The British journal of radiology | 2021

Low-dose whole-body CT using deep learning image reconstruction: image quality and lesion detection.

 
 
 
 
 
 
 
 

Abstract


OBJECTIVES\nTo evaluate image quality and lesion detection capabilities of low-dose (LD) portal venous phase whole-body CT using deep learning image reconstruction (DLIR).\n\n\nMETHODS\nThe 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.\n\n\nRESULTS\nThe 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).\n\n\nCONCLUSION\nDLIR 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.\n\n\nADVANCES IN KNOWLEDGE\nDeep 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.

Volume None
Pages \n 20201329\n
DOI 10.1259/bjr.20201329
Language English
Journal The British journal of radiology

Full Text