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
Affiliations (5)
Affiliations (5)
- Radiation Physics, Department of Hematology, Oncology and Radiation Physics, Skåne University Hospital, 224 42, Lund, Sweden.
- Department of Translational Medicine and Wallenberg Centre of Molecular Medicine, Lund University, Malmö, Sweden.
- Department of Clinical Physiology and Nuclear Medicine, Skåne University Hospital, Malmö, Sweden.
- Radiation Physics, Department of Hematology, Oncology and Radiation Physics, Skåne University Hospital, 224 42, Lund, Sweden. [email protected].
- Department of Translational Medicine and Wallenberg Centre of Molecular Medicine, Lund University, Malmö, Sweden. [email protected].
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 [<sup>18</sup>F]fluorodeoxyglucose (FDG) PET scan were included. Each patient received 3.5 MBq/kg of [<sup>18</sup>F]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<sub>liver</sub>/SD<sub>liver</sub> and detectability were calculated as SUVpeak<sub>tumor</sub>/SUVpeak<sub>liver</sub>. 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<sub>peak</sub> in the lesion to SUV<sub>peak</sub> 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 [<sup>18</sup>F]FDG was administered.