TY - JOUR
T1 - Cycle-Consistent Generative Adversarial Network: Effect on Radiation Dose Reduction and Image Quality Improvement in Ultralow-Dose CT for Evaluation of Pulmonary Tuberculosis
AU - Yan, C.G.
AU - Lin, J.
AU - Li, H.X.
AU - Xu, J.
AU - Zhang, T.J.
AU - Chen, H.
AU - Woodruff, H.C.
AU - Wu, G.Y.
AU - Zhang, S.Q.
AU - Xu, Y.K.
AU - Lambin, P.
PY - 2021/6/1
Y1 - 2021/6/1
N2 - Objective: To investigate the image quality of ultralow-dose CT (ULDCT) of the chest reconstructed using a cycle-consistent generative adversarial network (CycleGAN)-based deep learning method in the evaluation of pulmonary tuberculosis. Materials and Methods: Between June 2019 and November 2019, 103 patients (mean age, 40.8 +/- 13.6 years; 61 men and 42 women) with pulmonary tuberculosis were prospectively enrolled to undergo standard-dose CT (120 kVp with automated exposure control), followed immediately by ULDCT (80 kVp and 10 mAs). The images of the two successive scans were used to train the CycleGAN framework for image-to-image translation. The denoising efficacy of the CycleGAN algorithm was compared with that of hybrid and model-based iterative reconstruction. Repeated-measures analysis of variance and Wilcoxon signed-rank test were performed to compare the objective measurements and the subjective image quality scores, respectively. Results: With the optimized CycleGAN denoising model, using the ULDCT images as input, the peak signal-to-noise ratio and structural similarity index improved by 2.0 dB and 0.21, respectively. The CycleGAN-generated denoised ULDCT images typically provided satisfactory image quality for optimal visibility of anatomic structures and pathological findings, with a lower level of image noise (mean +/- standard deviation [SD], 19.5 +/- 3.0 Hounsfield unit [HU]) than that of the hybrid (66.3 +/- 10.5 HU, p < 0.001) and a similar noise level to model-based iterative reconstruction (19.6 +/- 2.6 HU, p > 0.908). The CycleGAN-generated images showed the highest contrast-to-noise ratios for the pulmonary lesions, followed by the model-based and hybrid iterative reconstruction. The mean effective radiation dose of ULDCT was 0.12 mSv with a mean 93.9% reduction compared to standard-dose CT. Conclusion: The optimized CycleGAN technique may allow the synthesis of diagnostically acceptable images from ULDCT of the chest for the evaluation of pulmonary tuberculosis.
AB - Objective: To investigate the image quality of ultralow-dose CT (ULDCT) of the chest reconstructed using a cycle-consistent generative adversarial network (CycleGAN)-based deep learning method in the evaluation of pulmonary tuberculosis. Materials and Methods: Between June 2019 and November 2019, 103 patients (mean age, 40.8 +/- 13.6 years; 61 men and 42 women) with pulmonary tuberculosis were prospectively enrolled to undergo standard-dose CT (120 kVp with automated exposure control), followed immediately by ULDCT (80 kVp and 10 mAs). The images of the two successive scans were used to train the CycleGAN framework for image-to-image translation. The denoising efficacy of the CycleGAN algorithm was compared with that of hybrid and model-based iterative reconstruction. Repeated-measures analysis of variance and Wilcoxon signed-rank test were performed to compare the objective measurements and the subjective image quality scores, respectively. Results: With the optimized CycleGAN denoising model, using the ULDCT images as input, the peak signal-to-noise ratio and structural similarity index improved by 2.0 dB and 0.21, respectively. The CycleGAN-generated denoised ULDCT images typically provided satisfactory image quality for optimal visibility of anatomic structures and pathological findings, with a lower level of image noise (mean +/- standard deviation [SD], 19.5 +/- 3.0 Hounsfield unit [HU]) than that of the hybrid (66.3 +/- 10.5 HU, p < 0.001) and a similar noise level to model-based iterative reconstruction (19.6 +/- 2.6 HU, p > 0.908). The CycleGAN-generated images showed the highest contrast-to-noise ratios for the pulmonary lesions, followed by the model-based and hybrid iterative reconstruction. The mean effective radiation dose of ULDCT was 0.12 mSv with a mean 93.9% reduction compared to standard-dose CT. Conclusion: The optimized CycleGAN technique may allow the synthesis of diagnostically acceptable images from ULDCT of the chest for the evaluation of pulmonary tuberculosis.
KW - Computed tomography
KW - Radiation dose
KW - Infection
KW - Artificial intelligence
KW - Deep learning
KW - FILTERED BACK-PROJECTION
KW - ITERATIVE RECONSTRUCTION
KW - COMPUTED-TOMOGRAPHY
U2 - 10.3348/kjr.2020.0988
DO - 10.3348/kjr.2020.0988
M3 - Article
C2 - 33739634
SN - 1229-6929
VL - 22
SP - 983
EP - 993
JO - Korean Journal of Radiology
JF - Korean Journal of Radiology
IS - 6
ER -