Deep learning based spectral CT imaging.
| Title: | Deep learning based spectral CT imaging. |
|---|---|
| Authors: | Wu W; Department of Diagnostic Radiology, Queen Mary Hospital, University of Hong Kong, Hong Kong, People's Republic of China; Biomedical Imaging Center, Center for Biotechnology and Interdisciplinary Studies, Department of Biomedical Engineering, School of Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA.; Hu D; The Laboratory of Image Science and Technology, Southeast University, Nanjing, People's Republic of China.; Niu C; Biomedical Imaging Center, Center for Biotechnology and Interdisciplinary Studies, Department of Biomedical Engineering, School of Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA.; Broeke LV; Department of Diagnostic Radiology, Queen Mary Hospital, University of Hong Kong, Hong Kong, People's Republic of China.; Butler APH; Department of Radiology, University of Otago, Christchurch, New Zealand.; Cao P; Department of Diagnostic Radiology, Queen Mary Hospital, University of Hong Kong, Hong Kong, People's Republic of China.; Atlas J; Department of Radiology, University of Otago, Christchurch, New Zealand.; Chernoglazov A; Department of Radiology, University of Otago, Christchurch, New Zealand.; Vardhanabhuti V; Department of Diagnostic Radiology, Queen Mary Hospital, University of Hong Kong, Hong Kong, People's Republic of China. Electronic address: varv@hku.hk.; Wang G; Biomedical Imaging Center, Center for Biotechnology and Interdisciplinary Studies, Department of Biomedical Engineering, School of Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA. |
| Source: | Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2021 Dec; Vol. 144, pp. 342-358. Date of Electronic Publication: 2021 Aug 28. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: Pergamon Press Country of Publication: United States NLM ID: 8805018 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2782 (Electronic) Linking ISSN: 08936080 NLM ISO Abbreviation: Neural Netw Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: New York : Pergamon Press, [c1988- |
| MeSH Terms: | Deep Learning*; Algorithms ; Image Processing, Computer-Assisted ; Phantoms, Imaging ; Tomography, X-Ray Computed |
| Abstract: | Spectral computed tomography (CT) has attracted much attention in radiation dose reduction, metal artifacts removal, tissue quantification and material discrimination. The x-ray energy spectrum is divided into several bins, each energy-bin-specific projection has a low signal-noise-ratio (SNR) than the current-integrating counterpart, which makes image reconstruction a unique challenge. Traditional wisdom is to use prior knowledge based iterative methods. However, this kind of methods demands a great computational cost. Inspired by deep learning, here we first develop a deep learning based reconstruction method; i.e., U-net with Lpp-norm, Total variation, Residual learning, and Anisotropic adaption (ULTRA). Specifically, we emphasize the various multi-scale feature fusion and multichannel filtering enhancement with a denser connection encoding architecture for residual learning and feature fusion. To address the image deblurring problem associated with the L22- loss, we propose a general Lpp-loss, p>0. Furthermore, the images from different energy bins share similar structures of the same object, the regularization characterizing correlations of different energy bins is incorporated into the Lpp- loss function, which helps unify the deep learning based methods with traditional compressed sensing based methods. Finally, the anisotropically weighted total variation is employed to characterize the sparsity in the spatial-spectral domain to regularize the proposed network In particular, we validate our ULTRA networks on three large-scale spectral CT datasets, and obtain excellent results relative to the competing algorithms. In conclusion, our quantitative and qualitative results in numerical simulation and preclinical experiments demonstrate that our proposed approach is accurate, efficient and robust for high-quality spectral CT image reconstruction.; (Copyright © 2021 Elsevier Ltd. All rights reserved.) |
| Competing Interests: | Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: loss; Deep learning; Image reconstruction; Regularization prior; Spectral CT |
| Entry Date(s): | Date Created: 20210924 Date Completed: 20211124 Latest Revision: 20211124 |
| Update Code: | 20260130 |
| DOI: | 10.1016/j.neunet.2021.08.026 |
| PMID: | 34560584 |
| Database: | MEDLINE |
Journal Article