic and Cardiologic PET/CT-Diagnosis: An Interdisciplinary Atlas and Manual. Springer London, Limited, 2008.

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Dilsizian, Vasken, and Gerald M. Pohost. Cardiac CT, PET and MR. Wiley & Sons, Limited, John, 2010.

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Dilsizian, Vasken, Robert O. Bonow, and Gerald M. Pohost. Cardiac CT, PET and MR. Wiley & Sons, Incorporated, John, 2008.

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Dilsizian, Vasken, and Gerald M. Pohost. Cardiac CT, PET and MR. Wiley & Sons, Limited, John, 2010.

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Dilsizian, Vasken, and Gerald M. Pohost. Cardiac CT, PET and MR. Wiley & Sons, Limited, John, 2010.

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Dilsizian, Vasken, and Gerald M. Pohost. Cardiac CT, PET and MR. Wiley & Sons, Incorporated, John, 2010.

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Dilsizian, Vasken, Robert O. Bonow, and Gerald M. Pohost. Cardiac CT, PET and MR. Wiley & Sons, Incorporated, John, 2008.

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Capítulos de livros sobre o assunto "PET imaging data"

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Saha, Gopal B. "Data Acquisition and Corrections." In Basics of PET Imaging. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-0805-6_3.

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Saha, Gopal B. "Data Acquisition and Corrections." In Basics of PET Imaging. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-16423-6_3.

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Surti, Suleman, and Joshua Scheuermann. "Data corrections and quantitative PET." In Physics of PET and SPECT Imaging. CRC Press, 2017. http://dx.doi.org/10.1201/9781315374383-12.

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Geissbühler, A., D. Townsend, and S. Kuijk. "A Graphics Workstation for PET Data Acquisition and Display." In Information Processing in Medical Imaging. Springer US, 1988. http://dx.doi.org/10.1007/978-1-4615-7263-3_26.

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Ratib, O., and C. Yap. "PET/CT Image Acquisition Protocols and Imaging Data Workflow." In Atlas of PET/CT Imaging in Oncology. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18517-5_4.

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Nichols, Thomas E., Jinyi Qi, and Richard M. Leahy. "Continuous Time Dynamic PET Imaging Using List Mode Data." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48714-x_8.

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Bousse, Alexandre, Jieqing Jiao, Kris Thielemans, et al. "Joint Direct Motion Estimation/Kinetic Images Reconstruction from Gated PET Data." In Computational Methods for Molecular Imaging. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18431-9_6.

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Thorwarth, Daniela. "Radiotherapy Target Volume Definition Based on PET/CT Imaging Data." In Imaging and Interventional Radiology for Radiation Oncology. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38261-2_6.

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Hu, Xiaoping, Kim K. Tan, David N. Levin, Charles A. Pelizzari, and George T. Y. Chen. "A Volume-Rendering Technique for Integrated Three-Dimensional display of MR and PET Data." In 3D Imaging in Medicine. Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-84211-5_24.

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Chen, Chin-Tu, Charles A. Pelizzari, George T. Y. Chen, Malcolm D. Cooper, and David N. Levin. "Image Analysis of PET Data with the aid of CT and MR Images." In Information Processing in Medical Imaging. Springer US, 1988. http://dx.doi.org/10.1007/978-1-4615-7263-3_41.

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Trabalhos de conferências sobre o assunto "PET imaging data"

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Qiao, H., F. Zhan, J. Hua, et al. "Deep learning-based PET image correction and optimization from CT-less PET data." In 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD). IEEE, 2024. http://dx.doi.org/10.1109/nss/mic/rtsd57108.2024.10656916.

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Thielemans, K., N. A. Karakatsanis, A. L. Kesner, M. Hansen, and R. G. Wells. "Progress report on PETSIRD: a standard for PET raw data." In 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD). IEEE, 2024. http://dx.doi.org/10.1109/nss/mic/rtsd57108.2024.10656513.

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Qi, W., J. Qi, L. Yang, Y. J. Tsai, and J. Kolthammer. "Data-driven Gating for PET Using a Population-based Encoder." In 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD). IEEE, 2024. http://dx.doi.org/10.1109/nss/mic/rtsd57108.2024.10656605.

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"DATA ACQUISITION ELECTRONICS FOR PET MAMMOGRAPHY IMAGING." In International Conference on Biomedical Electronics and Devices. SciTePress - Science and and Technology Publications, 2009. http://dx.doi.org/10.5220/0001533001920197.

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Chan, Chung, Steven Meikle, Roger Fulton, Tom W. Cai, and David D. Feng. "Quarantine MAP reconstruction of PET/CT data using dual priors." In Medical Imaging, edited by Michael J. Flynn and Jiang Hsieh. SPIE, 2006. http://dx.doi.org/10.1117/12.652898.

