In The British journal of radiology
OBJECTIVES : To evaluate image quality and lesion detection capabilities of low-dose (LD) portal venous phase whole-body CT using deep learning image reconstruction (DLIR).
METHODS : The study cohort of 59 consecutive patients (mean age 67.2 years) who underwent whole-body LD CT and a prior standard-dose (SD) CT reconstructed with hybrid iterative reconstruction (SD-IR) within one-year for surveillance of malignancy were assessed. The LD CT images were reconstructed with hybrid iterative reconstruction of 40% (LD-IR) and DLIR (LD-DLIR). The radiologists independently evaluated image quality (5-point scale) and lesion detection. Attenuation values in Hounsfield units (HU) of the liver, pancreas, spleen, abdominal aorta, and portal vein; the background noise and signal-to-noise ratio (SNR) of the liver, pancreas, and spleen were calculated. Qualitative and quantitative parameters were compared between the SD-IR, LD-IR, and LD-DLIR images. The CT dose-index volumes (CTDIvol) and dose-length product (DLP) were compared between SD and LD scans.
RESULTS : The image quality and lesion detection rate of the LD-DLIR was comparable to the SD-IR. The image quality was significantly better in SD-IR than in LD-IR (p < 0.017). The attenuation values of all anatomical structures were comparable between the SD-IR and LD-DLIR (p = 0.28-0.96). However, background noise was significantly lower in the LD-DLIR (p < 0.001) and resulted in improved SNRs (p < 0.001) compared to the SD-IR and LD-IR images. The mean CTDIvol and DLP were significantly lower in the LD (2.9 mGy and 216.2 mGy·cm) than in the SD (13.5 mGy and 1011.6 mGy·cm) (p < 0.0001).
CONCLUSION : DLIR reconstructed low dose CT images enable radiation dose reduction of >75% while maintaining image quality and lesion detection rate and superior SNR in comparison to SD-IR.
ADVANCES IN KNOWLEDGE : Deep learning image reconstruction algorithm enables around 80% reduction in radiation dose while maintaining the image quality and lesion detection compared to standard-dose whole-body computed tomography.
Noda Yoshifumi, Kaga Tetsuro, Kawai Nobuyuki, Miyoshi Toshiharu, Kawada Hiroshi, Hyodo Fuminori, Kambadakone Avinash, Matsuo Masayuki