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Inform. med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Benefits in patient comfort, efficiency, and sustainability can come from reducing positron emission tomography (PET) scan\u2019s acquisition duration. This study assesses the clinical adequacy of restoring fast-acquisition\n                    <jats:sup>18<\/jats:sup>\n                    F-fluorodeoxyglucose ([\n                    <jats:sup>18<\/jats:sup>\n                    F]FDG) PET to its standard-of-care image quality through deep-learning-based (DL) methods. Fast and standard whole-body [\n                    <jats:sup>18<\/jats:sup>\n                    F]FDG PET acquisitions of 117 oncological patients were included in the training and testing of three convolutional neural networks. The best-performing network during training was chosen for clinical evaluation on the test set (\n                    <jats:italic>N<\/jats:italic>\n                    \u2009=\u200925). Visual assessment and lesion detectability of the fast acquisitions, of 20 and 30 seconds per axial field of view (s\/AFOV), with and without DL-based denoising, and of the local standard of care, of 70 s\/AFOV, were performed by three experienced nuclear medicine physicians. Quantification was conducted globally (voxel-wise), in healthy organs and the reported lesions. Optimised Gaussian and non-local means filters served as benchmarks. Visual assessment revealed 20 and 30 s\/AFOV with DL-based denoising to have similar image quality to the standard of care. Average lesion-based sensitivity and positive predictive value were 74% and 72%, respectively, for 20\u00a0s\/AFOV\u2009+\u2009DL and 72% and 80% for 30\u00a0s\/AFOV\u2009+\u2009DL. DL-based denoising displayed the highest voxel-wise agreement with the standard-of-care (\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.001). Liver and lungs in the DL-denoised images exhibited a higher signal-to-noise ratio than the standard of care. The median absolute maximum standardised uptake value deviation in the lesions was as low as 0.39 for 20\u00a0s\/AFOV\u2009+\u2009DL and 0.30 for 30\u00a0s\/AFOV\u2009+\u2009DL. The proposed DL-based method proved to be suitable for the restoration of fast-acquisition whole-body [\n                    <jats:sup>18<\/jats:sup>\n                    F]FDG PET, having resulted in images similar to the standard-of-care acquisitions. DL-based denoising outperformed standard benchmark methods.\n                  <\/jats:p>","DOI":"10.1007\/s10278-025-01638-9","type":"journal-article","created":{"date-parts":[[2025,8,28]],"date-time":"2025-08-28T16:14:54Z","timestamp":1756397694000},"page":"2582-2592","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An AI-Based Solution for Denoising Fast-Acquisition [18F]FDG PET: Clinical Feasibility and Quantitative Assessment"],"prefix":"10.1007","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1990-8759","authenticated-orcid":false,"given":"Lu\u00edsa","family":"C. 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