Korean Journal of Radiology

  • 제22권6호
  • /
  • Pages.983-993
  • /
  • 2021
  • /
  • 1229-6929(pISSN)
  • /
  • 2005-8330(eISSN)
대한영상의학회 (The Korean Society of Radiology)
권호 표지
DOI QR코드

DOI QR Code

Cycle-Consistent Generative Adversarial Network: Effect on Radiation Dose Reduction and Image Quality Improvement in Ultralow-Dose CT for Evaluation of Pulmonary Tuberculosis

  • Chenggong Yan (Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University) ;
  • Jie Lin (Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University) ;
  • Haixia Li (Clinical and Technical Solution, Philips Healthcare) ;
  • Jun Xu (Department of Hematology, Nanfang Hospital, Southern Medical University) ;
  • Tianjing Zhang (Clinical and Technical Solution, Philips Healthcare) ;
  • Hao Chen (Jiangsu JITRI Sioux Technologies Co., Ltd.) ;
  • Henry C. Woodruff (The D-Lab, Department of Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University) ;
  • Guangyao Wu (The D-Lab, Department of Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University) ;
  • Siqi Zhang (Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University) ;
  • Yikai Xu (Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University) ;
  • Philippe Lambin (The D-Lab, Department of Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University)
  • 투고 : 2020.08.16
  • 심사 : 2020.12.21
  • 발행 : 2021.06.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

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.

키워드

과제정보

The authors thank Xiaochen Huai, MM, Philips Healthcare, China, as well as Jin Qi, PhD, School of Information and Communication Engineering, University of electronic science and technology of China, for their support in this study.

