Artificial Intelligence-Guided PET Image Reconstruction and Multi-Tracer Imaging : Novel Methods, Challenges, and Opportunities - 29/09/25

Doi : 10.1016/j.cpet.2025.07.005

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

^∗Corresponding author.

Résumé

This article reviews recent advancements in PET/computed tomography imaging, emphasizing the transformative impact of total-body and long-axial field-of-view scanners, which offer increased sensitivity, larger coverage, and faster, lower-dose imaging. It highlights the growing role of artificial intelligence (AI) in enhancing image reconstruction, resolution, and multi-tracer applications, enabling rapid processing and improved quantification. AI-driven techniques, such as super-resolution, positron range correction, and motion compensation, are improving lesion detectability and image quality. The review underscores the potential of these innovations to revolutionize clinical and research PET imaging, while also noting the challenges in validation and implementation for routine practice.

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Keywords : Long-axial field-of-view, Artificial intelligence, Image enhancement, Multiplexed imaging

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Introduction

Artificial intelligence for image reconstruction: single-tracer and simultaneous multi-tracer PET

Deep Learned PET Reconstruction

Multiplexed-PET Imaging

Artificial intelligence methods for image resolution enhancement

Summary

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Vol 20 - N° 4

P. 453-461 - octobre 2025 Retour au numéro

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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
Fereshteh Yousefirizi, Movindu Dassanayake, Alejandro Lopez, Andrew Reader, Gary J.R. Cook, Clemens Mingels, Arman Rahmim, Robert Seifert, Ian Alberts

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Artificial Intelligence-Guided PET Image Reconstruction and Multi-Tracer Imaging : Novel Methods, Challenges, and Opportunities - 29/09/25

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