Impact of the Noise Penalty Factor on Quantification in Bayesian Penalized Likelihood (Q.Clear) Reconstructions of 68Ga-PSMA PET/CT Scans

Rijnsdorp, Sjoerd; Roef, Mark J.; Arends, Albert J.

doi:10.3390/diagnostics11050847

Cited by 18 publications

(20 citation statements)

References 56 publications

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“…Similar studies have also reported better overall image quality by Q.Clear compared with OSEM in [ 68 Ga]Ga-DOTANOC PET/CT scans [ 29 ], 18 F-fluciclovine PET/CT scans [ 20 ], and [ 68 Ga]Ga-PSMA PET/CT scans [ 30 ]. This indicates that the improved image quality provided by Q.Clear is not exclusively dedicated to 2-[ 18 F]FDG-PET/CT scans.…”

Section: Discussionmentioning

confidence: 72%

Comparison of Image Quality and Quantification Parameters between Q.Clear and OSEM Reconstruction Methods on FDG-PET/CT Images in Patients with Metastatic Breast Cancer

et al. 2023

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We compared the image quality and quantification parameters through bayesian penalized likelihood reconstruction algorithm (Q.Clear) and ordered subset expectation maximization (OSEM) algorithm for 2-[18F]FDG-PET/CT scans performed for response monitoring in patients with metastatic breast cancer in prospective setting. We included 37 metastatic breast cancer patients diagnosed and monitored with 2-[18F]FDG-PET/CT at Odense University Hospital (Denmark). A total of 100 scans were analyzed blinded toward Q.Clear and OSEM reconstruction algorithms regarding image quality parameters (noise, sharpness, contrast, diagnostic confidence, artefacts, and blotchy appearance) using a five-point scale. The hottest lesion was selected in scans with measurable disease, considering the same volume of interest in both reconstruction methods. SULpeak (g/mL) and SUVmax (g/mL) were compared for the same hottest lesion. There was no significant difference regarding noise, diagnostic confidence, and artefacts within reconstruction methods; Q.Clear had significantly better sharpness (p < 0.001) and contrast (p = 0.001) than the OSEM reconstruction, while the OSEM reconstruction had significantly less blotchy appearance compared with Q.Clear reconstruction (p < 0.001). Quantitative analysis on 75/100 scans indicated that Q.Clear reconstruction had significantly higher SULpeak (5.33 ± 2.8 vs. 4.85 ± 2.5, p < 0.001) and SUVmax (8.27 ± 4.8 vs. 6.90 ± 3.8, p < 0.001) compared with OSEM reconstruction. In conclusion, Q.Clear reconstruction revealed better sharpness, better contrast, higher SUVmax, and higher SULpeak, while OSEM reconstruction had less blotchy appearance.

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Section: Discussionmentioning

confidence: 72%

Comparison of Image Quality and Quantification Parameters between Q.Clear and OSEM Reconstruction Methods on FDG-PET/CT Images in Patients with Metastatic Breast Cancer

et al. 2023

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“…Generally, high image quality is essential to precise interpretation of PET/CT clinical studies. Bayesian penalized likelihood estimation reconstruction algorithm (BPL) has been developed and clinically implemented to improve image signal-to-noise ratio and lesion signal-to-background ratio compared to the widely used ordered subset expectation maximization (OSEM) for 68 Ga tracer PET [5] , which is partly accountable to the full iterative convergence of the BPL algorithm without excessive noise ampli cation.…”

Section: Introductionmentioning

confidence: 99%

Phantom and clinical evaluation of a new Bayesian penalized likelihood reconstruction algorithm HYPER Iterative on the image quality of 68Ga-DOTA-NOC PET/CT

Xu¹,

Cui

Li³

et al. 2022

Preprint

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Background Bayesian penalized likelihood (BPL) algorithm is an effective way to suppress the noise by incorporating a smooth penalty in the positron emission tomography (PET) image reconstruction process. The strength of the smooth penalty is controlled by the penalization factor. The aim was to investigate the impact of different penalization factor and acquisition time in a new BPL algorithm HYPER Iterative on the image quality of 68Ga-DOTA-NOC PET/CT. A phantom and 25 patients with neuroendocrine neoplasm underwent 68Ga-DOTA-NOC PET/CT were included. The PET data were acquired with a digital PET/CT in a list-mode and reconstructed by ordered subset expectation maximization (OSEM) and HYPER Iterative algorithm with seven penalization factors between 0.03 and 0.5 for 2 and 3 minutes-per-bed (m/b) acquisition, both including time of flight and point of spread function recovery. The contrast recovery (CR) and background variability (BV) of the phantom, SUVmean and coefficient of variation of liver (CV), and SUVmax of the lesions were measured. Image quality was ranked by two radiologists using the five-point Likert scale. Results The CR and BV decreases with the increased of penalization factor for four hot spheres, and HYPER Iterative 2 m/b groups with penalization factor 0.07 to 0.2 had equivalent CR and better BV performance compared to OSEM 3m/b group. The liver SUVmean was approximately equal in all reconstruction groups (range 5.95–5.97), and the liver CVs of HYPER Iterative 2 m/b and 3 m/b groups with the penalization factor of 0.1 to 0.2 were equivalent to OSEM 3 m/b group (p = 0.113–0.711 and p = 0.079–0.287), while the lesions SUVmax significantly increased 19–22% and 25% (all p < 0.001). The highest qualitative score was attained at the penalization factor of 0.2 for HYPER Iterative 2 m/b group (3.20 ± 0.52) and 3 m/b group (3.70 ± 0.36) which was comparable to or greater than OSEM 3m/b group (3.09 ± 0.36, p = 0.388 and p < 0.001). Conclusions HYPER Iterative algorithm with penalization factor of 0.2 resulted in higher lesion contrast and lower image noise in comparison with OSEM for 68Ga-DOTA-NOC PET/CT, which allows lower injected activity and shorter acquisition time with preserved image quality.

