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| 100 | 1 | _ | |a Kabri, Samira |0 P:(DE-H253)PIP1106483 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Convergent Data-driven Regularizations for CT Reconstruction |
| 260 | _ | _ | |a Singapore |c 2024 |b Springer Singapore |
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| 500 | _ | _ | |a Funding should be my DFG project BU 2327/19/-1 Grundlagen vollüberwachter Deep Learning Verfahren für Inverse ProblemePaper accepted, not yet published (written while SK and MB were still with FAU) |
| 520 | _ | _ | |a The reconstruction of images from their corresponding noisy Radontransform is a typical example of an ill-posed linear inverse problemas arising in the application of computerized tomography (CT). Asthe (naive) solution does not depend on the measured data continu-ously, regularization is needed to re-establish a continuous dependence.In this work, we investigate simple, but yet still provably convergentapproaches to learning linear regularization methods from data. Morespecifically, we analyze two approaches: One generic linear regularizationthat learns how to manipulate the singular values of the linear oper-ator in an extension of our previous work, and one tailored approachin the Fourier domain that is specific to CT-reconstruction. We provethat such approaches become convergent regularization methods as wellas the fact that the reconstructions they provide are typically muchsmoother than the training data they were trained on. Finally, wecompare the spectral as well as the Fourier-based approaches for CT-reconstruction numerically, discuss their advantages and disadvantagesand investigate the effect of discretization errors at different resolutions |
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| 999 | C | 5 | |a 10.1109/TMI.2018.2799231 |9 -- missing cx lookup -- |1 J Adler |p 1322 - |2 Crossref |u Adler, J., Öktem, O.: Learned primal-dual reconstruction. IEEE Trans. Med. Imaging 37(6), 1322–1332 (2018). https://doi.org/10.1109/TMI.2018.2799231 |t IEEE Trans. Med. Imaging |v 37 |y 2018 |
| 999 | C | 5 | |a 10.1109/TSP.2006.881199 |9 -- missing cx lookup -- |1 M Aharon |p 4311 - |2 Crossref |u Aharon, M., Elad, M., Bruckstein, A.: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54(11), 4311–4322 (2006) |t IEEE Trans. Signal Process. |v 54 |y 2006 |
| 999 | C | 5 | |1 GS Alberti |y 2021 |2 Crossref |u Alberti, G.S., De Vito, E., Lassas, M., Ratti, L., Santacesaria, M.: Learning the optimal Tikhonov regularizer for inverse problems. In: Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P.S., Vaughan, J.W. (eds.) Advances in Neural Information Processing Systems, vol. 34, pp. 25205–25216. Curran Associates Inc., New York (2021) |t Advances in Neural Information Processing Systems |
| 999 | C | 5 | |a 10.1007/978-3-642-32160-3_1 |9 -- missing cx lookup -- |1 L Ambrosio |p 1 - |2 Crossref |u Ambrosio, L., Gigli, N.: A user’s guide to optimal transport. In: Modelling and Optimisation of Flows on Networks, pp. 1–155. Springer, Berlin (2013). https://doi.org/10.1007/978-3-642-32160-3_1 |y 2013 |
| 999 | C | 5 | |2 Crossref |u Amos, B., Xu, L., Kolter, J.Z.: Input convex neural networks. In: ICML, pp. 146–155. PMLR (2017) |
| 999 | C | 5 | |a 10.1017/S0962492919000059 |9 -- missing cx lookup -- |1 S Arridge |p 1 - |2 Crossref |u Arridge, S., Maass, P., Öktem, O., Schönlieb, C.-B.: Solving inverse problems using data-driven models. Acta Numer. 28, 1–174 (2019) |t Acta Numer. |v 28 |y 2019 |
