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Background <p>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.</p> Materials and methods <p>Twenty patients referred for a clinical [<sup>18</sup>F]fluorodeoxyglucose (FDG) PET scan were included. Each patient received 3.5&#xa0;MBq/kg of [<sup>18</sup>F]FDG and underwent a whole-body PET acquisition (120&#xa0;s/bed) on a digital BGO PET/CT (32&#xa0;cm FOV) 60&#xa0;min post-injection. Data were reconstructed into images (384 × 384 matrix) representing different acquisition times (120&#xa0;s, 90&#xa0;s, 60&#xa0;s, 45&#xa0;s, 30&#xa0;s and 15&#xa0;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>.</p> Results <p>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&#xa0;s, 90&#xa0;s), β100 provided the highest detectability, while shorter times (60–15&#xa0;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.</p> Conclusion <p>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&#xa0;s with β-values of 500–300, in combination with the Medium DLb-TOF model, when 3.5&#xa0;MBq/kg [<sup>18</sup>F]FDG was administered.</p>

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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

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  • 发表日期：

    2026年7月12日

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  • Anna Stenvall,
  • David Minarik,
  • Elin Trägårdh,
  • Sofia Kvernby

摘要

Background

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.

Materials and methods

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 SUVmeanliver/SDliver and detectability were calculated as SUVpeaktumor/SUVpeakliver.

Results

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.

Conclusion

SNR increased with higher β-values, longer acquisition times, and DLb-TOF application. Lesion detectability, defined as the ratio of SUVpeak in the lesion to SUVpeak 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.