Advancing Positron Emission Tomography Image Quantification: Artificial Intelligence-Driven Methods, Clinical Challenges, and Emerging Opportunities in Long-Axial Field-of-View Positron Emission Tomography/Computed Tomography Imaging.
Authors
Yousefirizi F,Dassanayake M,Lopez A,Reader A,Cook GJR,Mingels C,Rahmim A,Seifert R,Alberts I
Affiliations (7)
Affiliations (7)
- Department of Integrative Oncology, BC Cancer Research Centre, Vancouver, Canada.
- Department of Biomedical Computing, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
- Department of Imaging Sciences, King's College London, UK.
- Department of Molecular Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London; Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London; Guy's & St Thomas' NHS Trust, PET Centre, St Thomas' Hospital, London, UK.
- Department of Radiology, University of British Columbia, Canada; Department of Physics, University of British Columbia, Canada; Department of Biomedical Engineering, University of British Columbia, Canada; BC Cancer Research Centre, Vancouver, Canada.
- Department of Molecular Imaging and Therapy, BC Cancer, Vancouver. Electronic address: [email protected].
Abstract
Positron emission tomography/computed tomography (PET/CT) imaging plays a pivotal role in oncology, aiding tumor metabolism assessment, disease staging, and therapy response evaluation. Traditionally, semi-quantitative metrics such as SUVmax have been extensively used, though these methods face limitations in reproducibility and predictive capability. Recent advancements in artificial intelligence (AI), particularly deep learning, have revolutionized PET imaging, significantly enhancing image quantification accuracy, and biomarker extraction capabilities, thereby enabling more precise clinical decision-making.
Topics
Journal ArticleReview