Low-count 18F-FDG PET/CT using a deep-learning reconstruction algorithm on a 35-cm axial field-of-view digital PET system: a prospective clinical evaluation
Year: 2026
Type: article
Abstract: Background/Introduction: To determine how far injected activity can be reduced in clinical 18 F-FDG PET/CT while preserving clinical interpretability using a deep progressive reconstruction (DPR) algorithm on one of Europe’s first 35-cm axial field-of-view (AFOV) digital PET/CT systems. Methods: One hundred fifty consecutive patients undergoing vertex-to-mid-thigh 18 F-FDG PET/CT were prospectively included. All acquisitions were performed at 3 MBq/kg with an acquisition time of 120 s/bed. List-mode data were retrospectively truncated to simulate shorter acquisition durations of 90, 60, and 30 s/bed (corresponding to 75%, 50%, and 25% of the total counts, respectively). Images were reconstructed using EARL-2–compliant TOF-OSEM and DPR. Two blinded physicians scored image quality on a 5-point Likert scale; clinical interpretability was defined as a Likert score ≥ 3 . SUV peak , SUV max , signal-to-noise ratio (SNR), and lesion contrast-to-noise ratio (CNR) were measured using propagated volumes of interest. Results: DPR yielded higher qualitative image quality than EARL-2 OSEM at all count levels. At 60 s/bed ( ∼ 50% counts), DPR remained clinically interpretable, whereas matched-count EARL-2 OSEM showed marked image degradation. Lesion CNR was higher with DPR across all simulated count levels. SUV peak was largely preserved in reference tissues and increased modestly in lesions, whereas SUV max showed larger reconstruction-dependent increases. Conclusion: This prospective clinical evaluation demonstrates that, on a 35-cm AFOV digital PET/CT system, DPR enables an approximately 50% reduction in effective count statistics (60 s/bed or 1.5 MBq/kg under linear scaling assumptions) without compromising clinical interpretability. Quantitative SUV measurements, particularly SUV max , remain reconstruction dependent.
Source: EANM Innovation
Authors: Michel Atieh, Kevin Dylan Pushparajah, Jérôme Ghidaglia, Maria-Angéla Castilla-Lièvre, Daniel Deliu, Virginie Massaria, Kevin Harnais, Agnieszka Buffon, Maiwenn Beau, Yannick Jentreau, Natacha Da Fonseca, Stephane Onephandara, Widad Makhlouf, Aude Amoin, Christopher Boyeau, Karim En Nourhi, Solène Marie, Emmanuel Durand, Yolaine Suc, Sylvain Sans, Ahmadreza Rezaei, Stewart Young, Florent L. Besson
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W7167469297