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 - 29/09/25

Doi : 10.1016/j.cpet.2025.07.010

Fereshteh Yousefirizi, PhD ^a, Movindu Dassanayake, PhD Candidate ^b, Alejandro Lopez, PhD ^c, Andrew Reader, PhD ^d, Gary J.R. Cook, MBBS, MSc, MD, FRCR, FRCP ^e,^f,^g, Clemens Mingels, MD ^c, Arman Rahmim, PhD, DABSNM ^h,^i,^j,^k, Robert Seifert, MD, MBA ^c, Ian Alberts, MBBS, MA, MD, PhD, FEBNM ^l,^m,^⁎
^a Department of Integrative Oncology, BC Cancer Research Centre, Vancouver, Canada
^b Department of Biomedical Computing, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
^c Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
^d Department of Imaging Sciences, King’s College London, UK
^e Department of Molecular Imaging, School of Biomedical Engineering and Imaging Sciences, King’s College London
^f Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King’s College London
^g Guy’s & St Thomas’ NHS Trust, PET Centre, St Thomas’ Hospital, London, UK
^h Department of Radiology, University of British Columbia, Canada
ⁱ Department of Physics, University of British Columbia, Canada
^j Department of Biomedical Engineering, University of British Columbia, Canada
^k BC Cancer Research Centre, Vancouver, Canada
^l Department of Molecular Imaging and Therapy, BC Cancer, Vancouver
^m Department of Radiology, University of British Columbia, Vancouver, British Columbia, Canada

^∗Corresponding author.

Résumé

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.

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Keywords : Long-axial field-of-view, Artificial intelligence, Image enhancement, Metabolic tumor volume, Radiomics, Multiplexed imaging

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Key points

Introduction

A clinical need for improved quantification? Identifying whereby advanced quantification and artificial intelligence may have clinical impact

Automated Disease Burden Quantification in Positron Emission Tomography/Computed Tomography Images

Beyond Total Metabolic Tumor Volume: the Role of Artificial Intelligence-Driven Radiomics in Quantitative Disease Burden Assessment

Artificial Intelligence-Driven Segmentation and Quantification in Long-Axial Field-of-View Positron Emission Tomography/Computed Tomography

Normative Positron Emission Tomography/Computed Tomography Dataset

Enabling the Next Generation of Long-Axial Field-of-View Positron Emission Tomography Imaging Through Collaborative Artificial Intelligence Innovation

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Vol 20 - N° 4

P. 463-473 - octobre 2025 Retour au numéro

Article précédent

Artificial Intelligence-Guided PET Image Reconstruction and Multi-Tracer Imaging : Novel Methods, Challenges, and Opportunities
Movindu Dassanayake, Alejandro Lopez, Andrew Reader, Gary J.R. Cook, Clemens Mingels, Arman Rahmim, Robert Seifert, Ian Alberts, Fereshteh Yousefirizi

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Objective Task-Based Evaluation of Quantitative Medical Imaging Methods : Emerging Frameworks and Future Directions
Yan Liu, Huitian Xia, Nancy A. Obuchowski, Richard Laforest, Arman Rahmim, Barry A. Siegel, Abhinav K. Jha

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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 - 29/09/25

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