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Verhaeghe, J., P. Gravel, R. Mio, et al. "Motion-compensated fully 4D PET reconstruction using PET data supersets." In 2009 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC 2009). IEEE, 2009. http://dx.doi.org/10.1109/nssmic.2009.5401590.

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Berker, Yannick, and Volkmar Schulz. "Scattered PET data for attenuation-map reconstruction in PET/MRI: Fundamentals." In 2014 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). IEEE, 2014. http://dx.doi.org/10.1109/nssmic.2014.7430785.

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Kamasak, Mustafa E., Charles A. Bouman, Evan D. Morris, and Ken D. Sauer. "Parametric reconstruction of kinetic PET data with plasma function estimation." In Electronic Imaging 2005, edited by Charles A. Bouman and Eric L. Miller. SPIE, 2005. http://dx.doi.org/10.1117/12.597630.

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Galve, P., A. Lopez-Montes, J. M. Udias, S. C. Moore, and J. L. Herraiz. "Data-driven Improved Sampling in PET." In 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). IEEE, 2017. http://dx.doi.org/10.1109/nssmic.2017.8532745.

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Opfer, Roland, Sven Kabus, Torben Schneider, Ingwer C. Carlsen, Steffen Renisch, and Jörg Sabczynski. "Follow-up segmentation of lung tumors in PET and CT data." In SPIE Medical Imaging, edited by Nico Karssemeijer and Maryellen L. Giger. SPIE, 2009. http://dx.doi.org/10.1117/12.811599.

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Relatórios de organizações sobre o assunto "PET imaging data"

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FDG-PET/CT SUV for Response to Cancer Therapy, Clinically Feasible Profile. Chair Nathan Hall and Jeffrey Yap. Radiological Society of North America (RSNA) / Quantitative Imaging Biomarkers Alliance (QIBA), 2023. http://dx.doi.org/10.1148/qiba/20230615.

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This QIBA Profile documents specifications and requirements to provide comparability and consistency for quantitative FDG-PET across scanners in oncology. It can be applied to both clinical trial use as well as individual patient management. This document organizes acquisition, reconstruction and post-processing, analysis and interpretation as steps in a pipeline that transforms data to information to knowledge. The document, developed through the efforts of the QIBA FDG-PET Biomarker Committee, has shared content with the FDG-PET UPICT protocol, as well as additional material focused on the d
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Atherosclerosis Biomarkers by Computed Tomography Angiography (CTA). Chair Andrew Buckler, Luca Saba, and Uwe Joseph Schoepf. Radiological Society of North America (RSNA) / Quantitative Imaging Biomarkers Alliance (QIBA), 2023. http://dx.doi.org/10.1148/qiba/20230328.

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The clinical application of Computed Tomography Angiography (CTA) is widely available as a technique to optimize the therapeutic approach to treating vascular disease. Evaluation of atherosclerotic arterial plaque characteristics is currently based on qualitative biomarkers. However, the reproducibility of such findings has historically been limited even among experts (1). Quantitative imaging biomarkers have been shown to have additive value above traditional qualitative imaging metrics and clinical risk scores regarding patient outcomes (2). However, many definitions and cut-offs are present
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Saba, Luca, and Uwe Joseph Schoepf. Atherosclerosis Biomarkers by Computed Tomography Angiography (CTA) - Maintenance version June 2024. Chair Andrew Buckler. Radiological Society of North America (RSNA) / Quantitative Imaging Biomarkers Alliance (QIBA), 2024. http://dx.doi.org/10.1148/qiba/202406.

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The clinical application of Computed Tomography Angiography (CTA) is widely available as a technique to optimize the therapeutic approach to treating vascular disease. Evaluation of atherosclerotic arterial plaque characteristics is currently based on qualitative biomarkers. However, the reproducibility of such findings has historically been limited even among experts. Quantitative imaging biomarkers have been shown to have additive value above traditional qualitative imaging metrics and clinical risk scores regarding patient outcomes. However, many definitions and cut-offs are present in the
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Pepin, Kay, ed. MR Elastography of the Liver, Clinically Feasible Profile. Chair Richard Ehman and Patricia Cole. Radiological Society of North America (RSNA) / Quantitative Imaging Biomarkers Alliance (QIBA), 2023. http://dx.doi.org/10.1148/qiba/20231107.

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The goal of a QIBA Profile is to help achieve a useful level of performance for a given biomarker. The Claim (Section 2) describes the biomarker performance. The Activities (Section 3) contribute to generating the biomarker. Requirements are placed on the Actors that participate in those activities as necessary to achieve the Claim. Assessment Procedures (Section 4) for evaluating specific requirements are defined as needed. This QIBA Profile (Magnetic Resonance Elastography of the Liver) addresses the application of Magnetic Resonance Elastography (MRE) for the quantification of liver stiffne
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MRI-Based PDFF of the Liver, Consensus QIBA Profile. Chair Diego Hernando and Houchun (Harry) Hu. Radiological Society of North America (RSNA)/Quantitative Imaging Biomarkers Alliance (QIBA), 2024. https://doi.org/10.1148/qiba/20240619.

