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Artificial Intelligence in Vascular-PET:: Translational and Clinical Applications.
Paravastu, Sriram S; Theng, Elizabeth H; Morris, Michael A; Grayson, Peter; Collins, Michael T; Maass-Moreno, Roberto; Piri, Reza; Gerke, Oke; Alavi, Abass; Flemming Høilund-Carlsen, Poul; Edenbrandt, Lars; Saboury, Babak.
Afiliação
  • Paravastu SS; Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health (NIH), Bethesda, MD 20892, USA; Skeletal Disorders and Mineral Homeostasis Section, National Institute of Dental and Craniofacial Research, National Institutes of Health (NIH), Bethesda, MD 20892, USA; Schoo
  • Theng EH; Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health (NIH), Bethesda, MD 20892, USA; Skeletal Disorders and Mineral Homeostasis Section, National Institute of Dental and Craniofacial Research, National Institutes of Health (NIH), Bethesda, MD 20892, USA; Schoo
  • Morris MA; Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health (NIH), Bethesda, MD 20892, USA; Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD, USA; Institute for Data Science, Department of Diagnostic R
  • Grayson P; National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, 10 Center Dr, Building 10 Room 12S-253, Bethesda, MD 20892, USA.
  • Collins MT; Skeletal Disorders and Mineral Homeostasis Section, National Institute of Dental and Craniofacial Research, National Institutes of Health (NIH), Bethesda, MD 20892, USA.
  • Maass-Moreno R; Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health (NIH), Bethesda, MD 20892, USA.
  • Piri R; Department of Nuclear Medicine, Odense University Hospital, 5000 Odense C, Denmark; Department of Clinical Research, University of Southern Denmark, Odense, Denmark.
  • Gerke O; Department of Nuclear Medicine, Odense University Hospital, 5000 Odense C, Denmark; Department of Clinical Research, University of Southern Denmark, Odense, Denmark.
  • Alavi A; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Flemming Høilund-Carlsen P; Department of Nuclear Medicine, Odense University Hospital, 5000 Odense C, Denmark; Department of Clinical Research, University of Southern Denmark, Odense, Denmark.
  • Edenbrandt L; Region Västra Götaland, Sahlgrenska University Hospital, Department of Clinical Physiology, Gothenburg, Sweden; Department of Molecular and Clinical Medicine, Institute of Medicine, SU Sahlgrenska, 413 45 Göteborg, Sweden.
  • Saboury B; Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health (NIH), Bethesda, MD 20892, USA; Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD, USA; Department of Radiology, Perelman School of Medicine,
PET Clin ; 17(1): 95-113, 2022 Jan.
Article em En | MEDLINE | ID: mdl-34809874
ABSTRACT
Positron emission tomography (PET) offers an incredible wealth of diverse research applications in vascular disease, providing a depth of molecular, functional, structural, and spatial information. Despite this, vascular PET imaging has not yet assumed the same clinical use as vascular ultrasound, CT, and MR imaging which provides information about late-onset, structural tissue changes. The current clinical utility of PET relies heavily on visual inspection and suboptimal parameters such as SUVmax; emerging applications have begun to harness the tool of whole-body PET to better understand the disease. Even still, without automation, this is a time-consuming and variable process. This review summarizes PET applications in vascular disorders, highlights emerging AI methods, and discusses the unlocked potential of AI in the clinical space.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Tomografia por Emissão de Pósitrons Idioma: En Ano de publicação: 2022 Tipo de documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Tomografia por Emissão de Pósitrons Idioma: En Ano de publicação: 2022 Tipo de documento: Article