New AI trick helps older PET scanners match cutting-edge imaging performance
Researchers have cracked how to optimize a clever workaround that lets hospitals get better tumor scans from older PET equipment without costly upgrades. The findings reveal the precise settings needed to balance scan speed, image quality, and cancer detection—potentially saving healthcare systems millions while improving patient outcomes.
Originaltitel: 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.
BGO-detektorer utan tidsupplösning (TOF) kräver nya optimeringsprotokoll för PET/CT-avbildning. Forskare vid Skåne universitetssjukhus utvärde djupinlärningsbaserade TOF-modeller (DLb-TOF) för att kompensera denna nackdel och identifierade kritiska inställningar för klinisk användning. I studien på 20 patienter testades olika scanningstider (15–120 sekunder) och BSREM-penaliseringsfaktorer (β15–β300). Högre β-värden och längre scanningstider förbättrade signal-brus-förhållandet. För läsiondetektabilitet framträdde ett tydligt mönster: långa scanningstider (90–120 s) krävde β100, medan korta tider (15–60 s) förespråkade β300 för optimal synlighet. De DLb-TOF-modeller som tillämpades visade genomgående SNR-förbättringar. Resultaten möjliggör protokolloptimering som reducerar patientdos samtidigt som diagnostisk kvalitet bevares — en väsentlig faktor för regionala inköpsbeslut och regulatorisk efterlevnad vid implementering av nya PET-system.
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 [ 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 SUV