The Impact of Different Scanning Methods and Reconstruction Algorithms on CT Image Quality

Purpose: To evaluate the image quality of deep learning-based image reconstruction (DLIR) algorithm in unenhanced abdominal low-dose CT (LDCT). Methods: CT images of a phantom were reconstructed with Hybrid iterative reconstruction and deep learning image reconstruction (DLIR). The noise power spectrum (NPS) and task transfer function (TTF) were measured. Two patient groups were included in this study: consecutive patients who underwent unenhanced abdominal standard-dose CT reconstructed with hybrid iterative reconstruction (SDCT group) and consecutive patients who underwent unenhanced abdominal LDCT reconstructed of HIR and DLIR (LDCT group). The CT values, standard deviation (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) of the hepatic parenchyma and paraspinal muscle and abdominal subcutaneous fat were evaluated. Radiologists assessed the subjective image quality and lesion diagnostic confidence using a 5-point Likert scale. Quantitative and qualitative parameters were compared between SDCT and LDCT groups.