In:
The British Journal of Radiology, British Institute of Radiology, Vol. 96, No. 1149 ( 2023-09)
Abstract:
Our work aims to study the feasibility of a deep learning algorithm to reduce the 68 Ga-FAPI radiotracer injected activity and/or shorten the scanning time and to investigate its effects on image quality and lesion detection ability. Methods: The data of 130 patients who underwent 68 Ga-FAPI positron emission tomography (PET)/CT in two centers were studied. Predicted full-dose images (DL-22%, DL-28% and DL-33%) were obtained from three groups of low-dose images using a deep learning method and compared with the standard-dose images (raw data). Injection activity for full-dose images was 2.16 ± 0.61 MBq/kg. The quality of the predicted full-dose PET images was subjectively evaluated by two nuclear physicians using a 5-point Likert scale, and objectively evaluated by the peak signal-to-noise ratio, structural similarity index and root mean square error. The maximum standardized uptake value and the mean standardized uptake value (SUVmean) were used to quantitatively analyze the four volumes of interest (the brain, liver, left lung and right lung) and all lesions, and the lesion detection rate was calculated. Results: Data showed that the DL-33% images of the two test data sets met the clinical diagnosis requirements, and the overall lesion detection rate of the two centers reached 95.9%. Conclusion: Through deep learning, we demonstrated that reducing the 68 Ga-FAPI injected activity and/or shortening the scanning time in PET/CT imaging was feasible. In addition, 68 Ga-FAPI dose as low as 33% of the standard dose maintained acceptable image quality. Advances in knowledge: This is the first study of low-dose 68 Ga-FAPI PET images from two centers using a deep learning algorithm.
Type of Medium:
Online Resource
ISSN:
0007-1285
,
1748-880X
DOI:
10.1259/bjr.20230038
Language:
English
Publisher:
British Institute of Radiology
Publication Date:
2023
detail.hit.zdb_id:
1468548-6