| 999 | C | 5 | |a 10.1080/01630563.2020.1740734 |9 -- missing cx lookup -- |1 A Aspri |p 1190 - |2 Crossref |u Aspri, A., Banert, S., Öktem, O., Scherzer, O.: A data-driven iteratively regularized Landweber iteration. Numer. Funct. Anal. Optim. 41(10), 1190–1227 (2020) |t Numer. Funct. Anal. Optim. |v 41 |y 2020 |
| 999 | C | 5 | |a 10.1088/1361-6420/aba415 |1 DO Baguer |9 -- missing cx lookup -- |2 Crossref |u Baguer, D.O., Leuschner, J., Schmidt, M.: Computed tomography reconstruction using deep image prior and learned reconstruction methods. Inverse Prob. 36(9), 094004 (2020). https://doi.org/10.1088/1361-6420/aba415 |t Inverse Prob. |v 36 |y 2020 |
| 999 | C | 5 | |2 Crossref |u Bai, S., Kolter, J.Z., Koltun, V. Deep equilibrium models. In: Wallach, H., Larochelle, H., Beygelzimer, A., d'Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32, Curran Associates, Inc., New York (2019) |
| 999 | C | 5 | |a 10.1038/s41598-021-97226-2 |9 -- missing cx lookup -- |1 S Barutcu |p 17740 - |2 Crossref |u Barutcu, S., Aslan, S., Katsaggelos, A.K., Gürsoy, D.: Limited-angle computed tomography with deep image and physics priors. Sci. Rep. 11(1), 17740 (2021). https://doi.org/10.1038/s41598-021-97226-2 |t Sci. Rep. |v 11 |y 2021 |
| 999 | C | 5 | |2 Crossref |u Bauermeister, H., Burger, M., Moeller, M.: Learning spectral regularizations for linear inverse problems. In: NeurIPS 2020 Workshop on Deep Learning and Inverse Problems (2020) |
| 999 | C | 5 | |a 10.1017/S0962492918000016 |9 -- missing cx lookup -- |1 M Benning |p 1 - |2 Crossref |u Benning, M., Burger, M.: Modern regularization methods for inverse problems. Acta Numer. 27, 1–111 (2018) |t Acta Numer. |v 27 |y 2018 |
| 999 | C | 5 | |2 Crossref |u Bora, A., Jalal, A., Price, E., Dimakis, A.G.: Compressed sensing using generative models. In: ICML, pp. 537–546. PMLR (2017) |
| 999 | C | 5 | |a 10.1109/TIP.2014.2299065 |9 -- missing cx lookup -- |1 Y Chen |p 1060 - |2 Crossref |u Chen, Y., Ranftl, R., Pock, T.: Insights into analysis operator learning: from patch-based sparse models to higher order MRFs. IEEE Trans. Image Process. 23(3), 1060–1072 (2014) |t IEEE Trans. Image Process. |v 23 |y 2014 |
| 999 | C | 5 | |a 10.1109/TMI.2018.2805692 |9 -- missing cx lookup -- |1 H Chen |p 1333 - |2 Crossref |u Chen, H., Zhang, Y., Chen, Y., Zhang, J., Zhang, W., Sun, H., Lyu, Y., Liao, P., Zhou, J., Wang, G.: LEARN: learned experts’ assessment-based reconstruction network for sparse-data CT. IEEE Trans. Med. Imaging 37(6), 1333–1347 (2018) |t IEEE Trans. Med. Imaging |v 37 |y 2018 |
| 999 | C | 5 | |a 10.1109/TMI.2017.2715284 |9 -- missing cx lookup -- |1 H Chen |p 2524 - |2 Crossref |u Chen, H., Zhang, Y., Kalra, M.K., Lin, F., Chen, Y., Liao, P., Zhou, J., Wang, G.: Low-dose CT with a residual encoder-decoder convolutional neural network. IEEE Trans. Med. Imaging 36(12), 2524–2535 (2017). https://doi.org/10.1109/TMI.2017.2715284 |t IEEE Trans. Med. Imaging |v 36 |y 2017 |
| 999 | C | 5 | |a 10.1137/100812938 |9 -- missing cx lookup -- |1 J Chung |p 3132 - |2 Crossref |u Chung, J., Chung, M., O’Leary, D.P.: Designing optimal spectral filters for inverse problems. SIAM J. Sci. Comput. 33(6), 3132–3152 (2011) |t SIAM J. Sci. Comput. |v 33 |y 2011 |
| 999 | C | 5 | |a 10.1007/978-94-009-1740-8 |1 HW Engl |y 1996 |2 Crossref |u Engl, H.W., Hanke, M., Neubauer, A.: Regularization of Inverse Problems, vol. 375. Kluwer, Dordrecht (1996) |9 -- missing cx lookup -- |
