Low-dose CT Image Reconstruction Method Based on CNN and Transformer Coupling Network

QIAO Yiyu; QIAO Zhiwei

doi:10.15953/j.ctta.2022.114

Volume 31 Issue 6

Nov. 2022

Turn off MathJax

Article Contents

Abstract

References

CT Theory and Applications > 2022 > 31(6): 697-707. > 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. (in Chinese).

Citation:

PDF (3681 KB)

Low-dose CT Image Reconstruction Method Based on CNN and Transformer Coupling Network

QIAO Yiyu,
QIAO Zhiwei^,

School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China

More Information

Received Date: June 11, 2022
Revised Date: July 25, 2022
Accepted Date: July 26, 2022
Available Online: August 11, 2022
Published Date: November 02, 2022

Graphical Abstract

Abstract

Abstract

Under the condition that the number of projection angles is constant, reducing the radiation dose under each angle is an effective way to realize low-dose CT. However, the reconstructed images obtained through this method can be very noisy. At present, the deep learning image denoising method represented by convolutional neural networks (CNN) has become a classical method for low-dose CT image denoising. Inspired by the good performance of transformer in computer vision tasks, this paper proposes a CNN transformer coupling network (CTC) to further improve the performance of CT image denoising. CTC network makes comprehensive use of local information association ability of CNN and global information capture ability of transformer, constructs eight core network blocks composed of CNN components and an improved transformer component, which are interconnected based on residual connection mechanism and information reuse mechanism. Compared with the existing four denoising networks, CTC network demonstrate better denoising ability and can realize high-precision low-dose CT image reconstruction.
- low dose CT,
- self attention mechanism,
- convolutional neural network,
- residual connection

FullText(HTML)

References (25)

References

[1]	BRENNER D J, HALL E J. Computed tomography: An increasing source of radiation exposure[J]. New England Journal of Medicine, 2013, 357(22): 2277−2284.
[2]	KOLTAI P J, WOOD G W. Three dimensional CT reconstruction for the evaluation and surgical planning of facial fractures[J]. Otolaryngology-Head and Neck Surgery, 1986, 95(1): 10−15. doi: 10.1177/019459988609500103
[3]	MING C, LI L, CHEN Z, et al. A few-view reweighted sparsity hunting (FRESH) method for CT image reconstruction[J]. Journal of X-ray Science and Technology, 2013, 21(2): 161−176. doi: 10.3233/XST-130370
[4]	KANG E, MIN J, YE J C. A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction[J]. Medical Physics, 2017, 44(10): e360−e375. doi: 10.1002/mp.12344
[5]	PRASOON A, PETERSON K, IGEL C, et al. Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network[C]//International Conference on Medical Image Computing and Computer-assisted Intervention. Springer, Berlin, Heidelberg, 2013: 246-253.
[6]	YANG Q, YAN P, ZHANG Y, et al. Low-dose CT image denoising using a generative adversarial network with wasserstein distance and perceptual loss[J]. IEEE Transactions on Medical Imaging, 2018: 1348−1357.
[7]	HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016: 770-778.
[8]	YANG D, SUN J. BM3 D-Net: A convolutional neural network for transform-domain collaborative filtering[J]. IEEE Signal Processing Letters, 2017, 25(1): 55−59.
[9]	SINGH R, WU W, WANG G, et al. Artificial intelligence in image reconstruction: The change is here[J]. Physica Medica, 2020, 79: 113−125. doi: 10.1016/j.ejmp.2020.11.012
[10]	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, 2016, 26(7): 3142−3155.
[11]	CHEN H, ZHANG Y, KALRA M K, et al. Low-dose CT with a residual encoder-decoder convolutional neural network (RED-CNN)[J]. IEEE Transactions on Medical Imaging, 2017, 36(99): 2524−2535.
[12]	ZHANG K, ZUO W, ZHANG L. FFDNet: Toward a fast and flexible solution for CNN based image denoising[J]. IEEE Transactions on Image Processing, 2018.
[13]	ANWAR S, BARNES N. Real image denoising with feature attention[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019: 3155-3164.
[14]	TIAN C, XU Y, ZUO W. Image denoising using deep CNN with batch renormalization[J]. Neural Networks, 2020, 121: 461−473. doi: 10.1016/j.neunet.2019.08.022
[15]	MA Y, WEI B, FENG P, et al. Low-dose CT image denoising using a generative adversarial network with a hybrid loss function for noise learning[J]. IEEE Access, 2020, 8. DOI: 10.1109/ACCESS.2020.2986388.
[16]	LIANG J, CAO J, SUN G, et al. Swinir: Image restoration using swin transformer[C]// Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021: 1833-1844.
[17]	YUAN K, GUO S, LIU Z, et al. Incorporating convolution designs into visual transformers[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021: 579-588.
[18]	VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[J]. Advances in Neural Information Processing Systems, 2017. https://doi.org/10.48550/arXiv.1706.03762.
[19]	DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al. An image is worth 16×16 words: Transformers for image recognition at scale[J]. arXiv Preprint arXiv: 2010.11929, 2020.
[20]	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.
[21]	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.
[22]	HAN K, WANG Y, CHEN H, et al. A survey on vision transformer[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022.
[23]	HUANG G, LIU Z, Van der MAATEN L, et al. Densely connected convolutional networks[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017: 4700-4708.
[24]	ZHANG J Y, YAN C, HUANG X X. Edge detection of images based on improved Sobel operator and genetic algorithms[C]//2009 International Conference on Image Analysis and Signal Processing. IEEE, 2009: 31-35.
[25]	TOLSTIKHIN I O, HOULSBY N, KOLESNIKOV A, et al. MLP-mixer: An all-MLP architecture for vision[J]. Advances in Neural Information Processing Systems, 2021: 34.

Cited By

Get Citation

PDF

XML

Article views (1579) PDF downloads (346)

Low-dose CT Image Reconstruction Method Based on CNN and Transformer Coupling Network

Abstract

References

Catalog

Related

Low-dose CT Image Reconstruction Method Based on CNN and Transformer Coupling Network

Abstract

References

Catalog

Related

Export File

Citation

Format

Content