TY - GEN
T1 - Deep-Learning Based Super-Resolution for Low-Dose CT
AU - Zhou, Shiwei
AU - Yu, Lifeng
AU - Jin, Mingwu
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Recently we proposed a texture transformer network for the image super-resolution (TTSR), a reference-based deep learning super-resolution method, to improve the spatial resolution and suppress the noise of computed tomography (CT) images, where its effectiveness was demonstrated using the XCAT phantom. In this work, we focus on developing TTSR for real patient CT data. The mapping from the noisy low-resolution CT (LRCT) images to the clean high-resolution CT (HRCT) images is learned through TTSR. First, we iteratively reconstruct HRCT (full-dose, full projection data, 512x512 image resolution) and LRCT (quarter dose, 1/4 of all projection data, 128x128 image resolution) images using the CT projection data from 10 patients. Then, the TTSR models are trained using LRCT and HRCT images from nine patients to establish the translation from LRCT images to HRCT images. Finally, the SR model performance is quantitatively evaluated by peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) using the test patient images. Cubic spline interpolation and a generative adversarial network (GAN) with cycle consistency ("GAN-CIRCLE") are used for comparison. TTSR can restore more details in SRCT images than interpolation and GAN-CIRCLE. The quantitative results of PSNR and SSIM also show that TTSR can achieve the best performance. We finally show that TTSR using only 1/4 of all projection data can outperform LDCT with advanced denoising using all projection data. This development paves a way for deep learning-based SR for CT with lessened requirement on the number of detectors and much reduced computation time.
AB - Recently we proposed a texture transformer network for the image super-resolution (TTSR), a reference-based deep learning super-resolution method, to improve the spatial resolution and suppress the noise of computed tomography (CT) images, where its effectiveness was demonstrated using the XCAT phantom. In this work, we focus on developing TTSR for real patient CT data. The mapping from the noisy low-resolution CT (LRCT) images to the clean high-resolution CT (HRCT) images is learned through TTSR. First, we iteratively reconstruct HRCT (full-dose, full projection data, 512x512 image resolution) and LRCT (quarter dose, 1/4 of all projection data, 128x128 image resolution) images using the CT projection data from 10 patients. Then, the TTSR models are trained using LRCT and HRCT images from nine patients to establish the translation from LRCT images to HRCT images. Finally, the SR model performance is quantitatively evaluated by peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) using the test patient images. Cubic spline interpolation and a generative adversarial network (GAN) with cycle consistency ("GAN-CIRCLE") are used for comparison. TTSR can restore more details in SRCT images than interpolation and GAN-CIRCLE. The quantitative results of PSNR and SSIM also show that TTSR can achieve the best performance. We finally show that TTSR using only 1/4 of all projection data can outperform LDCT with advanced denoising using all projection data. This development paves a way for deep learning-based SR for CT with lessened requirement on the number of detectors and much reduced computation time.
UR - http://www.scopus.com/inward/record.url?scp=85185380810&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85185380810&partnerID=8YFLogxK
U2 - 10.1109/NSS/MIC44845.2022.10398997
DO - 10.1109/NSS/MIC44845.2022.10398997
M3 - Conference contribution
AN - SCOPUS:85185380810
T3 - 2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference
BT - 2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE Nuclear Science Symposium, Medical Imaging Conference, and Room Temperature Semiconductor Detector Conference, IEEE NSS MIC RTSD 2022
Y2 - 5 November 2022 through 12 November 2022
ER -