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A QIBA Profile is an implementation guide to generate a biomarker with an effective level of performance, mostly by reducing variability and bias in the measurement. The expected performance is expressed as Claims (Section 1.2). To achieve those claims, Actors, both human and equipment, (for example: scanners, data acquisition parameters, data reconstruction software and algorithms, image analysis tools, technologists and radiographers, medical physicists, radiologists) must meet the Checklist Requirements (Section 3) covering Periodic QA, Subject Handling, Image Data Acquisition, Image Data R
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Small Lung Nodule Volume Assessment and Monitoring in Low Dose CT Screening, Clinically Feasible Profile. Chair Artit Jirapatnakul, James Mulshine, and Kyle Myers. Radiological Society of North America (RSNA) / Quantitative Imaging Biomarkers Alliance (QIBA), 2023. http://dx.doi.org/10.1148/qiba/20231219.

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The goal of a QIBA Profile is to help achieve a useful level of performance for a given biomarker. The Claim (Section 2) describes the biomarker performance. The Profile Activities (Section 3) contribute to generating the biomarker. Requirements are placed on the Actors that participate in those activities as necessary to achieve the Claim. Assessment Procedures (Section 4) defines the technical methods to be used for evaluating conformance with profile requirements. This includes the steps needed for clinical sites and equipment vendors to be compliant with the Profile. This QIBA Profile (Sma
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DSC-MRI Consensus QIBA Profile. Chair Ona Wu, Mark Shiroishi, and Leland Hu. Radiological Society of North America (RSNA)/Quantitative Imaging Biomarkers Alliance (QIBA), 2020. https://doi.org/10.1148/qiba/20201022.

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The goal of a QIBA Profile is to help achieve a useful level of performance for a given biomarker. Profile development is an evolutionary, phased process; this Profile is in the Public Comment Resolution Draft stage. The performance claims represent expert consensus and will be empirically demonstrated at a subsequent stage. Users of this Profile are encouraged to refer to the following site to understand the document’s context: http://qibawiki.rsna.org/index.php/QIBA_Profile_Stages. The Claim (Section 2) describes the biomarker performance. The Activities (Section 3) contribute to generating
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SPECT Dopamine Transporters, Consensus QIBA Profile. Chair John Dickson and John Seibyl. Radiological Society of North America (RSNA)/Quantitative Imaging Biomarkers Alliance (QIBA), 2019. https://doi.org/10.1148/qiba/20191015.

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Parkinsonism is a major health problem. Distinguishing neurodegenerative causes of parkinsonism from non-degenerative movement disorders that can mimic Parkinson’s disease (PD) has important implications for prognosis and clinical management. The goal of this QIBA Profile is to optimize the performance of Iodine-123 (123I) ioflupane single photon emission computed tomography (SPECT) for quantifying the concentration of regional cerebral dopamine transporters (DaT) in patients with movement disorders. The Claim (Section 2): This profile claims that conformance with its specifications will provi
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Ultrasound Volume Blood Flow, Consensus QIBA Profile. Chair J. Brian Fowlkes, James Jago, and Oliver Kripfgans. American Institute of Ultrasound in Medicine (AIUM)/Radiological Society of North America (RSNA)/Quantitative Imaging Biomarkers Alliance (QIBA), 2024. https://doi.org/10.1148/qiba/20240105.

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A QIBA Profile is an implementation guide to generate a biomarker with an effective level of performance, mostly by reducing variability and bias in the measurement. The expected performance is expressed as Claims (Section 1.2). To achieve those claims, Actors (Manufacturers/Vendors/Field Service Engineers, Sonographers/Technologists, Physicians, Physicist/Clinical Engineer/QA manager, and Image Analysis Tools) must meet the Checklist Requirements (Section 3) covering Product Validation, Staff Qualification, Pre-delivery, Installation, Periodic QA, Subject Handling, Image Data Acquisition, Ima
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CT Lung Densitometry, Consensus QIBA Profile. Chair Charles Hatt and Miranda Kirby. • The Publisher is Radiological Society of North America (RSNA)/Quantitative Imaging Biomarkers Alliance (QIBA), 2020. https://doi.org/10.1148/qiba/20200904.

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The goal of a QIBA Profile is to achieve a repeatable and useful level of performance for measures of lung density from quantitative CT using the RA-950 HU and Perc15 biomarkers of emphysema. Please see Appendix C for more detailed information on the calculation of and rationale for RA-950 HU and Perc15 as the biomarkers of choice. The Claim (Section 2) describes the performance in terms of bias and precision of RA-950 HU and Perc15 for detecting change in lung density. The Activities (Section 3) describe how to generate RA-950 HU and Perc15 for longitudinal studies of the change in lung densi
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