Impact of hybrid iterative reconstruction on unenhanced liver CT

Improving image quality and lung nodule detection for low-dose chest CT by using generative adversarial network reconstruction

OBJECTIVES

To investigate the improvement of two denoising models with different learning targets (Dir and Res) of generative adversarial network (GAN) on image quality and lung nodule detectability in chest low-dose CT (LDCT).

METHODS

In training phase, by using LDCT images simulated from standard dose CT (SDCT) of 200 participants, Dir model was trained targeting SDCT images, while Res model targeting the residual between SDCT and LDCT images. In testing phase, a phantom and 95 chest LDCT, exclusively with training data, were included for evaluation of imaging quality and pulmonary nodules detectability.

RESULTS

For phantom images, structural similarity, peak signal-to-noise ratio of both Res and Dir models were higher than that of LDCT. Standard deviation of Res model was the lowest. For patient images, image noise and quality of both two models, were better than that of LDCT. Artifacts of Res model was less than that of LDCT. The diagnostic sensitivity of lung nodule by two readers for LDCT, Res and Dir model, were 72/77%, 79/83% and 72/79% respectively.

CONCLUSION

Two GAN denoising models, including Res and Dir trained with different targets, could effectively reduce image noise of chest LDCT. The image quality evaluation scoring and nodule detectability of Res denoising model was better than that of Dir denoising model and that of hybrid IR images.

ADVANCES IN KNOWLEDGE

The GAN-trained model, which learned the residual between SDCT and LDCT images, reduced image noise and increased the lung nodule detectability by radiologists on chest LDCT. This demonstrates the potential for clinical benefit.

Improved visualization of a fine intrahepatic biliary duct on drip infusion cholangiography-computed tomography: Impact of knowledge-based iterative model reconstruction

AIM

The purpose of this study was to investigate the visualization of fine biliary ducts with knowledge-based iterative model reconstruction (IMR) in low-dose drip infusion computed tomography (CT) cholangiography (DIC-CT) as compared with filtered back projection (FBP) and hybrid iterative reconstruction (iDose⁴ ).

METHODS

A total of 38 patients underwent DIC-CT for living donor liver transplantation. CT was performed approximately 20 min after the end of the infusion of meglumine iotroxate (100 mL). Images were reconstructed using FBP, iDose⁴ , and IMR, and 1-mm slice images at fixed window level and width were prepared for assessment. Two reviewers independently evaluated the quality of visualization of the fine biliary ducts of the caudate lobe (B1) using a 5-point scale. The visualization scores of three reconstructed images were compared using the Kruskal-Wallis test and Mann-Whitney U-test.

RESULTS

For reviewer 1, the visualization score of IMR was significantly higher than that of FBP (P = 0.012), and tended to be higher than that of iDose⁴ (P = 0.078). For reviewer 2, the visualization score of IMR was significantly higher than those of both FBP and iDose⁴ (P < 0.01).

CONCLUSIONS

IMR showed better visualization of B1 on DIC-CT than FBP or iDose⁴ . DIC-CT reconstructed with IMR may be useful to the anatomical grasp of biliary tracts in cases of hepatectomy.