Ultra-low-dose CT reconstructed with the artificial intelligence iterative reconstruction algorithm (AIIR) in (18)F-FDG total-body PET/CT examination: a preliminary study

PURPOSE: To investigate the feasibility of ultra-low-dose CT (ULDCT) reconstructed with the artificial intelligence iterative reconstruction (AIIR) algorithm in total-body PET/CT imaging. METHODS: The study included both the phantom and clinical parts. An anthropomorphic phantom underwent CT imaging with ULDCT (10mAs) and standard-dose CT (SDCT) (120mAs), respectively. ULDCT was reconstructed with AIIR and hybrid iterative reconstruction (HIR) (expressed as ULDCT-AIIR(phantom) and ULDCT-HIR(phantom)), respectively, and SDCT was reconstructed with HIR (SDCT-HIR(phantom)) as control. In the clinical part, 52 patients with malignant tumors underwent the total-body PET/CT scan. ULDCT with AIIR (ULDCT-AIIR) and HIR (ULDCT-HIR), respectively, was reconstructed for PET attenuation correction, followed by the SDCT reconstructed with HIR (SDCT-HIR) for anatomical location. PET/CT images’ quality was qualitatively assessed by two readers. The CT(mean), as well as the CT standard deviation (CT(sd)), SUV(max), SUV(mean), and the SUV standard deviation (SUV(sd)), was recorded. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated and compared. RESULTS: The image quality of ULDCT-HIR(phantom) was inferior to the SDCT-HIR(phantom), but no significant difference was found between the ULDCT-AIIR(phantom) and SDCT-HIR(phantom). The subjective score of ULDCT-AIIR in the neck, chest and lower limb was equivalent to that of SDCT-HIR. Besides the brain and lower limb, the change rates of CT(mean) in thyroid, neck muscle, lung, mediastinum, back muscle, liver, lumbar muscle, first lumbar spine and sigmoid colon were −2.15, −1.52, 0.66, 2.97, 0.23, 8.91, 0.06, −4.29 and 8.78%, respectively, while all CT(sd) of ULDCT-AIIR was lower than that of SDCT-HIR. Except for the brain, the CNR of ULDCT-AIIR was the same as the SDCT-HIR, but the SNR was higher. The change rates of SUV(max), SUV(mean) and SUV(sd) were within [Formula: see text] 3% in all ROIs. For the lesions, the SUV(max), SUV(sd) and TBR showed no significant difference between PET-AIIR and PET-HIR. CONCLUSION: The SDCT-HIR could not be replaced by the ULDCT-AIIR at date, but the AIIR algorithm decreased the image noise and increased the SNR, which can be implemented under special circumstances in PET/CT examination. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40658-022-00521-8.