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Quantitative PET in the 2020s: a roadmap.
Meikle, Steven R; Sossi, Vesna; Roncali, Emilie; Cherry, Simon R; Banati, Richard; Mankoff, David; Jones, Terry; James, Michelle; Sutcliffe, Julie; Ouyang, Jinsong; Petibon, Yoann; Ma, Chao; El Fakhri, Georges; Surti, Suleman; Karp, Joel S; Badawi, Ramsey D; Yamaya, Taiga; Akamatsu, Go; Schramm, Georg; Rezaei, Ahmadreza; Nuyts, Johan; Fulton, Roger; Kyme, André; Lois, Cristina; Sari, Hasan; Price, Julie; Boellaard, Ronald; Jeraj, Robert; Bailey, Dale L; Eslick, Enid; Willowson, Kathy P; Dutta, Joyita.
Afiliação
  • Meikle SR; Sydney School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Australia.
  • Sossi V; Brain and Mind Centre, The University of Sydney, Australia.
  • Roncali E; Department of Physics and Astronomy, University of British Columbia, Canada.
  • Cherry SR; Department of Biomedical Engineering, University of California, Davis, United States of America.
  • Banati R; Department of Biomedical Engineering, University of California, Davis, United States of America.
  • Mankoff D; Department of Radiology, University of California, Davis, United States of America.
  • Jones T; Sydney School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Australia.
  • James M; Brain and Mind Centre, The University of Sydney, Australia.
  • Sutcliffe J; Australian Nuclear Science and Technology Organisation, Sydney, Australia.
  • Ouyang J; Department of Radiology, University of Pennsylvania, United States of America.
  • Petibon Y; Department of Radiology, University of California, Davis, United States of America.
  • Ma C; Department of Radiology, Molecular Imaging Program at Stanford (MIPS), CA, United States of America.
  • El Fakhri G; Department of Neurology and Neurological Sciences, Stanford University, CA, United States of America.
  • Surti S; Department of Biomedical Engineering, University of California, Davis, United States of America.
  • Karp JS; Department of Internal Medicine, University of California, Davis, CA, United States of America.
  • Badawi RD; Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, United States of America.
  • Yamaya T; Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, United States of America.
  • Akamatsu G; Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, United States of America.
  • Schramm G; Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, United States of America.
  • Rezaei A; Department of Radiology, University of Pennsylvania, United States of America.
  • Nuyts J; Department of Radiology, University of Pennsylvania, United States of America.
  • Fulton R; Department of Biomedical Engineering, University of California, Davis, United States of America.
  • Kyme A; Department of Radiology, University of California, Davis, United States of America.
  • Lois C; National Institute of Radiological Sciences (NIRS), National Institutes for Quantum and Radiological Science and Technology (QST), Chiba, Japan.
  • Sari H; National Institute of Radiological Sciences (NIRS), National Institutes for Quantum and Radiological Science and Technology (QST), Chiba, Japan.
  • Price J; Department of Imaging and Pathology, Nuclear Medicine & Molecular imaging, KU Leuven, Belgium.
  • Boellaard R; Department of Imaging and Pathology, Nuclear Medicine & Molecular imaging, KU Leuven, Belgium.
  • Jeraj R; Department of Imaging and Pathology, Nuclear Medicine & Molecular imaging, KU Leuven, Belgium.
  • Bailey DL; Brain and Mind Centre, The University of Sydney, Australia.
  • Eslick E; Department of Medical Physics, Westmead Hospital, Sydney, Australia.
  • Willowson KP; Brain and Mind Centre, The University of Sydney, Australia.
  • Dutta J; School of Biomedical Engineering, Faculty of Engineering and IT, The University of Sydney, Australia.
Phys Med Biol ; 66(6): 06RM01, 2021 03 12.
Article em En | MEDLINE | ID: mdl-33339012
ABSTRACT
Positron emission tomography (PET) plays an increasingly important role in research and clinical applications, catalysed by remarkable technical advances and a growing appreciation of the need for reliable, sensitive biomarkers of human function in health and disease. Over the last 30 years, a large amount of the physics and engineering effort in PET has been motivated by the dominant clinical application during that period, oncology. This has led to important developments such as PET/CT, whole-body PET, 3D PET, accelerated statistical image reconstruction, and time-of-flight PET. Despite impressive improvements in image quality as a result of these advances, the emphasis on static, semi-quantitative 'hot spot' imaging for oncologic applications has meant that the capability of PET to quantify biologically relevant parameters based on tracer kinetics has not been fully exploited. More recent advances, such as PET/MR and total-body PET, have opened up the ability to address a vast range of new research questions, from which a future expansion of applications and radiotracers appears highly likely. Many of these new applications and tracers will, at least initially, require quantitative analyses that more fully exploit the exquisite sensitivity of PET and the tracer principle on which it is based. It is also expected that they will require more sophisticated quantitative analysis methods than those that are currently available. At the same time, artificial intelligence is revolutionizing data analysis and impacting the relationship between the statistical quality of the acquired data and the information we can extract from the data. In this roadmap, leaders of the key sub-disciplines of the field identify the challenges and opportunities to be addressed over the next ten years that will enable PET to realise its full quantitative potential, initially in research laboratories and, ultimately, in clinical practice.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Tomografia por Emissão de Pósitrons / Tomografia por Emissão de Pósitrons combinada à Tomografia Computadorizada / Neoplasias Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Phys Med Biol Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Austrália

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Tomografia por Emissão de Pósitrons / Tomografia por Emissão de Pósitrons combinada à Tomografia Computadorizada / Neoplasias Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Phys Med Biol Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Austrália