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“…Generally, high image quality is essential to the precise interpretation of PET/CT clinical studies. The Bayesian penalized likelihood (BPL) reconstruction algorithm has been developed and clinically implemented to improve the image signal-to-noise ratio and lesion signal-to-background ratio compared to the widely used ordered subset expectation maximization (OSEM) algorithm for 68 Ga tracers [ 5 ]; the superior performance of the BPL algorithm is due in part to its full iterative convergence without excessive noise amplification. Hence, the BPL algorithm has the potential to further improve quantitation accuracy [ 6 ], shorten acquisition time [ 7 ], and reduce the amount of radioactivity injected [ 8 ], while maintaining or even improving the image quality.…”

Section: Introductionmentioning

confidence: 99%

Phantom and clinical evaluation of the effect of a new Bayesian penalized likelihood reconstruction algorithm (HYPER Iterative) on 68Ga-DOTA-NOC PET/CT image quality

Cui

et al. 2022

EJNMMI Res

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Background Bayesian penalized likelihood (BPL) algorithm is an effective way to suppress noise in the process of positron emission tomography (PET) image reconstruction by incorporating a smooth penalty. The strength of the smooth penalty is controlled by the penalization factor. The aim was to investigate the impact of different penalization factors and acquisition times in a new BPL algorithm, HYPER Iterative, on the quality of 68Ga-DOTA-NOC PET/CT images. A phantom and 25 patients with neuroendocrine neoplasms who underwent 68Ga-DOTA-NOC PET/CT were included. The PET data were acquired in a list-mode with a digital PET/CT scanner and reconstructed by ordered subset expectation maximization (OSEM) and the HYPER Iterative algorithm with seven penalization factors between 0.03 and 0.5 for acquisitions of 2 and 3 min per bed position (m/b), both including time-of-flight and point of spread function recovery. The contrast recovery (CR), background variability (BV) and radioactivity concentration ratio (RCR) of the phantom; The SUVmean and coefficient of variation (CV) of the liver; and the SUVmax of the lesions were measured. Image quality was rated by two radiologists using a five-point Likert scale. Results The CR, BV, and RCR decreased with increasing penalization factors for four “hot” spheres, and the HYPER Iterative 2 m/b groups with penalization factors of 0.07 to 0.2 had equivalent CR and superior BV performance compared to the OSEM 3 m/b group. The liver SUVmean values were approximately equal in all reconstruction groups (range 5.95–5.97), and the liver CVs of the HYPER Iterative 2 m/b and 3 m/b groups with the penalization factors of 0.1 to 0.2 were equivalent to those of the OSEM 3 m/b group (p = 0.113–0.711 and p = 0.079–0.287, respectively), while the lesion SUVmax significantly increased by 19–22% and 25%, respectively (all p < 0.001). The highest qualitative score was attained at a penalization factor of 0.2 for the HYPER Iterative 2 m/b group (3.20 ± 0.52) and 3 m/b group (3.70 ± 0.36); those scores were comparable to or greater than that of the OSEM 3 m/b group (3.09 ± 0.36, p = 0.388 and p < 0.001, respectively). Conclusions The HYPER Iterative algorithm with a penalization factor of 0.2 resulted in higher lesion contrast and lower image noise than OSEM for 68Ga-DOTA-NOC PET/CT, allowing the same image quality to be achieved with less injected radioactivity and a shorter acquisition time.

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Impact of the Noise Penalty Factor on Quantification in Bayesian Penalized Likelihood (Q.Clear) Reconstructions of 68Ga-PSMA PET/CT Scans

Cited by 18 publications

References 56 publications

Comparison of Image Quality and Quantification Parameters between Q.Clear and OSEM Reconstruction Methods on FDG-PET/CT Images in Patients with Metastatic Breast Cancer

Comparison of Image Quality and Quantification Parameters between Q.Clear and OSEM Reconstruction Methods on FDG-PET/CT Images in Patients with Metastatic Breast Cancer

Phantom and clinical evaluation of a new Bayesian penalized likelihood reconstruction algorithm HYPER Iterative on the image quality of 68Ga-DOTA-NOC PET/CT

Phantom and clinical evaluation of the effect of a new Bayesian penalized likelihood reconstruction algorithm (HYPER Iterative) on 68Ga-DOTA-NOC PET/CT image quality

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