| 999 | C | 5 | |a 10.1109/TMI.2020.2964266 |9 -- missing cx lookup -- |1 J He |p 2076 - |2 Crossref |u He, J., Wang, Y., Ma, J.: Radon inversion via deep learning. IEEE Trans. Med. Imaging 39(6), 2076–2087 (2020) |t IEEE Trans. Med. Imaging |v 39 |y 2020 |
| 999 | C | 5 | |a 10.1109/TIP.2017.2713099 |9 -- missing cx lookup -- |1 KH Jin |p 4509 - |2 Crossref |u Jin, K.H., McCann, M.T., Froustey, E., Unser, M.: Deep convolutional neural network for inverse problems in imaging. IEEE Trans. Image Process. 26(9), 4509–4522 (2017). https://doi.org/10.1109/TIP.2017.2713099 |t IEEE Trans. Image Process. |v 26 |y 2017 |
| 999 | C | 5 | |a 10.1109/CVPR42600.2020.00757 |9 -- missing cx lookup -- |2 Crossref |u Kobler, E., Effland, A., Kunisch, K., Pock, T.: Total deep variation for linear inverse problems. In: CVPR, pp. 7546–7555 (2020) |
| 999 | C | 5 | |2 Crossref |u Latorre, F., Ektekhari, A., Cevher, V. Fast and provable ADMM for learning with generative priors. In: Wallach, H., Larochelle, H., Beygelzimer, A., d'Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32, Curran Associates, Inc., New York (2019) |
| 999 | C | 5 | |a 10.1038/s41597-021-00893-z |9 -- missing cx lookup -- |1 J Leuschner |p 109 - |2 Crossref |u Leuschner, J., Schmidt, M., Baguer, D.O., Maass, P.: LoDoPaB-CT, a benchmark dataset for low-dose computed tomography reconstruction. Sci. Data. 8, 109 (2021). https://doi.org/10.1038/s41597-021-00893-z |t Sci. Data |v 8 |y 2021 |
| 999 | C | 5 | |a 10.3390/jimaging7030044 |9 -- missing cx lookup -- |1 J Leuschner |p 44 - |2 Crossref |u Leuschner, J., Schmidt, M., Ganguly, P.S., Andriiashen, V., Coban, S.B., Denker, A., Bauer, D., Hadjifaradji, A., Batenburg, K.J., Maass, P., van Eijnatten, M.: Quantitative comparison of deep learning-based image reconstruction methods for low-dose and sparse-angle CT applications. J. Imaging 7(3), 44 (2021) |t J. Imaging |v 7 |y 2021 |
| 999 | C | 5 | |a 10.1088/1361-6420/ab6d57 |1 H Li |9 -- missing cx lookup -- |2 Crossref |u Li, H., Schwab, J., Antholzer, S., Haltmeier, M.: NETT: solving inverse problems with deep neural networks. Inverse Prob. 36(6), 065005 (2020) |t Inverse Prob. |v 36 |y 2020 |
| 999 | C | 5 | |a 10.1109/TMI.2019.2910760 |9 -- missing cx lookup -- |1 Y Li |p 2469 - |2 Crossref |u Li, Y., Li, K., Zhang, C., Montoya, J., Chen, G.-H.: Learning to reconstruct computed tomography images directly from sinogram data under a variety of data acquisition conditions. IEEE Trans. Med. Imaging 38(10), 2469–2481 (2019). https://doi.org/10.1109/TMI.2019.2910760 |t IEEE Trans. Med. Imaging |v 38 |y 2019 |
| 999 | C | 5 | |2 Crossref |u Mairal, J., Ponce, J., Sapiro, G., Zisserman, A., Bach, F.: Supervised dictionary learning. In: NeurIPS (2008) |
| 999 | C | 5 | |a 10.1109/ICCV.2017.198 |9 -- missing cx lookup -- |2 Crossref |u Meinhardt, T., Moeller, M., Hazirbas, C., Cremers, D.: Learning proximal operators: using denoising networks for regularizing inverse imaging problems. In: ICCV, pp. 1781–1790 (2017) |
| 999 | C | 5 | |a 10.1109/ICCV.2019.00335 |9 -- missing cx lookup -- |2 Crossref |u Moeller, M., Mollenhoff, T., Cremers, D.: Controlling neural networks via energy dissipation. In: ICCV, pp. 3256–3265 (2019) |
| 999 | C | 5 | |2 Crossref |u Riccio, D., Ehrhardt, M.J., Benning, M.: Regularization of inverse problems: deep equilibrium models versus bilevel learning. arXiv:2206.13193 (2022) |
