ISSN 1004-4140
CN 11-3017/P
| Citation: |
Wu S W, Fang C Y, Qiao Z W. Enhanced Restormer for Low-Dose CT Image Reconstruction Based on Multi-Attention Fusion[J]. CT Theory and Applications, xxxx, x(x): 1-10. DOI: 10.15953/j.ctta.2025.052. (in Chinese).
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Computed Tomography (CT) technology plays a crucial role in medical diagnosis. Reducing the radiation dose per projection angle while maintaining a constant number of projection angles is an effective approach to achieving low-dose CT. However, this reduction often introduces significant noise into the reconstructed CT images, adversely affecting subsequent image analysis and research. To address this issue, we propose the Enhanced Restormer for Low-Dose CT Image Reconstruction Based on Multi-Attention Fusion (ERestormer) for low-dose CT image denoising. The network integrates channel attention, receptive field attention, and multi-head transposed attention to enhance the model’s ability to focus on critical information, thereby improving its feature learning capacity. Furthermore, a feature fusion mechanism is introduced to strengthen feature reuse between the encoder and decoder. Experimental results show that the proposed network achieves superior denoising performance and enhanced preservation of image detail when compared to five classical networks: DNCNN, RED-CNN, UNet, Uformer, and Restormer.
| [1] |
BRENNER D J, HALL E J. Computed tomography—an increasing source of radiation exposure[J]. New England Journal of Medicine, 2007, 357(22): 2277-2284. DOI: 10.1056/NEJMra072149.
|
| [2] |
QIAO Z, LU Y, LIU P, et al. An iterative reconstruction algorithm without system matrix for EPR imaging[J]. Journal of Magnetic Resonance, 2022, 344: 107307. DOI: 10.1016/j.jmr.2022.107307.
|
| [3] |
QIAOZ, ZHANG Z, PAN X, et al. Optimization-based image reconstruction from sparsely sampled data in electron paramagnetic resonance imaging[J]. Journal of Magnetic Resonance, 2018, 294: 24-34. DOI: 10.1016/j.jmr.2018.06.015.
|
| [4] |
QIAO Z, LIU P, FANG C, et al. Directional TV algorithm for image reconstruction from sparse-view projections in EPR imaging[J]. Physics in Medicine & Biology, 2024, 69(11): 115051.
|
| [5] |
FANG C, XI Y, EPEL B, et al. Directional TV algorithm for fast EPR imaging[J]. Journal of Magnetic Resonance, 2024, 361: 107652. DOI: 10.1016/j.jmr.2024.107652.
|
| [6] |
马靓怡, 乔志伟. 基于Chambolle-Pock框架的核TV多通道图像重建算法[J]. CT理论与应用研究, 2022, 31(6): 731-747. DOI: 10.15953/j.ctta.2022.111.
MA J Y, QIAO Z W. Nuclear TV multi-channel image reconstruction algorithms based on Chambolle-pock framework[J]. CT Theory and Applications, 2022, 31(6): 731-747. DOI: 10.15953/j.ctta.2022.111.
|
| [7] |
张家浩, 乔志伟. 基于相对TV最小的CT图像重建算法[J]. CT理论与应用研究, 2023, 32(2): 153-169. DOI: 10.15953/j.ctta.2022.190.
ZHANG J H, QIAO Z W. Computed tomography reconstruction algorithm based on relative total variation minimization[J]. CT Theory and Applications, 2023, 32(2): 153-169. DOI: 10.15953/j.ctta.2022.190.
|
| [8] |
LIU P, FANG C, QIAO Z. A dense and U-shaped transformer with dual-domain multi-loss function for sparse-view CT reconstruction[J]. Journal of X-Ray Science and Technology, 2024(Preprint): 1-22.
|
| [9] |
LIU P, ZHANG Y, XI Y, et al. PU-CDM: A pyramid UNet based conditional diffusion model for sparse-view reconstruction in EPRI[J]. Biomedical Signal Processing and Control, 2025, 100: 107182. DOI: 10.1016/j.bspc.2024.107182.
|
| [10] |
YAN H, FANG C, QIAO Z. A multi-attention Uformer for low-dose CT image denoising[J]. Signal, Image and Video Processing, 2024, 18(2): 1429-1442. DOI: 10.1007/s11760-023-02853-z.
|
| [11] |
ZHANG K, ZUO W, CHEN Y, et al. Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising[J]. IEEE Transactions on Image Processing, 2017, 26(7): 3142-3155. DOI: 10.1109/TIP.2017.2662206.
|
| [12] |
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, 2017, 36(12): 2524-2535. DOI: 10.1109/TMI.2017.2715284.
