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

Yan Hu; Zhe Zheng; Haojun Yu; Jingyi Wang; Xinlan Yang; Hongcheng Shi

doi:10.1186/s40658-022-00521-8

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

EJNMMI Phys. 2023 Jan 2;10(1):1. doi: 10.1186/s40658-022-00521-8.

Authors

Yan Hu^{1

2

3}, Zhe Zheng^{1

2

3}, Haojun Yu^{1

2

3}, Jingyi Wang⁴, Xinlan Yang⁴, Hongcheng Shi^{5

6

7}

Affiliations

¹ Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Rd, Shanghai, 200032, China.
² Nuclear Medicine Institute of Fudan University, Shanghai, 200032, China.
³ Shanghai Institute of Medical Imaging, Shanghai, 200032, China.
⁴ United Imaging Healthcare Co., Ltd., Shanghai, China.
⁵ Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Rd, Shanghai, 200032, China. shi.hongcheng@zs-hospital.sh.cn.
⁶ Nuclear Medicine Institute of Fudan University, Shanghai, 200032, China. shi.hongcheng@zs-hospital.sh.cn.
⁷ Shanghai Institute of Medical Imaging, Shanghai, 200032, China. shi.hongcheng@zs-hospital.sh.cn.

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 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.

Keywords: Artificial intelligence iterative reconstruction; PET/CT; Standard-dose; Ultra-low-dose.

Abstract

Grants and funding