The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Model-Based Learning for Accelerated, Limited-View 3-D Photoacoustic Tomography
Open accessAbstract
Recent advances in deep learning for tomographic reconstructions have shown great potential to create accurate and high quality images with a considerable speed up. In this paper, we present a deep neural network that is specifically designed to provide high resolution 3-D images from restricted photoacoustic measurements. The network is designed to represent an iterative scheme and incorporates gradient information of the data fit to compensate for limited view artifacts. Due to the high complexity of the photoacoustic forward operator, we separate training and computation of the gradient information. A suitable prior for the desired image structures is learned as part of the training. The resulting network is trained and tested on a set of segmented vessels from lung computed tomography scans and then applied to in-vivo photoacoustic measurement data.
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Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Donoho, D. L. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2006.871582 · Cited by 23,994
Donoho, D. L. (2004) · Cited by 16,703
Stable signal recovery from incomplete and inaccurate measurements
Candès, E. J., Romberg, J. K., & Tao, T. (2006) · Communications on Pure and Applied Mathematics · doi:10.1002/cpa.20124 · Cited by 7,036
Multiscale vessel enhancement filtering
Frangi, A. F., Niessen, W. J., Vincken, K. L., & Viergever, M. A. (1998) · Lecture Notes in Computer Science · doi:10.1007/bfb0056195 · Cited by 3,707
Single-pixel imaging via compressive sampling
Duarte, M. F., Davenport, M. A., Takhar, D., Laska, J. N., Sun, T., Kelly, K. F., & Baraniuk, R. G. (2008) · IEEE Signal Processing Magazine · doi:10.1109/msp.2007.914730 · Cited by 3,575
Deep Convolutional Neural Network for Inverse Problems in Imaging
Jin, K. H., McCann, M. T., Froustey, E., & Unser, M. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2713099 · Cited by 2,529 · cited 2×
“For our application, we have followed the modified U-Net architecture proposed by [20] for post-processing of 2D X-ray tomography, that learns to compute an update to the initial reconstruction.”
BackgroundMethodk-Wave: MATLAB toolbox for the simulation and reconstruction of photoacoustic wave fields
Treeby, B. E., & Cox, B. T. (2010) · Journal of Biomedical Optics · doi:10.1117/1.3360308 · Cited by 2,337
Self-citationBiomedical photoacoustic imaging
Beard, P. (2011) · Interface Focus · doi:10.1098/rsfs.2011.0028 · Cited by 2,205
Self-citation
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
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
Self-citationPhotoacoustic tomography of blood oxygenation: A mini review
Li, M., Tang, Y., & Yao, J. (2018) · Photoacoustics · doi:10.1016/j.pacs.2018.05.001 · Cited by 359
Metadata
- Authors
- Hauptmann, A., Lucka, F., Betcke, M., Huynh, N., Adler, J., Cox, B., Beard, P., Ourselin, S., & Arridge, S.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2018-06
- Volume
- 37
- Issue
- 6
- Pages
- 1382-1393
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2751563926
- Cited by
- 339
- References
- 40
- FWCI
- 15.48
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- The paper
- Hauptmann, A., Lucka, F., Betcke, M., Huynh, N., Adler, J., Cox, B., Beard, P., Ourselin, S., & Arridge, S. (2018). Model-Based Learning for Accelerated, Limited-View 3-D Photoacoustic Tomography. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/tmi.2018.2820382
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