Acquisition Protocols to Optimize ⁶⁸Ga-DOTA-NOC Position Emission Tomography/Computer Tomography Image Quality Based on Patient Body Mass Index and Injected Dose

Abstract

Background: The choice of 68Ga-DOTA-1-Nal3-octreotide (68Ga-DOTA-NOC) injected dose and Position emission tomography/computer tomography (PET/CT) acquisition time is still a challenge for obtaining consistently high-quality PET image. Objective: To determine the optimal acquisition protocols based on patient body mass index (BMI) and the injected dose per kilogram for 68Ga-DOTA-NOC PET/CT imaging. Patients and Methods: This was a retrospective analysis of 51 patients (21 males and 30 females) who underwent clinical 68Ga-DOTANOC PET/CT imaging from November 2016 to March 2018 in Nanjing first hospital, the average BMI of these patients was 23.18 ± 3.45 kg/m2 with injected dose of 39.55–110.11 MBq. The study population was classified into groups based on Chinese standard BMI and injected dose. PET image quality and acquisition time were evaluated by coefficient of variance (CV) in the liver slice. Results: (1) The CV significantly increased with increasing weight and BMI (r = 0.647, 0.483, all P < 0.01), and significantly decreased with increasing injected dose per kilogram (r = 0 695, P < 0.01). (2) The CV differed significantly among 4 BMI-based groups, except for normal-weight group versus overweight group and overweight group versus obese group (P < 0.01), and the ratio of overweight group and obese group to normal weight group was approximately 1.1 and 1.2, respectively. Meanwhile, the CV had a significant statistical difference among 3 injected dose per kilogram groups (P < 0.01), and the ratio of that for low dose group and high dose group to moderate dose group was approximately 1.2 and 0.8. Conclusion: The findings showed a feasibility of obtaining consistently high-quality PET image at low injected dose and shorter acquisition time. Estimation of optimal acquisition time and injected dose using CV is valid in improving PET image quality, which can provide reference for the establishment and promotion of 68Ga-DOTA-NOC imaging protocols in China.

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

“…In addition, further increasing penalization factor might produce smoother images, but result in less contrast enhancement, pointing out that this noise level should be considered as the target noise setting for acquiring high quality images in 68 Ga-DOTA-NOC PET/CT practice. This phenomenon was consistent with previous studies using BPL algorithm on 68 Ga tracer PET with high penalization factor [12,18] .…”

“…Minor change of average normalized SUV max was found for large lesions in all HYPER iterative groups, and the minor change was also found for the medium lesions. The mean of normalized SUV max was higher than research and PET detection performance [12] . The mean of normalized SUV max for high injected activity groups was slightly higher than that for low injected activity in all HYPER iterative groups except for HR2.3-H2.5 (Figure 3b).…”

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

“…By current procedure guidelines, the recommended activity of for 68 Ga-DOTA-conjugated peptides ranges from 100 to 300 MBq depending on the PET imaging characteristics [ 16 , 17 ]. Our data showed that, regardless of the injected activity per kilogram, HYPER Iterative had a higher lesion SUV max than OSEM, but the penalization factor had a limited influence on the increase in lesion SUV max between HYPER Iterative groups with the same acquisition time, and the lesion SUV max increased slightly as acquisition time was increased from 2 m/b to 3 m/b.…”

Intelligent recognition system of converter station operation text based on computer image acquisition

“…In the image acquisition and processing system, the system design usually follows three design modes: hardware acquisition and software processing mode, separated hardware acquisition and processing mode and integrated hardware acquisition and processing mode. The three schemes have their own characteristics and are suitable for different occasions [1] . Video is a vivid description of objective things, an intuitive and concrete form of information expression, and the most important information carrier of human beings.…”