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

Sriram S Paravastu; Elizabeth H Theng; Michael A Morris; Peter Grayson; Michael T Collins; Roberto Maass-Moreno; Reza Piri; Oke Gerke; Abass Alavi; Poul Flemming Høilund-Carlsen; Lars Edenbrandt; Babak Saboury

doi:10.1016/j.cpet.2021.09.003

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

PET Clin. 2022 Jan;17(1):95-113. doi: 10.1016/j.cpet.2021.09.003.

Authors

Affiliations

¹ 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.
² 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.
³ 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.
⁴ Skeletal Disorders and Mineral Homeostasis Section, National Institute of Dental and Craniofacial Research, National Institutes of Health (NIH), Bethesda, MD 20892, USA.
⁵ Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health (NIH), Bethesda, MD 20892, USA.
⁶ Department of Nuclear Medicine, Odense University Hospital, 5000 Odense C, Denmark; Department of Clinical Research, University of Southern Denmark, Odense, Denmark.
⁷ Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
⁸ 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.
⁹ 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, University of Pennsylvania, Philadelphia, PA, USA. Electronic address: babak.saboury@nih.gov.

PMID: 34809874
DOI: 10.1016/j.cpet.2021.09.003

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.

Keywords: Artificial intelligence; Atherosclerosis; Calcification; Inflammation; Segmentation; Vascular disease; Vasculitis.

Publication types

Review

MeSH terms

Artificial Intelligence*
Humans
Magnetic Resonance Imaging
Positron-Emission Tomography*