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.

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.