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John L. Dart Library
Closed for Maintenance
Phone: (843) 722-7550
West Ashley Library
9 a.m. - 5 p.m.
Phone: (843) 766-6635
Folly Beach Library
9 a.m. - 2 p.m.
*open the 2nd and 4th Saturday
*open the 2nd and 4th Saturday
Phone: (843) 588-2001
Edgar Allan Poe/Sullivan's Island Library
Closed for renovations
Phone: (843) 883-3914
Wando Mount Pleasant Library
9 a.m. - 5 p.m.
Phone: (843) 805-6888
Village Library
9 a.m. - 1 p.m.
Phone: (843) 884-9741
St. Paul's/Hollywood Library
9 a.m. - 5 p.m.
Phone: (843) 889-3300
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Unified Supervised-Unsupervised (SUPER) Learning for X-Ray CT Image Reconstruction.
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- Author(s): Ye, Siqi1 (AUTHOR) ; Li, Zhipeng1 (AUTHOR) ; McCann, Michael T.2 (AUTHOR); Long, Yong1 (AUTHOR) ; Ravishankar, Saiprasad2 (AUTHOR)
- Source:
IEEE Transactions on Medical Imaging. Nov2021, Vol. 40 Issue 11, p2986-3001. 16p.- Subject Terms:
- Source:
- Additional Information
- Abstract: Traditional model-based image reconstruction (MBIR) methods combine forward and noise models with simple object priors. Recent machine learning methods for image reconstruction typically involve supervised learning or unsupervised learning, both of which have their advantages and disadvantages. In this work, we propose a unified supervised-unsupervised (SUPER) learning framework for X-ray computed tomography (CT) image reconstruction. The proposed learning formulation combines both unsupervised learning-based priors (or even simple analytical priors) together with (supervised) deep network-based priors in a unified MBIR framework based on a fixed point iteration analysis. The proposed training algorithm is also an approximate scheme for a bilevel supervised training optimization problem, wherein the network-based regularizer in the lower-level MBIR problem is optimized using an upper-level reconstruction loss. The training problem is optimized by alternating between updating the network weights and iteratively updating the reconstructions based on those weights. We demonstrate the learned SUPER models’ efficacy for low-dose CT image reconstruction, for which we use the NIH AAPM Mayo Clinic Low Dose CT Grand Challenge dataset for training and testing. In our experiments, we studied different combinations of supervised deep network priors and unsupervised learning-based or analytical priors. Both numerical and visual results show the superiority of the proposed unified SUPER methods over standalone supervised learning-based methods, iterative MBIR methods, and variations of SUPER obtained via ablation studies. We also show that the proposed algorithm converges rapidly in practice. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of IEEE Transactions on Medical Imaging is the property of IEEE and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Abstract:
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