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.
Position Emission Tomography/Single-Photon Emission Tomography Neuroimaging for Detection of Premotor Parkinson's Disease
