Journal of Applied Optics, Volume. 46, Issue 2, 292(2025)
Low-dose CT denoising using combination of multi-scale residuals and global attention
[1] PINSKY P F, LYNCH D A, GIERADA D S. Incidental findings on low-dose CT lung cancer screenings and deaths from respiratory diseases[J]. Chest, 161, 1092-1100(2022).
[3] ZHU Siqi, WANG Jue, CAI Yufang. Low-dose CT denoising algorithm based on improved cycle GAN[J]. Acta Optica Sinica, 40, 70-78(2020).
[4] CHEN Y, DAI X, DUAN H et al. A quality improvement method for lung LDCT images[J]. Journal of X-ray Science and Technology, 28, 255-270(2020).
[5] LIU J, MA J, ZHANG Y et al. Discriminative feature representation to improve projection data inconsistency for low dose CT imaging[J]. IEEE Transactions on Medical Imaging, 36, 2499-2509(2017).
[6] MANDUCA A, YU L, TRZASKO J D et al. Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT[J]. Medical Physics, 36, 4911-4919(2009).
[7] BALDA M, HORNEGGER J, HEISMANN B. Ray contribution masks for structure adaptive sinogram filtering[J]. IEEE Transactions on Medical Imaging, 31, 1228-1239(2011).
[8] LIU Y, CHEN Y, CHEN P et al. Artifact suppressed nonlinear diffusion filtering for low-dose CT image processing[J]. IEEE Access, 7, 9856-9869(2019).
[10] LIU Y, MA J, FAN Y et al. Adaptive-weighted total variation minimization for sparse data toward low-dose X-ray computed tomography image reconstruction[J]. Physics in Medicine and Biology, 57, 23-56(2012).
[11] XU Q, YU H, MOU X et al. Low-dose X-ray CT reconstruction via dictionary learning[J]. IEEE Transactions on Medical Imaging, 31, 1682-1697(2012).
[13] CHEN H, ZHANG Y, KALRA M K et al. Low-dose CT with a residual encoder-decoder convolutional neural network[J]. IEEE Transactions on Medical Imaging, 36, 2524-2535(2017).
[14] PENG Y, ZHANG L, LIU S et al. Dilated residual networks with symmetric skip connection for image denoising[J]. Neurocomputing, 345, 67-76(2019).
[15] LI M, HSU W, XIE X D et al. SACNN: self-attention convolutional neural network for low-dose CT denoising with self-supervised perceptual loss network[J]. IEEE Transactions on Medical Imaging, 39, 2289-2301(2020).
[16] LIANG T, JIN Y, LI Y et al. EDCNN: edge enhancement-based densely connected network with compound loss for low-dose CT denoising[C], 193-198(2020).
[17] GUI X, GUO Y, ZHANG X et al. Artifact-assisted multi-level and multi-scale feature fusion attention network for low-dose CT denoising[J]. Journal of X-Ray Science and Technology, 30, 875-889(2022).
[18] WANG D, F FAN, WU Z et al. CTformer: convolution-free token2token dilated vision transformer for low-dose CT denoising[J]. Computer Science, 10, 623(2022).
[19] WOO S, PARK J, LEE J Y et al. CBAM: Convolutional block attention module[C], 3-19(2018).
[20] MCCOLLOUGH C. TU-FG-207A-04: overview of the low dose CT grand challenge[J]. Medical Physics, 43, 3759-3760(2016).
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Yanan SUN, Ping CHEN, Jinxiao PAN. Low-dose CT denoising using combination of multi-scale residuals and global attention[J]. Journal of Applied Optics, 2025, 46(2): 292
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Received: Apr. 15, 2024
Accepted: --
Published Online: May. 13, 2025
The Author Email: Ping CHEN (陈平)