The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Deep Learning Computed Tomography: Learning Projection-Domain Weights From Image Domain in Limited Angle Problems
Abstract
In this paper, we present a new deep learning framework for 3-D tomographic reconstruction. To this end, we map filtered back-projection-type algorithms to neural networks. However, the back-projection cannot be implemented as a fully connected layer due to its memory requirements. To overcome this problem, we propose a new type of cone-beam back-projection layer, efficiently calculating the forward pass. We derive this layer's backward pass as a projection operation. Unlike most deep learning approaches for reconstruction, our new layer permits joint optimization of correction steps in volume and projection domain. Evaluation is performed numerically on a public data set in a limited angle setting showing a consistent improvement over analytical algorithms while keeping the same computational test-time complexity by design. In the region of interest, the peak signal-to-noise ratio has increased by 23%. In addition, we show that the learned algorithm can be interpreted using known concepts from cone beam reconstruction: the network is able to automatically learn strategies such as compensation weights and apodization windows.
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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
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
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362
Greedy layer-wise training of deep networks
Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0024 · Cited by 4,841
Marginal space deep learning: efficient architecture for volumetric image parsing
Ghesu, F. C., Krubasik, E., Georgescu, B., Singh, V., Zheng, Y., Hornegger, J., & Comaniciu, D. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2538802 · Cited by 142
Cited by
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
DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstruction
Liu, J., Anirudh, R., Thiagarajan, J. J., He, S., Mohan, K. A., Kamilov, U. S., & Kim, H. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00963 · Cited by 106
SAINT: Spatially Aware Interpolation NeTwork for Medical Slice Synthesis
Peng, C., Lin, W.-A., Liao, H., Chellappa, R., & Zhou, S. K. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00777 · Cited by 75
Enabling machine learning in X-ray-based procedures via realistic simulation of image formation
Unberath, M., Zaech, J.-N., Gao, C., Bier, B., Goldmann, F., Lee, S. C., Fotouhi, J., Taylor, R., Armand, M., & Navab, N. (2019) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-019-02011-2 · Cited by 63
Self-citationGeneralizing Spatial Transformers to Projective Geometry with Applications to 2D/3D Registration
Gao, C., Liu, X., Gu, W., Killeen, B., Armand, M., Taylor, R., & Unberath, M. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-59716-0_32 · Cited by 46
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Metadata
- Authors
- Würfl, T., Hoffmann, M., Christlein, V., Breininger, K., Huang, Y., Unberath, M., & Maier, A. K.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2018-06
- Volume
- 37
- Issue
- 6
- Pages
- 1454-1463
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2803086176
- Cited by
- 245
- References
- 5
- FWCI
- 18.27
- Type
- journal article
- Language
- en
- Open access
- closed
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- Würfl, T., Hoffmann, M., Christlein, V., Breininger, K., Huang, Y., Unberath, M., & Maier, A. K. (2018). Deep Learning Computed Tomography: Learning Projection-Domain Weights From Image Domain in Limited Angle Problems. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/tmi.2018.2833499
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