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

Abstract 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 part. 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-AIIRphantom and ULDCT-HIRphantom) respectively, and SDCT was reconstructed with HIR (SDCT-HIRphantom) as control. In the clinical part, 52 patients with malignant tumours underwent the total-body PET/CT scann. 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 were qualitatively assessed by two readers. The CTmean as well as the CT standard deviation (CTsd), SUVmax, SUVmean, and the SUV standard deviation (SUVsd) were recorded. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated and compared.Results: The image quality of ULDCT-HIRphantom was inferior to the SDCT-HIRphantom, but no significant difference was found between the ULDCT-AIIRphantom and SDCT-HIRphantom. The subjective score of ULDCT-AIIR in the neck, throax and lower limb was equivalent to that of SDCT-HIR. Besides the brain and lower limb, the CTmean of the lesion and other regiongs in ULDCT-AIIR didn’t fluctuate too much, while the CTsd 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 SUVmax, SUVmean and SUVsd of PET-AIIR had a slightly change in the whole body. For the lesions, the SUVmax, SUVsd and TBR showed no significant difference between PET-AIIR and PET-HIR. Conclusion: The SDCT-HIR was not replaced by the ULDCT-AIIR, but the AIIR algorithm decreased the image noise and increased the SNR, which can be implemented under special circumstance in PET/CT examination.