Performance evaluation of the Q.Clear reconstruction framework versus conventional reconstruction algorithms for quantitative brain PET-MR studies

Abstract Background Q.Clear is a Bayesian penalized likelihood (BPL) reconstruction algorithm that presents improvements in signal-to-noise ratio (SNR) clinical positron emission tomography (PET) scans. Brain studies research require provides good spatial resolution and accentuates contrast features however, filtered back-projection (FBP) not available on GE SIGNA PET-Magnetic Resonance (PET-MR) have been reconstructed with an ordered subset expectation maximization (OSEM) algorithm. This study aims to propose strategy approximate brain PET quantitative outcomes obtained from images versus traditional FBP OSEM. Methods Contrast recovery background variability were investigated the National Electrical Manufacturers Association (NEMA) Image Quality (IQ) phantom. Resolution, axial uniformity SNR using Hoffman Both phantoms scanned Siemens Biograph 6 TruePoint PET-Computed Tomography (CT) General Electric PET-MR, for FBP, OSEM Q.Clear. Differences between metrics different ? values PET-CT determined. Results For plane resolution, low presented best results, whereas higher gave results. The results are greatly impacted by value, where < 600 can yield worse compared reconstruction. Conclusion shows improves better SNR, comparison OSEM, PET-MR. When values, provide similar ones reconstruction, suggesting it be used kinetic modelling studies.