Abstract
Sparse-view computed tomography (CT) is one of the primary means to reduce the radiation risk. But the reconstruction of sparse-view CT will be contaminated by severe artifacts. By carefully designing the regularization terms, the iterative reconstruction (IR) algorithm can achieve promising results. With the introduction of deep learning techniques, learned regularization terms with convolution neural network (CNN) attracts much attention and can further improve the performance. In this paper, we propose a learned local-nonlocal regularization-based model called RegFormer to reconstruct CT images. Specifically, we unroll the iterative scheme into a neural network and replace handcrafted regularization terms with learnable kernels. The convolution layers are used to learn local regularization with excellent denoising performance. Simultaneously, transformer encoders and decoders incorporate the learned nonlocal prior into the model, preserving the structures and details. To improve the ability to extract deep features during iteration, we introduce an iteration transmission (IT) module, which can further promote the efficiency of each iteration. The experimental results show that our proposed RegFormer achieves competitive performance in artifact reduction and detail preservation compared to some state-of-the-art sparse-view CT reconstruction methods.
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References
Sidky, E.Y., Kao, C.-M., Pan, X.: Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT. J. X-Ray. Sci. Technol. 14(2), 119–139 (2006)
Sidky, E.Y., Pan, X.: Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization. Phys. Med. Biol. 53(17), 4777–4807 (2008)
Yu, G., Li, L., Gu, J., Zhang, L.: Total variation based iterative image reconstruction. In: Liu, Y., Jiang, T., Zhang, C. (eds.) CVBIA 2005. LNCS, vol. 3765, pp. 526–534. Springer, Heidelberg (2005). https://doi.org/10.1007/11569541_53
Niu, S., et al.: Sparse-view X-ray CT reconstruction via total generalized variation regularization. Phys. Med. Biol. 59(12), 2997–3017 (2014)
Xu, Q., et al.: Low-dose X-ray CT reconstruction via dictionary learning. IEEE Trans. Med. Imaging 31(9), 1682–1697 (2012)
Chen, Y., et al.: Bayesian statistical reconstruction for low-dose X-ray computed tomography using an adaptive-weighting nonlocal prior. Comput. Med. Imag. Graph. 33(7), 495–500 (2009)
Ma, J., et al.: Iterative image reconstruction for cerebral perfusion CT using a pre-contrast scan induced edge-preserving prior. Phys. Med. Biol. 57(22), 7519–7542 (2012)
Zhang, Y., et al.: Spectral CT reconstruction with image sparsity and spectral mean. IEEE Trans. Comput. Imaging 2(4), 510–523 (2016)
Gao, H., Yu, H., Osher, S., Wang, G.: Multi-energy CT based on a prior rank, intensity and sparsity model (PRISM). Inverse Probl. 27(11), 115012 (2011)
Cai, J.-F., et al.: Cine cone beam CT reconstruction using low-rank matrix factorization: algorithm and a proof-of-principle study. IEEE Trans. Med. Imaging 33(8), 1581–1591 (2014)
Kang, E., Min, J., Ye, J.C.: A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction. Med. Phys. 44(10), e360–e375 (2017)
Chen, H., et al.: Low-dose CT via convolutional neural network. Biomed. Opt. Exp. 8(2), 679–694 (2017)
Chen, H., et al.: Low-dose CT with a residual encoder-decoder convolutional neural network. IEEE Trans. Med. Imaging 36(12), 2524–2535 (2017)
Han, Y.S., Yoo, J., Ye, J.C.: Deep residual learning for compressed sensing CT reconstruction via persistent homology analysis, arXiv preprint arXiv:1611.06391 (2016)
Han, Y., Ye, J.C.: Framing U-Net via deep convolutional framelets: application to sparse-view CT. IEEE Trans. Med. Imaging 37(6), 1418–1429 (2018)
Jin, K.H., McCann, M.T., Froustey, E., Unser, M.: Deep convolutional neural network for inverse problems in imaging. IEEE Trans. Image Process. 26(9), 4509–4522 (2017)
Yang, Q., et al.: Low-dose CT image denoising using a generative adversarial network with wasserstein distance and perceptual loss. IEEE Trans. Med. Imaging 37(6), 1348–1357 (2018)
Shan, H., et al.: 3-D convolutional encoder-decoder network for lowdose CT via transfer learning from a 2-D trained network. IEEE Trans. Med. Imaging 37(6), 1522–1534 (2018)
Chen, H., et al.: LEARN: Learned experts’ assessment-based reconstruction network for sparse-data CT. IEEE Trans. Med. Imaging 37(6), 1333–1347 (2018)
Adler, J., Oktem, O.: Learned primal-dual reconstruction. IEEE Trans. Med. Imaging 37(6), 1322–1332 (2018)
Gupta, H., et al.: CNN-based projected gradient descent for consistent CT image reconstruction. IEEE Trans. Med. Imaging 37(6), 1440–1453 (2018)
He, J., et al.: Optimizing a parameterized plug-and-play ADMM for iterative low-dose CT reconstruction. IEEE Trans. Med. Imaging 38(2), 371–382 (2018)
Chun, I.Y., Huang, Z., Lim, H., Fessler, J.: Momentum-Net: fast and convergent iterative neural network for inverse problems. IEEE Trans. Pattern Anal. Mach. Intell. (2020)
Xiang, J., Dong, Y., Yang, Y.: FISTA-Net: learning a fast iterative shrinkage thresholding network for inverse problems in imaging. IEEE Trans. Med. Imaging 40(5), 1329–1339 (2021)
Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale, arXiv preprint arXiv:2010.11929 (2020)
Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10012–10022 (2021)
Gao, H.: Fused analytical and iterative reconstruction (AIR) via modified proximal forward–backward splitting: a FDK-based iterative image reconstruction example for CBCT. Phys. Med. Biol. 61(19), 7187 (2016)
Chen, G., et al.: AirNet: Fused analytical and iterative reconstruction with deep neural network regularization for sparse-data CT. Med. Phys. 47(7), 2916–2930 (2020)
Vaswani, A., et al.: Attention is all you need. In: Proceedings of Advances in Neural Information Processing Systems, vol. 30 (2017)
McCollough, C.: TU-FG-207A-04: overview of the low dose CT grand challenge. Med. Phys. 43(6Part35), 3759–3760 (2016)
De Man, B., Basu, S.: Distance-driven projection and backprojection. In: Proceedings of IEEE Nuclear Science Symposium Conference Record, vol. 3, pp. 1477–1480 (2002)
De Man, B., Basu, S.: Distance-driven projection and backprojection in three dimensions. Phys. Med. Biol. 49(11), 2463–2475 (2004)
Wang, Z., Cun, X., Bao, J., Liu, J.: Uformer: a general u-shaped transformer for image restoration, arXiv preprint arXiv:2106.03106 (2021)
Loshchilov, I., Hutter, F.: Decoupled weight decay regularization, arXiv preprint arXiv:1711.05101 (2017)
Acknowledgements
This work was supported in part by Sichuan Science and Technology Program under Grant 2021JDJQ0024, and in part by Sichuan University ‘From 0 to 1’ Innovative Research Program under grant 2022SCUH0016.
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Xia, W., Yang, Z., Zhou, Q., Lu, Z., Wang, Z., Zhang, Y. (2022). A Transformer-Based Iterative Reconstruction Model for Sparse-View CT Reconstruction. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13436. Springer, Cham. https://doi.org/10.1007/978-3-031-16446-0_75
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