TY - GEN
T1 - Low-Dose CT Image Denoising Using Cycle-Consistent Adversarial Networks
AU - Li, Zeheng
AU - Huang, Junzhou
AU - Yu, Lifeng
AU - Chi, Yujie
AU - Jin, Mingwu
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - Computed tomography (CT) has been widely used in modern medical diagnosis and treatment. However, ionizing radiation of CT for a large population of patients becomes a concern. Low-dose CT is actively pursued to reduce harmful radiation, but faces challenges of elevated noise in images. To address this problem and improve low-dose CT image quality, we develop an image-domain denoising method based on cycle-consistent adversarial networks (CycleGAN). Different from previous deep learning based denoising methods, CycleGAN can learn data distribution of organ structures from unpaired full-dose and low-dose images, i.e. there is no one-to-one correspondence between full-dose and low-dose images. This is an important development of learning-based methods for low-dose CT since it enables the model growth using previously acquired full-dose images and later acquired low-dose images from different patients. As a proof-of-concept study, we used the NIH-AAPM-Mayo Clinic Low Dose CT Grand Challenge data to test our CycleGAN denoising method. The results show that the proposed method not only achieves better peak signal-to-noise ratio (PSNR) for quarter-dose images than non-local mean and dictionary learning denoising methods, but also preserves more details reflected by images and structural similarity index (SSIM). Our investigation also reveals that a larger sample size leads to a better denoising performance for CycleGAN.
AB - Computed tomography (CT) has been widely used in modern medical diagnosis and treatment. However, ionizing radiation of CT for a large population of patients becomes a concern. Low-dose CT is actively pursued to reduce harmful radiation, but faces challenges of elevated noise in images. To address this problem and improve low-dose CT image quality, we develop an image-domain denoising method based on cycle-consistent adversarial networks (CycleGAN). Different from previous deep learning based denoising methods, CycleGAN can learn data distribution of organ structures from unpaired full-dose and low-dose images, i.e. there is no one-to-one correspondence between full-dose and low-dose images. This is an important development of learning-based methods for low-dose CT since it enables the model growth using previously acquired full-dose images and later acquired low-dose images from different patients. As a proof-of-concept study, we used the NIH-AAPM-Mayo Clinic Low Dose CT Grand Challenge data to test our CycleGAN denoising method. The results show that the proposed method not only achieves better peak signal-to-noise ratio (PSNR) for quarter-dose images than non-local mean and dictionary learning denoising methods, but also preserves more details reflected by images and structural similarity index (SSIM). Our investigation also reveals that a larger sample size leads to a better denoising performance for CycleGAN.
KW - CycleGAN
KW - Low dose CT
KW - image denoising
UR - http://www.scopus.com/inward/record.url?scp=85083564701&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85083564701&partnerID=8YFLogxK
U2 - 10.1109/NSS/MIC42101.2019.9059965
DO - 10.1109/NSS/MIC42101.2019.9059965
M3 - Conference contribution
AN - SCOPUS:85083564701
T3 - 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2019
BT - 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2019
Y2 - 26 October 2019 through 2 November 2019
ER -