| 999 | C | 5 | |a 10.1109/ICCV.2017.627 |9 -- missing cx lookup -- |2 Crossref |u Rick Chang, J., Li, C.-L., Poczos, B., Vijaya Kumar, B., Sankaranarayanan, A.C.: One network to solve them all—solving linear inverse problems using deep projection models. In: ICCV, pp. 5888–5897 (2017) |
| 999 | C | 5 | |a 10.1137/16M1102884 |9 -- missing cx lookup -- |1 Y Romano |p 1804 - |2 Crossref |u Romano, Y., Elad, M., Milanfar, P.: The little engine that could: regularization by denoising (RED). SIAM J. Imaging Sci. 10(4), 1804–1844 (2017) |t SIAM J. Imaging Sci. |v 10 |y 2017 |
| 999 | C | 5 | |1 O Ronneberger |y 2015 |2 Crossref |u Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015, pp. 234–241. Springer, Cham (2015) |t Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015 |
| 999 | C | 5 | |a 10.1007/s11263-008-0197-6 |9 -- missing cx lookup -- |1 S Roth |p 205 - |2 Crossref |u Roth, S., Black, M.J.: Fields of experts. Int. J. Comput. Vis. 82(2), 205–229 (2009) |t Int. J. Comput. Vis. |v 82 |y 2009 |
| 999 | C | 5 | |a 10.1117/12.533472 |9 -- missing cx lookup -- |2 Crossref |u Servieres, M.C.J., Normand, N., Subirats, P., Guedon, J.: Some links between continuous and discrete Radon transform. In: Fitzpatrick, J.M., Sonka, M. (eds.) Medical Imaging 2004: Image Processing, vol. 5370, pp. 1961–1971. SPIE, WA, United States. International Society for Optics and Photonics (2004). https://doi.org/10.1117/12.533472 |
| 999 | C | 5 | |a 10.1109/CVPR.2018.00984 |9 -- missing cx lookup -- |2 Crossref |u Ulyanov, D., Vedaldi, A., Lempitsky, V.: Deep image prior. In: CVPR, pp. 9446–9454 (2018) |
| 999 | C | 5 | |a 10.1038/s42256-020-00273-z |9 -- missing cx lookup -- |1 G Wang |p 737 - |2 Crossref |u Wang, G., Ye, J.C., De Man, B.: Deep learning for tomographic image reconstruction. Nat. Mach. Intell. 2(12), 737–748 (2020). https://doi.org/10.1038/s42256-020-00273-z |t Nat. Mach. Intell. |v 2 |y 2020 |
| 999 | C | 5 | |a 10.1109/TMI.2021.3054167 |9 -- missing cx lookup -- |1 J Xiang |p 1329 - |2 Crossref |u Xiang, J., Dong, Y., Yang, Y.: FISTA-Net: learning a fast iterative shrinkage thresholding network for inverse problems in imaging. IEEE Trans. Med. Imaging 40(5), 1329–1339 (2021). https://doi.org/10.1109/TMI.2021.3054167 |t IEEE Trans. Med. Imaging |v 40 |y 2021 |
| 999 | C | 5 | |a 10.1109/TRPMS.2020.2991887 |9 -- missing cx lookup -- |1 M Xu |p 78 - |2 Crossref |u Xu, M., Hu, D., Luo, F., Liu, F., Wang, S., Wu, W.: Limited-angle x-ray CT reconstruction using image gradient $$\ell _0$$-norm with dictionary learning. IEEE Trans. Radiat. Plasma Med. Sci. 5(1), 78–87 (2021). https://doi.org/10.1109/TRPMS.2020.2991887 |t IEEE Trans. Radiat. Plasma Med. Sci. |v 5 |y 2021 |
| 999 | C | 5 | |a 10.1007/s40747-022-00724-7 |9 -- missing cx lookup -- |1 M Zhang |p 5545 - |2 Crossref |u Zhang, M., Gu, S., Shi, Y.: The use of deep learning methods in low-dose computed tomography image reconstruction: a systematic review. Complex Intell. Syst. 8, 5545–5561 (2022). https://doi.org/10.1007/s40747-022-00724-7 |t Complex Intell. Syst. |v 8 |y 2022 |
| 999 | C | 5 | |a 10.1038/nature25988 |9 -- missing cx lookup -- |1 B Zhu |p 487 - |2 Crossref |u Zhu, B., Liu, J.Z., Cauley, S.F., Rosen, B.R., Rosen, M.S.: Image reconstruction by domain-transform manifold learning. Nature 555(7697), 487–492 (2018). https://doi.org/10.1038/nature25988 |t Nature |v 555 |y 2018 |
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