Objectives: To investigate the differences in brain computed tomography (CT) attenuation with deep-learning reconstruction (DLR) versus conventional reconstruction methods using a bias-free approach. Methods: Unenhanced brain CT scans of 27 participants (11 males and 16 females; mean age: 46. 4 y) without brain abnormalities were included. The scans were reconstructed using 4 methods: hybrid iterative reconstruction (Hybrid-IR), DLRₘild, DLRₛtandard, and DLRₛtrong. All CT images were normalized to Montreal Neurological Institute coordinates, and CT attenuation values and signal-to-noise ratios (SNRs) were measured using 22 white and 10 gray-matter regions of interest. The edge rise distance (ERD) was measured in the right and left lateral ventricles. Results: The mean attenuation between Hybrid-IR and DLR ROIs showed strong correlations in both white (R =0. 953 to 0. 964) and gray (R =0. 914 to 0. 943) matter. The Bland-Altman analysis showed greater attenuation with DLR than with Hybrid-IR, with mean biases of 2. 27 to 2. 40 in white matter and 1. 78 to 1. 84 in gray matter. The median SNRs were highest for DLRₛtrong, followed by DLRₛtandard, DLRₘild, and Hybrid-IR. The median ERD of DLRₘild 2. 86 (pixels) was the lowest, followed by DLRₛtandard (3. 27), Hybrid-IR (3. 27), and DLRₛtrong (3. 55). The Freedman test revealed significant differences based on the reconstruction method (P =0. 0080). Conclusions: The attenuation values for Hybrid-IR and DLR were strongly correlated, with DLR showing higher attenuation.
Yamakuni et al. (Mon,) studied this question.