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Background The Bayesian penalized likelihood (BPL) algorithm Q.Clear (GE Healthcare) allows fully convergent iterative reconstruction that results in better image quality and quantitative accuracy, while limiting image noise. The present study aimed to optimize BPL reconstruction parameters for 18F-NaF PET/CT images and to determine the feasibility of 18F-NaF PET/CT image acquisition over shorter durations in clinical practice. Methods A custom-designed thoracic spine phantom consisting of several inserts, soft tissue, normal spine, and metastatic bone tumor, was scanned using a Discovery MI PET/CT scanner (GE Healthcare). The phantom allows optional adjustment of activity distribution, tumor size, and attenuation. We reconstructed PET images using OSEM + PSF + TOF (2 iterations, 17 subsets, and a 4-mm Gaussian filter), BPL + TOF (β = 200 to 700), and scan durations of 30–120 s. Signal-to-noise ratios (SNR), contrast, and coefficients of variance (CV) as image quality indicators were calculated, whereas the quantitative measures were recovery coefficients (RC) and RC linearity over a range of activity. We retrospectively analyzed images from five persons without bone metastases (male, n = 1; female, n = 4), then standardized uptake values (SUV), CV, and SNR at the 4th, 5th, and 6th thoracic vertebra were calculated in BPL + TOF (β = 400) images. Results The optimal reconstruction parameter of the BPL was β = 400 when images were acquired at 120 s/bed. At 90 s/bed, the BPL with a β value of 400 yielded 24% and 18% higher SNR and contrast, respectively, than OSEM (2 iterations; 120 s acquisitions). The BPL was superior to OSEM in terms of RC and the RC linearity over a range of activity, regardless of scan duration. The SUVmax were lower in BPL, than in OSEM. The CV and vertebral SNR in BPL were superior to those in OSEM. Conclusions The optimal reconstruction parameters of 18F-NaF PET/CT images acquired over different durations were determined. The BPL can reduce PET acquisition to 90 s/bed in 18F-NaF PET/CT imaging. Our results suggest that BPL (β = 400) on SiPM-based TOF PET/CT scanner maintained high image quality and quantitative accuracy even for shorter acquisition durations.

Purpose The Bayesian penalized likelihood (BPL) reconstruction algorithm, Q.Clear, can achieve a higher signal‐to‐noise ratio on images and more accurate quantitation than ordered subset‐expectation maximization (OSEM). The reconstruction parameter (β) in BPL requires optimization according to the radiopharmaceutical tracer. The present study aimed to define the optimal β value in BPL required to diagnose Alzheimer disease from brain positron emission tomography (PET) images acquired using 18F‐fluoro‐2‐deoxy‐D‐glucose ([18F]FDG) and 11C‐labeled Pittsburg compound B ([11C]PiB). Methods Images generated from Hoffman 3D brain and cylindrical phantoms were acquired using a Discovery PET/computed tomography (CT) 710 and reconstructed using OSEM + time‐of‐flight (TOF) under clinical conditions and BPL + TOF (β = 20–1000). Contrast was calculated from images generated by the Hoffman 3D brain phantom, and noise and uniformity were calculated from those generated by the cylindrical phantom. Five cognitively healthy controls and five patients with Alzheimer disease were assessed using [18F]FDG and [11C]PiB PET to validate the findings from the phantom study. The β values were restricted by the findings of the phantom study, then one certified nuclear medicine physician and two certified nuclear medicine technologists visually determined optimal β values by scoring the quality parameters of image contrast, image noise, cerebellar stability, and overall image quality of PET images from 1 (poor) to 5 (excellent). Results The contrast in BPL satisfied the Japanese Society of Nuclear Medicine (JSNM) criterion of ≥55% and exceeded that of OSEM at ranges of β = 20–450 and 20–600 for [18F]FDG and [11C]PiB, respectively. The image noise in BPL satisfied the JSNM criterion of ≤15% and was below that in OSEM when β = 150–1000 and 400–1000 for [18F]FDG and [11C]PiB, respectively. The phantom study restricted the ranges of β values to 100–300 and 300–500 for [18F]FDG and [11C]PiB, respectively. The BPL scores for gray–white matter contrast and image noise, exceeded those of OSEM in [18F]FDG and [11C]PiB images regardless of β values. Visual evaluation confirmed that the optimal β values were 200 and 450 for [18F]FDG and [11C]PiB, respectively. Conclusions The BPL achieved better image contrast and less image noise than OSEM, while maintaining quantitative standardized uptake value ratios (SUVR) due to full convergence, more rigorous noise control, and edge preservation. The optimal β values for [18F]FDG and [11C]PiB brain PET were apparently 200 and 450, respectively. The present study provides useful information about how to determine optimal β values in BPL for brain PET imaging.

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.