Artificial Intelligence in Vascular-PET:: Translational and Clinical Applications.

Journal: PET clinics
Published Date: Jan 1, 2022

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.

Authors

  • Sriram S Paravastu
    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; School of Medicine, University of Missouri-Kansas City, 2411 Holmes Street, Kansas City, MO 64108, USA.
  • Elizabeth H Theng
    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; School of Medicine, University of Missouri-Kansas City, 2411 Holmes Street, Kansas City, MO 64108, USA.
  • Michael A Morris
    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 Radiology and Nuclear Medicine - University of Miami Miller School of Medicine, Miami, FL, USA.
  • Peter Grayson
    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.
  • Michael T Collins
  • Roberto Maass-Moreno
    Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health (NIH), Bethesda, MD 20892, USA.
  • Reza Piri
    Department of Nuclear Medicine, Odense University Hospital, 5000 Odense C, Denmark; Department of Clinical Research, University of Southern Denmark, Odense, Denmark.
  • Oke Gerke
    Department of Nuclear Medicine, Odense University Hospital, 29 Sdr. Boulevard, 5000 Odense, Denmark. oke.gerke@rsyd.dk.
  • Abass Alavi
  • Poul Flemming Høilund-Carlsen
    Department of Nuclear Medicine, Odense University Hospital, 5000 Odense C, Denmark; Department of Clinical Research, University of Southern Denmark, Odense, Denmark.
  • Lars Edenbrandt
    Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
  • Babak Saboury
    IBM Research, Almaden, San Jose, California.

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