Improving PET Imaging Acquisition and Analysis With Machine Learning: A Narrative Review With Focus on Alzheimer's Disease and Oncology

• Published in: Molecular imaging Vol. 18; p. 1536012119869070
• Main Authors: Duffy, Ian R., Boyle, Amanda J., Vasdev, Neil
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01.01.2019; Sage Publications Ltd; SAGE Publishing
• Subjects: Algorithms; Alzheimer's disease; Artificial intelligence; Artificial Intelligence in Molecular Imaging Clinics; Biomarkers; Digital imaging; Emission analysis; Image acquisition; Image processing; Image reconstruction; Learning algorithms; Machine learning; Medical imaging; Neurodegenerative diseases; Oncology; Physicians; Positron emission; Positron emission tomography; Tomography; cancer detection imaging; PET; cancer imaging; molecular imaging of neurodegenerative diseases; Alzheimer's
• ISSN: 1536-0121; 1535-3508; 1536-0121
• DOI: 10.1177/1536012119869070

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.