The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
LEARN: Learned Experts’ Assessment-Based Reconstruction Network for Sparse-Data CT
Open accessAbstract
Compressive sensing (CS) has proved effective for tomographic reconstruction from sparsely collected data or under-sampled measurements, which are practically important for few-view computed tomography (CT), tomosynthesis, interior tomography, and so on. To perform sparse-data CT, the iterative reconstruction commonly uses regularizers in the CS framework. Currently, how to choose the parameters adaptively for regularization is a major open problem. In this paper, inspired by the idea of machine learning especially deep learning, we unfold the state-of-the-art "fields of experts"-based iterative reconstruction scheme up to a number of iterations for data-driven training, construct a learned experts' assessment-based reconstruction network (LEARN) for sparse-data CT, and demonstrate the feasibility and merits of our LEARN network. The experimental results with our proposed LEARN network produces a superior performance with the well-known Mayo Clinic low-dose challenge data set relative to the several state-of-the-art methods, in terms of artifact reduction, feature preservation, and computational speed. This is consistent to our insight that because all the regularization terms and parameters used in the iterative reconstruction are now learned from the training data, our LEARN network utilizes application-oriented knowledge more effectively and recovers underlying images more favorably than competing algorithms. Also, the number of layers in the LEARN network is only 50, reducing the computational complexity of typical iterative algorithms by orders of magnitude.
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Deep learning for tomographic image reconstruction
Wang, G., Ye, J. C., & De Man, B. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-00273-z · Cited by 531
Self-citationSAINT: Spatially Aware Interpolation NeTwork for Medical Slice Synthesis
Peng, C., Lin, W.-A., Liao, H., Chellappa, R., & Zhou, S. K. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00777 · Cited by 75
Deep-Learning-Based Few-Angle Cardiac SPECT Reconstruction Using Transformer
Xie, H., Thorn, S., Liu, Y.-H., Lee, S., Liu, Z., Wang, G., Sinusas, A. J., & Liu, C. (2023) · IEEE Transactions on Radiation and Plasma Medical Sciences · doi:10.1109/trpms.2022.3187595 · Cited by 16
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Metadata
- Authors
- Chen, H., Zhang, Y., Chen, Y., Zhang, J., Zhang, W., Sun, H., Lv, Y., Liao, P., Zhou, J., & Wang, G.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2018-06
- Volume
- 37
- Issue
- 6
- Pages
- 1333-1347
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2793419304
- Cited by
- 433
- References
- 10
- FWCI
- 25.51
- Type
- journal article
- Language
- en
- Open access
- green
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