참고문헌

  1. Zumla A, George A, Sharma V, Herbert RH, Oxley A, Oliver M. The WHO 2014 global tuberculosis report-further to go. Lancet Glob Health 2015;3:e10-e12 
  2. Furin J, Cox H, Pai M. Tuberculosis. Lancet 2019;393:1642-1656 
  3. Jeong YJ, Lee KS. Pulmonary tuberculosis: up-to-date imaging and management. AJR Am J Roentgenol 2008;191:834-844 
  4. Sosa LE, Njie GJ, Lobato MN, Morris SB, Buchta W, Casey ML, et al. Tuberculosis screening, testing, and treatment of U.S. health care personnel: recommendations from the national tuberculosis controllers association and CDC, 2019. MMWR Morb Mortal Wkly Rep 2019;68:439-443 
  5. Kalra MK, Sodickson AD, Mayo-Smith WW. CT radiation: key concepts for gentle and wise use. Radiographics 2015;35:1706-1721 
  6. Stiller W. Basics of iterative reconstruction methods in computed tomography: a vendor-independent overview. Eur J Radiol 2018;109:147-154 
  7. Moloney F, James K, Twomey M, Ryan D, Grey TM, Downes A, et al. Low-dose CT imaging of the acute abdomen using model-based iterative reconstruction: a prospective study. Emerg Radiol 2019;26:169-177 
  8. Yamada Y, Jinzaki M, Tanami Y, Shiomi E, Sugiura H, Abe T, et al. Model-based iterative reconstruction technique for ultralow-dose computed tomography of the lung: a pilot study. Invest Radiol 2012;47:482-489 
  9. Yan C, Liang C, Xu J, Wu Y, Xiong W, Zheng H, et al. Ultralow-dose CT with knowledge-based iterative model reconstruction (IMR) in evaluation of pulmonary tuberculosis: comparison of radiation dose and image quality. Eur Radiol 2019;29:5358-5366 
  10. Shin YJ, Chang W, Ye JC, Kang E, Oh DY, Lee YJ, et al. Low-dose abdominal CT using a deep learning-based denoising algorithm: a comparison with CT reconstructed with filtered back projection or iterative reconstruction algorithm. Korean J Radiol 2020;21:356-364 
  11. Chen KT, Gong E, de Carvalho Macruz FB, Xu J, Boumis A, Khalighi M, et al. Ultra-low-dose 18F-florbetaben amyloid PET imaging using deep learning with multi-contrast MRI inputs. Radiology 2019;290:649-656 
  12. Zhao T, McNitt-Gray M, Ruan D. A convolutional neural network for ultra-low-dose CT denoising and emphysema screening. Med Phys 2019;46:3941-3950 
  13. Yin X, Zhao Q, Liu J, Yang W, Yang J, Quan G, et al. Domain progressive 3D residual convolution network to improve low-dose CT imaging. IEEE Trans Med Imaging 2019;38:2903-2913 
  14. Finck T, Li H, Grundl L, Eichinger P, Bussas M, Muhlau M, et al. Deep-learning generated synthetic double inversion recovery images improve multiple sclerosis lesion detection. Invest Radiol 2020;55:318-323 
  15. Kaplan S, Zhu YM. Full-dose PET image estimation from low-dose PET image using deep learning: a pilot study. J Digit Imaging 2019;32:773-778 
  16. Liang X, Chen L, Nguyen D, Zhou Z, Gu X, Yang M, et al. Generating synthesized computed tomography (CT) from cone-beam computed tomography (CBCT) using CycleGAN for adaptive radiation therapy. Phys Med Biol 2019;64:125002 
  17. Wolterink JM, Leiner T, Viergever MA, Isgum I. Generative adversarial networks for noise reduction in low-dose CT. IEEE Trans Med Imaging 2017;36:2536-2545 
  18. Yang Q, Yan P, Zhang Y, Yu H, Shi Y, Mou X, et al. Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss. IEEE Trans Med Imaging 2018;37:1348-1357 
  19. Pontana F, Billard AS, Duhamel A, Schmidt B, Faivre JB, Hachulla E, et al. Effect of iterative reconstruction on the detection of systemic sclerosis-related interstitial lung disease: clinical experience in 55 patients. Radiology 2016;279:297-305 
  20. Padole A, Digumarthy S, Flores E, Madan R, Mishra S, Sharma A, et al. Assessment of chest CT at CTDIvol less than 1mGy with iterative reconstruction techniques. Br J Radiol 2017;90:20160625 
  21. Ye K, Zhu Q, Li M, Lu Y, Yuan H. A feasibility study of pulmonary nodule detection by ultralow-dose CT with adaptive statistical iterative reconstruction-V technique. Eur J Radiol 2019;119:108652 
  22. Tang H, Liu Z, Hu Z, He T, Li D, Yu N, et al. Clinical value of a new generation adaptive statistical iterative reconstruction (ASIR-V) in the diagnosis of pulmonary nodule in low-dose chest CT. Br J Radiol 2019;92:20180909 
  23. Hwang EJ, Park CM. Clinical implementation of deep learning in thoracic radiology: potential applications and challenges. Korean J Radiol 2020;21:511-525 
  24. Yan C, Xu J, Liang C, Wei Q, Wu Y, Xiong W, et al. Radiation dose reduction by using CT with iterative model reconstruction in patients with pulmonary invasive fungal infection. Radiology 2018;288:285-292 
  25. Messerli M, Ottilinger T, Warschkow R, Leschka S, Alkadhi H, Wildermuth S, et al. Emphysema quantification and lung volumetry in chest X-ray equivalent ultralow dose CT - Intra-individual comparison with standard dose CT. Eur J Radiol 2017;91:1-9 
  26. Padole A, Singh S, Ackman JB, Wu C, Do S, Pourjabbar S, et al. Submillisievert chest CT with filtered back projection and iterative reconstruction techniques. AJR Am J Roentgenol 2014;203:772-781 
  27. Ouyang J, Chen KT, Gong E, Pauly J, Zaharchuk G. Ultra-low-dose PET reconstruction using generative adversarial network with feature matching and task-specific perceptual loss. Med Phys 2019;46:3555-3564 
  28. Yi X, Babyn P. Sharpness-aware low-dose CT denoising using conditional generative adversarial network. J Digit Imaging 2018;31:655-669 
  29. Kang E, Koo HJ, Yang DH, Seo JB, Ye JC. Cycle-consistent adversarial denoising network for multiphase coronary CT angiography. Med Phys 2019;46:550-562 
  30. Koike Y, Akino Y, Sumida I, Shiomi H, Mizuno H, Yagi M, et al. Feasibility of synthetic computed tomography generated with an adversarial network for multi-sequence magnetic resonance-based brain radiotherapy. J Radiat Res 2020;6:92-103 
  31. Tie X, Lam SK, Zhang Y, Lee KH, Au KH, Cai J. Pseudo-CT generation from multi-parametric MRI using a novel multi-channel multi-path conditional generative adversarial network for nasopharyngeal carcinoma patients. Med Phys 2020;47:1750-1762 
  32. Kida S, Kaji S, Nawa K, Imae T, Nakamoto T, Ozaki S, et al. Visual enhancement of cone-beam CT by use of CycleGAN. Med Phys 2020;47:998-1010