Abstract
BackgroundQ.Clear is a Bayesian penalized likelihood (BPL) reconstruction algorithm that presents improvements in signal-to-noise ratio (SNR) in clinical positron emission tomography (PET) scans. Brain studies in research require a reconstruction that provides a good spatial resolution and accentuates contrast features however, filtered back-projection (FBP) reconstruction is not available on GE SIGNA PET-Magnetic Resonance (PET-MR) and studies have been reconstructed with an ordered subset expectation maximization (OSEM) algorithm. This study aims to propose a strategy to approximate brain PET quantitative outcomes obtained from images reconstructed with Q.Clear versus traditional FBP and OSEM.MethodsContrast recovery and background variability were investigated with the National Electrical Manufacturers Association (NEMA) Image Quality (IQ) phantom. Resolution, axial uniformity and SNR were investigated using the Hoffman phantom. Both phantoms were scanned on a Siemens Biograph 6 TruePoint PET-Computed Tomography (CT) and a General Electric SIGNA PET-MR, for FBP, OSEM and Q.Clear. Differences between the metrics obtained with Q.Clear with different β values and FBP obtained on the PET-CT were determined.ResultsFor in plane and axial resolution, Q.Clear with low β values presented the best results, whereas for SNR Q.Clear with higher β gave the best results. The uniformity results are greatly impacted by the β value, where β < 600 can yield worse uniformity results compared with the FBP reconstruction.ConclusionThis study shows that Q.Clear improves contrast recovery and provides better resolution and SNR, in comparison to OSEM, on the PET-MR. When using low β values, Q.Clear can provide similar results to the ones obtained with traditional FBP reconstruction, suggesting it can be used for quantitative brain PET kinetic modelling studies.
Highlights
Q.Clear is a Bayesian penalized likelihood (BPL) reconstruction algorithm that presents improvements in signal-to-noise ratio (SNR) in clinical positron emission tomography (PET) scans
Iterative reconstruction algorithms are routinely used in the clinical setting, where image quality and lesion contrast are of great importance, analytical reconstruction algorithms are still used in research for accurate PET data quantification via kinetic modelling [1]
This study aimed to evaluate the performance of the Q.Clear, against that of the widely used ordered subset expectation maximization (OSEM) and the filtered back-projection (FBP) algorithms in brain phantom images acquired on a clinical PETCT and on a clinical PET-Magnetic resonance (MR) system using 18F- and 11C-labelled radiotracers
Summary
Q.Clear is a Bayesian penalized likelihood (BPL) reconstruction algorithm that presents improvements in signal-to-noise ratio (SNR) in clinical positron emission tomography (PET) scans. Iterative reconstruction algorithms are routinely used in the clinical setting, where image quality and lesion contrast are of great importance, analytical reconstruction algorithms are still used in research for accurate PET data quantification via kinetic modelling [1]. The FBP reconstruction is not available for clinical use on the GE SIGNA PET-MR scanner; OSEM reconstructions have been used for processing brain studies In smaller regions, such as the ones that can be found in the brain, the convergence rate of OSEM process must be stopped early in order to not compromise image quality due to excessive noise [4, 5]. The impact of using nonFBP methods for reconstruction of quantitative brain studies is poorly understood and with latest PET-MR technology rapidly gaining momentum in the field of brain clinical research, studies are needed to assess and minimise the gap between traditional PET-CT kinetic modelling studies with data reconstructed using FBP versus PET-MRI OSEM and Q.Clear approaches
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