Feasibility of dose reduction for [18F]FDG-PET/MR imaging of patients with non-lesional epilepsy

Abstract

The aim of the study was to evaluate the effect of reduced injected [18F]FDG activity levels on the quantitative and diagnostic accuracy of PET images of patients with non-lesional epilepsy (NLE).

Nine healthy volunteers and nine patients with NLE underwent 60-min dynamic list-mode (LM) scans on a fully-integrated PET/MRI system. Injected FDG activity levels were reduced virtually by randomly removing counts from the last 10-min of the LM data, so as to simulate the following activity levels: 50 %, 35 %, 20 %, and 10 % of the original activity. Four image reconstructions were evaluated: standard OSEM, OSEM with resolution recovery (PSF), the A-MAP, and the Asymmetrical Bowsher (AsymBowsher) algorithms. For the A-MAP algorithms, two weights were selected (low and high). Image contrast and noise levels were evaluated for all subjects while the lesion-to-background ratio (L/B) was only evaluated for patients. Patient images were scored by a Nuclear Medicine physician on a 5-point scale to assess clinical impression associated with the various reconstruction algorithms.

The image contrast and L/B ratio characterizing all four reconstruction algorithms were similar, except for reconstructions based on only 10 % of total counts. Based on clinical impression, images with diagnostic quality can be achieved with as low as 35 % of the standard injected activity. The selection of algorithms utilizing an anatomical prior did not provide a significant advantage for clinical readings, despite a small improvement in L/B (< 5 %) using the A-MAP and AsymBowsher reconstruction algorithms.

In patients with NLE who are undergoing [18F]FDG-PET/MR imaging, the injected [18F]FDG activity can be reduced to 35 % of the original dose levels without compromising.

Zusammenfassung

Das Ziel der Studie war es, die quantitativen Aussagen und die diagnostische Wertigkeit der PET Bilder für reduzierte applizierte Tracermengen von [18F]FDG im Fall von Patienten mit nicht läsionaler Epilepsie (NLE) zu bewerten. Dafür unterzogen sich neun gesunde Probanden und neun Patienten mit NLE einer 60-minütigen dynamischen (list-mode) Untersuchung in einem vollständig integrierten PET/MRT-System. Die injizierten FDG-Aktivitätsniveaus wurden virtuell durch zufälliges Entfernen von Messereignissen aus den letzten 10 Minuten von den LM-Daten reduziert, um 50 %, 35 %, 20 % und 10 % der ursprünglichen Aktivität zu simulieren. Vier Bildrekonstruktionen wurden ausgewertet: Standard-OSEM, OSEM mit Auflösungswiederherstellung (point spread function, PSF), die A-MAP und die asymmetrische Bowsher (AsymBowsher)-Algorithmen. Für die A-MAP-Algorithmen wurden zwei Gewichtungen (niedrig und hoch) ausgewählt. Bildkontrast und Bildrauschen wurden für alle Untersuchungen (Probanden und Patienten) bewertet, während das Läsions-zu-Hintergrund-Verhältnis (L/B) nur für Patienten bewertet wurde. Die Patientenbilder aus den 4 Rekonstruktionen wurden von einem Arzt der Nuklearmedizin unter Verwendeung einer 5-Punkte-Skala hinsichtlich der klinischen Wertigkeit beurteilt. Der Bildkontrast und das L/B-Verhältnis war für alle vier Rekonstruktionen ähnlich, mit Ausnahme von Rekonstruktionen basierend auf nur 10% applizierten Aktivität. Basierend auf dem klinischen Eindruck, können Bilder mit diagnostischer Qualität mit 35% der standardmäßig injizierten Aktivität, oder mehr erreicht werden. Die Auswahl von Algorithmen, die einen anatomischen Prior verwenden, lieferten keinen signifikanten Vorteil für die klinische Auswertung, trotz einer kleinen Verbesserung in L/B (< 5%) mit A-MAP und AsymBowsher. Bei Patienten mit NLE, die sich einer [18F]FDG-PET/MR Bildgebung unterziehen, kann die injizierte [18F]FDG-Aktivität auf 35% der ursprünglichen Dosierung ohne Einschränkung der klinischen Bildqualität reduziert werden.

Schlüsselwörter

Fluordesoxyglucose - F18 - Positronen-Emissions-Tomografie - Bildrekonstruktion - Gehirn

Key words

fluorodeoxyglucose - F18 - positron emission tomography - image reconstruction - brain

Publikationsverlauf

Eingereicht: 24. August 2022

Angenommen: 16. Januar 2023

Artikel online veröffentlicht:
20. Februar 2023

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