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

Lei Xu; Can Cui; Rushuai Li; Rui Yang; Rencong Liu; Qingle Meng; Feng Wang

doi:10.1186/s13550-022-00945-4

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

EJNMMI Res. 2022 Dec 12;12(1):73. doi: 10.1186/s13550-022-00945-4.

Authors

Lei Xu^#¹, Can Cui^#², Rushuai Li¹, Rui Yang¹, Rencong Liu¹, Qingle Meng³, Feng Wang⁴

Affiliations

¹ Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, 210006, Jiangsu, China.
² Department of PET/CT Center, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, 210009, Jiangsu, China.
³ Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, 210006, Jiangsu, China. qingle.meng@163.com.
⁴ Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, 210006, Jiangsu, China. fengwangcn@njmu.edu.cn.

^# Contributed equally.

Abstract

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 SUV_mean and coefficient of variation (CV) of the liver; and the SUV_max 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 SUV_mean 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 SUV_max 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.

Keywords: 68Ga-DOTA-NOC; Bayesian penalized likelihood; Image reconstruction; Neuroendocrine neoplasm; PET; Penalization factor.

Abstract

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