Expert consensus on workflow of PET/CT with long axial field-of-view
- PMID: 39520515
- DOI: 10.1007/s00259-024-06968-4
Expert consensus on workflow of PET/CT with long axial field-of-view
Abstract
Purpose: Positron emission tomography/computed tomography (PET/CT) imaging has been widely used in clinical practice. Long axial field-of-view (LAFOV) systems have enhanced clinical practice by leveraging their technological advantages and have emerged as the new state-of-the-art PET imaging modalities. A consensus was conducted to explore expert views in this emerging field to comprehensively elucidate the proposed workflow in LAFOV PET/CT examinations and highlight the potential challenges inherent in the workflow.
Methods: A multidisciplinary task group formed by 28 experts from six countries over the world discussed and approved the consensus based on the published guidelines, peer-reviewed articles of LAFOV PET/CT, and the collective experience from clinical practice. This consensus focuses on the workflow that allows for a broader range of imaging protocols of LAFOV PET/CT, catering to diverse patient needs and in line with precision medicine principles.
Results: This consensus describes the workflows and imaging protocols of LAFOV PET/CT for various imaging scenarios including routine static imaging, dynamic imaging, low-activity imaging, fast imaging, prolonged imaging, delayed imaging, and dual-tracer imaging. In addition, imaging reconstruction and reviewing specific to LAFOV PET/CT imaging, as well as the main challenges facing installation and application of LAFOV PET/CT scanner were also summarized.
Conclusion: This consensus summarized the various imaging workflow, imaging protocol, and challenges of LAFOV PET/CT imaging, aiming to enhance the clinical and research applications of these scanners.
Keywords: Expert consensus; Imaging protocols; Imaging workflow; Long axial field-of-view; Positron emission tomography/computed tomography (PET/CT); Total-body imaging.
© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Conflict of interest statement
Declarations. Conflict of interest: Zhongshan Hospital affiliated to Fudan University has a strategic partnership with United Imaging Healthcare (UIH) and professor Hongcheng Shi has been commonly invited to offer a lecture to UIH. UC Davis has a research agreement and a sales-based revenue-sharing agreement with United Imaging Healthcare. UIH partially supports a fellowship training program at UC Davis. Harshad Kulkarni from BAMF Health is part of the Speaker bureau of United Imaging within the past 2 years. Some investigator-initiated research projects at BAMF Health were funded by United Imaging. Axel Rominger receives speaker honoraria from Siemens and has research agreement ongoing. Martina Sollini is a member of the EJNMMI Editorial Board and an Associate Editor for the “Artificial intelligence, machine learning and radiomics” section of Cancer Imaging. Other authors declare no conflict of interest regarding this article. Ethics approval: Institutional Review Board approval was not required because this is a expert consensus. Consent to participate: Not applicable. Consent for publication: Not applicable.
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