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

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 n...

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Main Authors: Ian R. Duffy PhD, Amanda J. Boyle PhD, Neil Vasdev PhD
Format: Article
Language:English
Published: SAGE Publishing 2019-08-01
Series:Molecular Imaging
Online Access:https://doi.org/10.1177/1536012119869070
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author Ian R. Duffy PhD
Amanda J. Boyle PhD
Neil Vasdev PhD
author_facet Ian R. Duffy PhD
Amanda J. Boyle PhD
Neil Vasdev PhD
author_sort Ian R. Duffy PhD
collection DOAJ
description 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.
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spelling doaj.art-2417e31f18044399b3b8c2d9ca47b3c02025-01-02T02:58:18ZengSAGE PublishingMolecular Imaging1536-01212019-08-011810.1177/1536012119869070Improving PET Imaging Acquisition and Analysis With Machine Learning: A Narrative Review With Focus on Alzheimer's Disease and OncologyIan R. Duffy PhD0Amanda J. Boyle PhD1Neil Vasdev PhD2 Azrieli Centre for Neuro-Radiochemistry, Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario, Canada Azrieli Centre for Neuro-Radiochemistry, Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario, Canada Department of Psychiatry, University of Toronto, Toronto, Ontario, CanadaMachine 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.https://doi.org/10.1177/1536012119869070
spellingShingle Ian R. Duffy PhD
Amanda J. Boyle PhD
Neil Vasdev PhD
Improving PET Imaging Acquisition and Analysis With Machine Learning: A Narrative Review With Focus on Alzheimer's Disease and Oncology
Molecular Imaging
title Improving PET Imaging Acquisition and Analysis With Machine Learning: A Narrative Review With Focus on Alzheimer's Disease and Oncology
title_full Improving PET Imaging Acquisition and Analysis With Machine Learning: A Narrative Review With Focus on Alzheimer's Disease and Oncology
title_fullStr Improving PET Imaging Acquisition and Analysis With Machine Learning: A Narrative Review With Focus on Alzheimer's Disease and Oncology
title_full_unstemmed Improving PET Imaging Acquisition and Analysis With Machine Learning: A Narrative Review With Focus on Alzheimer's Disease and Oncology
title_short Improving PET Imaging Acquisition and Analysis With Machine Learning: A Narrative Review With Focus on Alzheimer's Disease and Oncology
title_sort improving pet imaging acquisition and analysis with machine learning a narrative review with focus on alzheimer s disease and oncology
url https://doi.org/10.1177/1536012119869070
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