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

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 inve...
Ausführliche Beschreibung

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. Ausführliche Beschreibung

Autor*in:	Xu, Lei [verfasserIn] Cui, Can Li, Rushuai Yang, Rui Liu, Rencong Meng, Qingle Wang, Feng

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	PET Ga-DOTA-NOC Neuroendocrine neoplasm Image reconstruction Bayesian penalized likelihood Penalization factor

Anmerkung:	© The Author(s) 2022

Übergeordnetes Werk:	Enthalten in: EJNMMI Research - Berlin : Springer, 2011, 12(2022), 1 vom: 12. Dez.
Übergeordnetes Werk:	volume:12 ; year:2022 ; number:1 ; day:12 ; month:12

Links:	Volltext

DOI / URN:	10.1186/s13550-022-00945-4

Katalog-ID:	SPR048859435