Evaluation of deep learning-based reconstruction models on non-TOF BGO PET/CT: impact of acquisition times and BSREM penalization factors on lesion detectability and SNR.

Authors

Anna Stenvall, David Minarik, Elin Trägårdh, Sofia Kvernby

Published in

EJNMMI physics. Jul 12, 2026. Epub Jul 12, 2026.

Abstract

New long field-of-view (FOV) PET scanners using bismuth germanate (BGO) detectors without time-of-flight (TOF) capability are now available. These systems incorporate deep learning-based TOF (DLb-TOF) models to compensate for the absence of TOF. There is a lack of studies systematically investigating the optimal balance between signal to noise ratio and lesion detectability across a broader range of acquisition times and β-values for these DLb-TOF models. This study aims to evaluate the trade-off between acquisition time, signal-to-noise ratio (SNR) and lesion detectability to guide optimization of clinical protocol.
Twenty patients referred for a clinical [¹⁸F]fluorodeoxyglucose (FDG) PET scan were included. Each patient received 3.5 MBq/kg of [¹⁸F]FDG and underwent a whole-body PET acquisition (120 s/bed) on a digital BGO PET/CT (32 cm FOV) 60 min post-injection. Data were reconstructed into images (384 × 384 matrix) representing different acquisition times (120 s, 90 s, 60 s, 45 s, 30 s and 15 s) using BSREM with β-values ranging from 50 to 1100. Three DLb-TOF models (Low, Medium, High) were applied. Volumes of interest were placed in the liver and two avid lesions per patient. SNR were calculated as SUVmean_liver/SD_liver and detectability were calculated as SUVpeak_tumor/SUVpeak_liver.
SNR increased with longer acquisition times and higher β-values. DLb-TOF models improved SNR across all settings, with the Low DLb-TOF model producing the largest increase. Lesion detectability depended on the acquisition time and β-value. At longer acquisition times (120 s, 90 s), β100 provided the highest detectability, while shorter times (60-15 s) required higher β-value (β300) for optimal detectability. Among DLb-TOF models, the High model gave the best detectability overall, though the Low model performed better at lower β-values.
SNR increased with higher β-values, longer acquisition times, and DLb-TOF application. Lesion detectability, defined as the ratio of SUV_peak in the lesion to SUV_peak in the liver, depended on the β-value, acquisition time, and the DLb-TOF model used. The Low DLb-TOF model had the best SNR but at the expense of detectability. The optimal parameters for the evaluated BGO PET/CT system, balancing SNR and lesion detectability within a clinical reasonable acquisition time, were 60-90 s with β-values of 500-300, in combination with the Medium DLb-TOF model, when 3.5 MBq/kg [¹⁸F]FDG was administered.

PMID:
42437412
Bibliographic data and abstract were imported from PubMed on 12 Jul 2026.

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