Improving PET Imaging Acquisition and Analysis With Machine Learning: A Narrative Review With Focus on Alzheimer's Disease and Oncology. (19th August 2019)

• Record Type: Journal Article
• Authors: Duffy, Ian R.; Boyle, Amanda J.; Vasdev, Neil
• Is Part Of: Molecular imaging. Volume 18(2019)
• Publication Date: 2019-08-19
• Subjects: molecular imaging of neurodegenerative diseases -- cancer detection imaging -- PET -- Alzheimer's -- cancer imaging; Molecular diagnosis -- Periodicals; Diagnostic imaging -- Periodicals; Molecular biology -- Periodicals; Molecular diagnosis; Diagnostic imaging; Molecular biology; Periodicals; 616.075
• DOI: 10.1177/1536012119869070
• Languages: English
• ISSNs: 1535-3508

Abstract

Machine learning (ML) algorithms have found increasing utility in the medical imaging field and numerous applications in the analysis of digital biomarkers within positron emission tomography (PET) imaging have emerged. Interest in the use of artificial intelligence in PET imaging for the study of neurodegenerative diseases and oncology stems from the potential for such techniques to streamline decision support for physicians providing early and accurate diagnosis and allowing personalized treatment regimens. In this review, the use of ML to improve PET image acquisition and reconstruction is presented, along with an overview of its applications in the analysis of PET images for the study of Alzheimer's disease and oncology.