The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Deep learning for photoacoustic tomography from sparse data
Open accessAbstract
The development of fast and accurate image reconstruction algorithms is a central aspect of computed tomography. In this paper, we investigate this issue for the sparse data problem in photoacoustic tomography (PAT). We develop a direct and highly efficient reconstruction algorithm based on deep learning. In our approach, image reconstruction is performed with a deep convolutional neural network (CNN), whose weights are adjusted prior to the actual image reconstruction based on a set of training data. The proposed reconstruction approach can be interpreted as a network that uses the PAT filtered backprojection algorithm for the first layer, followed by the U-net architecture for the remaining layers. Actual image reconstruction with deep learning consists in one evaluation of the trained CNN, which does not require time-consuming solution of the forward and adjoint problems. At the same time, our numerical results demonstrate that the proposed deep learning approach reconstructs images with a quality comparable to state of the art iterative approaches for PAT from sparse data.
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Cites
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3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
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Cited by
Fully Dense UNet for 2-D Sparse Photoacoustic Tomography Artifact Removal
Guan, S., Khan, A. A., Sikdar, S., & Chitnis, P. V. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2912935 · Cited by 538
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
One-step robust deep learning phase unwrapping
Wang, K., Li, Y., Kemao, Q., Di, J., & Zhao, J. (2019) · Optics Express · doi:10.1364/oe.27.015100 · Cited by 409
Deep D-Bar: Real-Time Electrical Impedance Tomography Imaging With Deep Neural Networks
Hamilton, S. J., & Hauptmann, A. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2828303 · Cited by 365
Model-Based Learning for Accelerated, Limited-View 3-D Photoacoustic Tomography
Hauptmann, A., Lucka, F., Betcke, M., Huynh, N., Adler, J., Cox, B., Beard, P., Ourselin, S., & Arridge, S. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2820382 · Cited by 339
Metadata
- Authors
- Antholzer, S., Haltmeier, M., & Schwab, J.
- Venue
- Inverse Problems in Science and Engineering
- Publisher
- Informa UK Limited
- Published
- 2018-09-11
- Volume
- 27
- Issue
- 7
- Pages
- 987-1005
- ISSN
- 1741-5977 (ISSN-L)
- 1741-5985
- OpenAlex
- W2605740169
- Cited by
- 301
- References
- 7
- FWCI
- 14.44
- Type
- journal article
- Language
- en
- Open access
- hybrid
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