TD-Net: A Tri-domain network for sparse-view CT reconstruction
Abstract: Sparse-view CT reconstruction, aimed at reducing X-ray radiation risks, frequently suffers from image quality degradation, manifested as noise and artifacts. Existing post-processing and dual-domain techniques, although effective in radiation reduction, often lead to over-smoothed results, compromising diagnostic clarity. Addressing this, we introduce TD-Net, a pioneering tri-domain approach that unifies sinogram, image, and frequency domain optimizations. By incorporating Frequency Supervision Module(FSM), TD-Net adeptly preserves intricate details, overcoming the prevalent over-smoothing issue. Extensive evaluations demonstrate TD-Net's superior performance in reconstructing high-quality CT images from sparse views, efficiently balancing radiation safety and image fidelity. The enhanced capabilities of TD-Net in varied noise scenarios highlight its potential as a breakthrough in medical imaging.
- National Lung Screening Trial Research Team, “Reduced lung-cancer mortality with low-dose computed tomographic screening,” New England Journal of Medicine, vol. 365, no. 5, pp. 395–409, 2011.
- Frank Natterer, The mathematics of computerized tomography, SIAM, 2001.
- “Nonlinear total variation based noise removal algorithms,” Physica D: Nonlinear Phenomena, vol. 60, no. 1, pp. 259–268, 1992.
- “A wavelet-based method for multiscale tomographic reconstruction,” IEEE Transactions on Medical Imaging, vol. 15, no. 1, pp. 92–101, 1996.
- “Deep convolutional neural network for inverse problems in imaging,” IEEE transactions on image processing, vol. 26, no. 9, pp. 4509–4522, 2017.
- “Dudonet: Dual domain network for ct metal artifact reduction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 10512–10521.
- “Dudotrans: Dual-domain transformer for sparse-view ct reconstruction,” in International Workshop on Machine Learning for Medical Image Reconstruction. Springer, 2022, pp. 84–94.
- “Fourier image transformer,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 1846–1854.
- “Translating math formula images to latex sequences using deep neural networks with sequence-level training,” International Journal on Document Analysis and Recognition (IJDAR), vol. 24, no. 1-2, pp. 63–75, 2021.
- “Transformers are rnns: Fast autoregressive transformers with linear attention,” in International conference on machine learning. PMLR, 2020, pp. 5156–5165.
- “The lodopab-ct dataset: A benchmark dataset for low-dose ct reconstruction methods,” arXiv preprint arXiv:1910.01113, 2019.
- Armand Wirgin, “The inverse crime,” arXiv preprint math-ph/0401050, 2004.
- “Pytorch: An imperative style, high-performance deep learning library,” Advances in neural information processing systems, vol. 32, 2019.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.