Computed Tomography Radiation Dose Reduction: Effect of Different Iterative Reconstruction Algorithms on Image Quality

Objective: We evaluated the effects of hybrid and model-based iterative reconstruction (IR) algorithms from different vendors at multiple radiation dose levels on image quality of chest phantom scans. Methods: A chest phantom was scanned on state-of-the-art computed tomography scanners from 4 vendors at 4 dose levels (4.1 mGy, 3.0 mGy, 1.9 mGy, and 0.8 mGy). All data were reconstructed with filtered back projection (FBP) and reduced-dose data also with IR (iDose4, Adaptive Iterative Dose Reduction 3D, Adaptive Statistical Iterative Reconstruction, Sinogram-Affirmed Iterative Reconstruction, prototype Iterative Model Reconstruction, and Veo). Computed tomography numbers and noise were measured in the spine and lungs. Signal-to-noise ratios (SNR) and contrast-to-noise ratios (CNR) were calculated and differences were analyzed with the Friedman test. Results: For all vendors, radiation dose reduction with FBP resulted in significantly increased noise levels (<= 148%) as well as decreased SNR (<= 57%) and CNR (<= 58%) (P < 0.001). Conversely, IR resulted in decreased noise levels (<= 48%) as well as increased SNR (<= 94%) and CNR (<= 94%). The SNRs and CNRs of the model-based algorithms at 80% reduced dose were similar to reference-dose FBP. Conclusions: Hybrid IR algorithms have the potential to reduce radiation dose with 27% to 54% and model-based IR algorithms with up to 80%.