Transformer-Based Dual-Domain Network for Few-View Dedicated Cardiac SPECT Image Reconstructions

Author:

Xie Huidong,Zhou Bo,Chen Xiongchao,Guo Xueqi,Thorn Stephanie,Liu Yi-Hwa,Wang Ge,Sinusas Albert,Liu Chi

Publisher

Springer Nature Switzerland

Reference14 articles.

1. Bocher, M., Blevis, I., Tsukerman, L., Shrem, Y., Kovalski, G., Volokh, L.: A fast cardiac gamma camera with dynamic SPECT capabilities: design, system validation and future potential. Eur. J. Nucl. Med. Mol. Imaging 37(10), 1887–1902 (2010). https://doi.org/10.1007/s00259-010-1488-z

2. Dosovitskiy, A., et al.: An image is worth 16 $$\times $$ 16 words: transformers for image recognition at scale. arXiv:2010.11929 [cs], June 2021. http://arxiv.org/abs/2010.11929

3. Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Teh, Y.W., Titterington, M. (eds.) Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, vol. 9, pp. 249–256. PMLR, Chia Laguna Resort, Sardinia, Italy, 13–15 May 2010 (2010). http://proceedings.mlr.press/v9/glorot10a.html

4. Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.: Improved training of Wasserstein GANs. In: Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc. (2017)

5. He, J., Wang, Y., Ma, J.: Radon inversion via deep learning. IEEE Trans. Med. Imaging 39(6), 2076–2087 (2020). https://doi.org/10.1109/TMI.2020.2964266

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