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A 64-year-old male patient, newly diagnosed with Hodgkin lymphoma, underwent dynamic total-body [18F]FDG PET/CT imaging. The PET list mode data was binned into frames, and the tomographic images were reconstructed following a previously published protocol [1]. The net influx rate (Ki) image was obtained from dynamic PET images by applying the Patlak graphical method, and subsequently, it was compared with the standardized uptake value image normalised to body weight (A). The patient displayed hypermetabolic lymph nodes in the mediastinum (referred to as lesion L1) and in the left lung (L2). Upon comparison between PET frames, a misalignment in the lung position, and consequently in the lesion positions, were observed and ultimately associated with the patients’ respiratory motion patterns. The largest misalignment was observed between the first and last frames, and the corresponding frame overlap is depicted in Figure (B). The position of the lesion L1 across the PET frames varied, amounting to approximately 6–7 mm along the coronal and axial plane (B-left), while a modest movement of approximately 2–3 mm was detected for lesion L2 (B-right). These mismatches in the lesion position throughout the acquisition window led to inaccurate parametric net influx rate assessments. The Ki image showed (i) a reduced volume of the L1 lesions (ΔV = 3.3 cm3, − 15%) and (ii) the emergence of negative Ki values, primarily within the regions most affected by motion (C). The Ki image artefacts compromise the accuracy of tumour metabolic rate evaluation, carrying significant clinical implications, especially within the domain of oncology [2]. Considering the expanding role of parametric analysis in clinical practice [3, 4], the effective identification and correction of artefact sources in parametric image data represent a central challenge in the translation of this research into clinical application.
Data availability
The datasets generated during and/or analysed during the current study cannot be available due to ethics restrictions.
References
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Acknowledgements
The authors would like to thank Prof. Wim Oyen (Department of Radiology and Nuclear Medicine of Rijnstate Hospital, Arnhem, The Netherlands) for valuable discussions and also Drs. Maurizio Conti, Joshua Schaefferkoetter and Hassan Sari, from Siemens Healthineers for useful feedback and for providing the e7tools.
Funding
Author Alessia Artesani has received research support by 2014–2020 National Operational Program (DR 2021–181 RTDA-PON_INNOVAZIONE).
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All the authors contributed to the study conception and design. Material preparation and data collection were made by Johannes H. van Snick and Joyce van Sluis. Data analyses were performed by Alessia Artesani under the guidance of Joyce van Sluis, Laura Providência, and Charalampos Tsoumpas. Walter Noordzij had the clinical oversight of the study. Charalampos Tsoumpas was responsible for the overall project. The first draft of the manuscript was written by Alessia Artesani and Charalampos Tsoumpas, and all the authors commented on the previous versions of the manuscript. All the authors read and approved the final manuscript.
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This is an observational study. The UMCG Research Ethics Committee has confirmed that no ethical approval is required.
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Authors J. S. and CT declare collaboration and funding from Siemens Healthineers. All the other authors declare they have no financial interests.
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Artesani, A., van Sluis, J., van Snick, J.H. et al. Impact of patient motion on parametric PET imaging. Eur J Nucl Med Mol Imaging 51, 1493–1494 (2024). https://doi.org/10.1007/s00259-024-06599-9
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DOI: https://doi.org/10.1007/s00259-024-06599-9