Artificial Intelligence-Guided PET Image Reconstruction and Multi-Tracer Imaging: Novel Methods, Challenges, and Opportunities.
Authors
Affiliations (7)
Affiliations (7)
- 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 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, Canada. Electronic address: [email protected].
- Department of Integrative Oncology, BC Cancer Research Centre, Vancouver, Canada.
Abstract
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.