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) 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. Methods Contrast 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. Results For 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. Conclusion This 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.

Determination of optimal regularization factor in Bayesian penalized likelihood reconstruction of brain PET images using [18F]FDG and [11C]PiB

“…who found that the optimal β value for neuro‐oncological [ 18 F]FDG PET using a BGO‐PET/CT scanner was 200. Another study using 18 F and 11 C brain imaging with a SiPM PET/MR scanner recommended β = 100 37 …”

“…Another study using 18 F and 11 C brain imaging with a SiPM PET/MR scanner recommended β = 100. 37 The maximum standardized uptake value (SUV max ) and metabolic tumor volume (MTV) in oncological [ 18 F]FDG PET depends to some extent on the β value in BPL due to varying image quality. 25,28,38 Brain PET images for diagnosing AD are usually quantified by the SUVR in clinical practice.…”

The effect of Q.Clear reconstruction on quantification and spatial resolution of 18F-FDG PET in simultaneous PET/MR

“…Another difference is the attenuation correction (AC) method, where PET/MR needs segmented MR images to generate pseudo-CT images, while PET/CT can derive AC map from CT data directly. Recently, two studies have been published on Q.Clear in PET/MR and both concluded that Q.Clear achieved better image quality than OSEM [ 1 , 2 ]. However, neither studies simultaneously performed clinical and phantom measurements.…”

Assessing the impact of different penalty factors of the Bayesian reconstruction algorithm Q.Clear on in vivo low count kinetic analysis of [11C]PHNO brain PET-MR studies

“…The β value (editable parameter in the algorithm) regulates the strength of the penalty term, acting as a noise penalisation factor and improves the Signal to Noise ratio (SNR). GE Healthcare has released the BSREM penalised likelihood reconstruction algorithm with the denomination of Q.Clear [ 6 , 7 ]. PET images can be analysed with qualitative methods, which are based on visual assessments, and semi-quantitative or quantitative methods, such as standard uptake values or volumetric measurements, respectively [ 8 ].…”

“…PET images can be analysed with qualitative methods, which are based on visual assessments, and semi-quantitative or quantitative methods, such as standard uptake values or volumetric measurements, respectively [ 8 ]. The literature regarding the use of Q.Clear as a reconstruction algorithm for quantification is limited, with some manuscripts investigating the effect of the algorithm in phantom images [ 7 , 9 , 10 ]. Most of the available literature is primarily focused on fluorinated tracers, with some publications investigating the effect of the algorithm in semi-quantification of whole-body scans and/or small structures imaging [ 11 – 14 ].…”