The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Learned Primal-Dual Reconstruction
Open accessAbstract
We propose the Learned Primal-Dual algorithm for tomographic reconstruction. The algorithm accounts for a (possibly non-linear) forward operator in a deep neural network by unrolling a proximal primal-dual optimization method, but where the proximal operators have been replaced with convolutional neural networks. The algorithm is trained end-to-end, working directly from raw measured data and it does not depend on any initial reconstruction such as filtered back-projection (FBP). We compare performance of the proposed method on low dose computed tomography reconstruction against FBP, total variation (TV), and deep learning based post-processing of FBP. For the Shepp-Logan phantom we obtain >6 dB peak signal to noise ratio improvement against all compared methods. For human phantoms the corresponding improvement is 6.6 dB over TV and 2.2 dB over learned post-processing along with a substantial improvement in the structural similarity index. Finally, our algorithm involves only ten forward-back-projection computations, making the method feasible for time critical clinical applications.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741
Approximation capabilities of multilayer feedforward networks
Hornik, K. (1991) · Neural Networks · doi:10.1016/0893-6080(91)90009-t · Cited by 6,091
A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging
Chambolle, A., & Pock, T. (2011) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-010-0251-1 · Cited by 4,336
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×
“We re-implemented the algorithm from [7] but found that using the training procedure as stated in the paper gave sub-optimal results.”
MethodLow-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network
Chen, H., Zhang, Y., Kalra, M. K., Lin, F., Chen, Y., Liao, P., Zhou, J., & Wang, G. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2715284 · Cited by 1,866
Learning a variational network for reconstruction of accelerated MRI data
Hammernik, K., Klatzer, T., Kobler, E., Recht, M. P., Sodickson, D. K., Pock, T., & Knoll, F. (2018) · Magnetic Resonance in Medicine · doi:10.1002/mrm.26977 · Cited by 1,713
Fast and flexible X-ray tomography using the ASTRA toolbox
van Aarle, W., Palenstijn, W. J., Cant, J., Janssens, E., Bleichrodt, F., Dabravolski, A., De Beenhouwer, J., Joost Batenburg, K., & Sijbers, J. (2016) · Optics Express · doi:10.1364/oe.24.025129 · Cited by 1,036
The Little Engine That Could: Regularization by Denoising (RED)
Romano, Y., Elad, M., & Milanfar, P. (2017) · SIAM Journal on Imaging Sciences · doi:10.1137/16m1102884 · Cited by 934
Cited by
(2020) · doi:10.1007/978-3-030-58536-5_24 · Cited by 2,824
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
You, C., Cong, W., Vannier, M. W., Saha, P. K., Hoffman, E. A., Wang, G., Li, G., Zhang, Y., Zhang, X., Shan, H., et al. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2922960 · Cited by 459
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
Self-citationLi, M., Hsu, W., Xie, X., Cong, J., & Gao, W. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2968472 · Cited by 321
Metadata
- Authors
- Adler, J., & Oktem, O.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2018-06
- Volume
- 37
- Issue
- 6
- Pages
- 1322-1332
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2738743584
- Cited by
- 758
- References
- 18
- FWCI
- 42.52
- Type
- journal article
- Language
- en
- Open access
- green
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