Impact of γ factor in the penalty function of Bayesian penalized likelihood reconstruction (Q.Clear) to achieve high-resolution PET images

Abstract

Background The Bayesian penalized likelihood PET reconstruction (BPL) algorithm, Q.Clear (GE Healthcare), has recently been clinically applied to clinical image reconstruction. The BPL includes a relative difference penalty (RDP) as a penalty function. The β value that controls the behavior of RDP determines the global strength of noise suppression, whereas the γ factor in RDP controls the degree of edge preservation. The present study aimed to assess the effects of various γ factors in RDP on the ability to detect sub-centimeter lesions. Methods All PET data were acquired for 10 min using a Discovery MI PET/CT system (GE Healthcare). We used a NEMA IEC body phantom containing spheres with inner diameters of 10, 13, 17, 22, 28 and 37 mm and 4.0, 5.0, 6.2, 7.9, 10 and 13 mm. The target-to-background ratio of the phantom was 4:1, and the background activity concentration was 5.3 kBq/mL. We also evaluated cold spheres containing only non-radioactive water with the same background activity concentration. All images were reconstructed using BPL + time of flight (TOF). The ranges of β values and γ factors in BPL were 50–600 and 2–20, respectively. We reconstructed PET images using the Duetto toolbox for MATLAB software. We calculated the % hot contrast recovery coefficient (CRChot) of each hot sphere, the cold CRC (CRCcold) of each cold sphere, the background variability (BV) and residual lung error (LE). We measured the full width at half maximum (FWHM) of the micro hollow hot spheres ≤ 13 mm to assess spatial resolution on the reconstructed PET images. Results The CRChot and CRCcold for different β values and γ factors depended on the size of the small spheres. The CRChot, CRCcold and BV increased along with the γ factor. A 6.2-mm hot sphere was obvious in BPL as lower β values and higher γ factors, whereas γ factors ≥ 10 resulted in images with increased background noise. The FWHM became smaller when the γ factor increased. Conclusion High and low γ factors, respectively, preserved the edges of reconstructed PET images and promoted image smoothing. The BPL with a γ factor above the default value in Q.Clear (γ factor = 2) generated high-resolution PET images, although image noise slightly diverged. Optimizing the β value and the γ factor in BPL enabled the detection of lesions ≤ 6.2 mm.

The effects of various penalty parameter values in Q.Clear algorithm for rectal cancer detection on 18F-FDG images using a BGO-based PET/CT scanner: a phantom and clinical study

“…According to NEMA phantom analysis standards, metrics such as maximum standard uptake value ( ), contrast recovery (CR), recovery coefficient (RC), background variability (BV), lung residual error (LE), contrast, and signal-to-noise ratio (SNR) were measured. Each image was analyzed using PMOD software (version 3.8, developed by PMOD-Technologies LLC, Zurich, Switzerland) [ 16 , 31 , 32 ]. For image assessment in the PMOD, the slice traversing the central region of the spheres is identified.…”

“…Like other algorithms, Q.Clear incorporates point spread function modeling and, additionally, a relative difference penalty (RDP), which considers the relative difference between neighboring voxels to prevent excessive smoothing along significant edges [ 14 , 15 ]. The influence of RDP is regulated by a user-defined parameter referred to as the penalization factor ( β value), which governs the overall degree of noise suppression in Q.Clear, thereby allowing for numerous iterations without a corresponding increase in noise [ 16 ]. A higher number of iterations facilitates complete convergence for each individual voxel, enabling accurate estimation of quantitative parameters.…”

Investigation of PET image quality with acquisition time/bed and enhancement of lesion quantification accuracy through deep progressive learning

“…They found that Q.Clear yielded the highest SUV values for both sub-centimeter and larger nodules, while OSEM-TOF-PSF, OSEM-TOF, and OSEM followed in descending order. However, the PET image quality of Q.Clear was affected by the factors in the penalty function [ 23 ], which needed to be carefully adjusted by experience. On the contrary, DPL is a data-driven method which did not require any manual tuning.…”

Phantom and clinical evaluation of the Bayesian penalised likelihood reconstruction algorithm Q.Clear without PSF correction in amyloid PET images

“…The matrix size and FOV were set to 128 × 128 and 256 mm, respectively [ 18 ]. We reconstructed PET images using a workstation running the Duetto reconstruction toolbox for MATLAB R2017b (MathWorks Inc., Natick, MA, USA) available from GE HealthCare through a research collaboration agreement [ 26 ].…”