|
| [13] |
GUO S, YAN Z, ZHANG K, et al. Toward convolutional blind denoising of real photographs[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019: 1712-1722.
|
| [14] |
ZHAO Y, JIANG Z, MEN A, et al. Pyramid real image denoising network[C]//2019 IEEE Visual Communications and Image Processing (VCIP). IEEE, 2019: 1-4.
|
| [15] |
CHEN L, CHU X, ZHANG X, et al. Simple baselines for image restoration[C]//European conference on computer vision. Cham: Springer Nature Switzerland, 2022: 17-33.
|
| [16] |
ANWAR S, BARNES N. Real image denoising with feature attention[C]//Proceedings of the IEEE/CVF international conference on computer vision. 2019: 3155-3164.
|
| [17] |
CHEN J, YU Q, SHEN X, et al. ViTamin: Designing Scalable Vision Models in the Vision-Language Era[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024: 12954-12966.
|
| [18] |
CHEN X, LI H, LI M, et al. Learning a sparse transformer network for effective image deraining[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023: 5896-5905.
|
| [19] |
乔一瑜, 乔志伟. 基于CNN和Transformer耦合网络的低剂量CT图像重建方法[J]. CT理论与应用研究, 2022, 31(6): 697-707+694. DOI: 10.15953/j.ctta.2022.114.
QIAO Y Y, QIAO Z W. Low-dose CT image reconstruction method based on CNN and transformer coupling network[J]. CT Theory and Applications, 2022, 31(6): 697-707. DOI: 10.15953/j.ctta.2022.114.
|
| [20] |
樊雪林, 文昱齐, 乔志伟. 基于Transformer增强型U-net的CT图像稀疏重建与伪影抑制[J]. CT理论与应用研究, 2024, 33(1): 1-12. DOI: 10.15953/j.ctta.2023.183.
FAN X L, WEN Y Q, QIAO Z W. Sparse reconstruction of computed tomography images with transformer enhanced U-net[J]. CT Theory and Applications, 2024, 33(1): 1-12. DOI: 10.15953/j.ctta.2023.183.
|
| [21] |
魏屹立, 杨子元, 夏文军, 等. 基于子空间投影和边缘增强的低剂量CT去噪[J]. CT理论与应用研究, 2022, 31(6): 721-729. DOI: 10.15953/j.ctta.2022.108.
WEI Y L, YANG Z Y, XIA W J, et al. Low-dose CT denoising based on subspace projection and edge enhancement[J]. CT Theory and Applications, 2022, 31(6): 721-729. DOI: 10.15953/j.ctta.2022.108.
|
| [22] |
VASWANI A. Attention is all you need[J]. Advances in Neural Information Processing Systems, 2017.
|
| [23] |
DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al. An image is worth 16×16 words: Transformers for image recognition at scale[C]//Proceedings of the 9th International Conference on Learning Representations. Austria: OpenReview. net, 2021.
|
| [24] |
LIU Z, LIN Y, CAO Y, et al. Swin transformer: Hierarchical vision transformer using shifted windows[C]//Proceedings of the IEEE/CVF international conference on computer vision. 2021: 10012-10022.
|
| [25] |
WANG Z, CUN X, BAO J, et al. Uformer: A general u-shaped transformer for image restoration[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022: 17683-17693.
|
| [26] |
ZAMIR S W, ARORA A, KHAN S, et al. Restormer: Efficient transformer for high-resolution image restoration[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022: 5728-5739.
|
| [27] |
WANG T, ZHANG K, SHEN T, et al. Ultra-high-definition low-light image enhancement: A benchmark and transformer-based method[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2023, 37(3): 2654-2662.
|
| [28] |
XU S, SUN Z, ZHU J, et al. DemosaicFormer: Coarse-to-Fine Demosaicing Network for HybridEVS Camera[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024: 1126-1135.
|
| [29] |
OUYANG D, HE S, ZHANG G, et al. Efficient multi-scale attention module with cross-spatial learning[C]//ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023: 1-5.
|
| [30] |
TU Z, TALEBI H, ZHANG H, et al. Maxim: Multi-axis mlp for image processing[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022: 5769-5780.
|
| [31] |
CHEN X, WANG X, ZHOU J, et al. Activating more pixels in image super-resolution transformer[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2023: 22367-22377.
|
| [32] |
ZHANG X, LIU C, YANG D, et al. RFAConv: Innovating spatial attention and standard convolutional operation[J]. ArXiv, 2304: abs/2304.03198.
|
| [33] |
DAI Y, GIESEKE F, OEHMCKE S, et al. Attentional feature fusion[C]//Proceedings of the IEEE/CVF winter conference on applications of computer vision. 2021: 3560-3569.
|
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