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Computed tomography reconstruction using deep image prior and learned reconstruction methods. (English) Zbl 07252737
MSC:
68T07 Artificial neural networks and deep learning
92C Physiological, cellular and medical topics
65J Numerical analysis in abstract spaces
Software:
Adam; GitHub; PyTorch
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References:
[1] Adler J and Öktem O 2017 Solving ill-posed inverse problems using iterative deep neural networks Inverse Problems33 124007 · Zbl 1394.92070
[2] Adler J and Öktem O 2018 Deep Bayesian inversion (arXiv:1811.0591)
[3] Adler J and Öktem O 2018 Learned primal-dual reconstruction IEEE Trans. Med. Imaging37 1322-32
[4] Antun V, Renna F, Poon C, Adcock B and Hansen A C 2020 On instabilities of deep learning in image reconstruction and the potential costs of AI Proc. Natl Acad. Sci. (https://www.pnas.org/content/early/2020/05/08/1907377117/tab-article-info)
[5] Armato S G III et al 2011 The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans Med. Phys.38 915-31
[6] Arridge S, Maass P, Öktem O and Schönlieb C-B 2019 Solving inverse problems using data-driven models Acta Numerica28 1-174 · Zbl 1429.65116
[7] Bora A, Jalal A, Price E and Dimakis A G 2017 Compressed sensing using generative models Proc. 34th Int. Conf. on Machine Learning, ICML 2017(Sydney, NSW, Australia, 6-11 August 2017) pp 537-46
[8] Bubba T A, Kutyniok G, Lassas M, März M, Samek W, Siltanen S and Srinivasan V 2019 Learning the invisible: a hybrid deep learning-shearlet framework for limited angle computed tomography Inverse Problems35 064002 · Zbl 1416.92099
[9] Buzug T M 2008 Computed Tomography: From Photon Statistics to Modern Cone-Beam CT (Berlin, Heidelberg: Springer)
[10] Chakrabarty P and Maji S 2019 The spectral bias of the deep image prior (arXiv:1912.08905)
[11] Chen H, Zhang Y, Kalra M K, Lin F, Chen Y, Liao P, Zhou J and Wang G 2017 Low-dose CT with a residual encoder-decoder convolutional neural network IEEE Trans. Med. Imaging36 2524-35
[12] Cheng Z, Gadelha M, Maji S and Sheldon D 2019 A bayesian perspective on the deep image prior The IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)
[13] Denker A, Schmidt M, Leuschner J, Maass P and Behrmann J 2020 Conditional normalizing flows for low-dose computed tomography image reconstruction (arXiv:2006.06270)
[14] Dittmer S, Kluth T, Maass P and Otero Baguer D 2019 Regularization by architecture: a deep prior approach for inverse problems J. Math. Imaging Vis.62 456-70 · Zbl 1434.68505
[15] Donoho D L and Johnstone I M 1994 Ideal spatial adaptation by wavelet shrinkage Biometrika81 425-55 · Zbl 0815.62019
[16] Engl H W, Hanke M and Neubauer A 1996 Regularization of Inverse Problems (Dordrecht: Kluwer) Mathematics and its Applications vol 375
[17] Gandelsman Y, Shocher A and Irani M 2019 “Double-DIP”: Unsupervised image decomposition via coupled deep-image-priors 2019 IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR) pp 11018-27
[18] Gong K, Catana C, Qi J and Li Q 2019 PET image reconstruction using deep image prior IEEE Trans. Med. Imaging38 1655-65
[19] Gottschling N M, Antun V, Adcock B and Hansen A C 2020 The troublesome kernel: why deep learning for inverse problems is typically unstable (arXiv:2001.01258)
[20] Gupta H, Jin K H, Nguyen H Q, McCann M T and Unser M 2018 CNN-based projected gradient descent for consistent CT image reconstruction IEEE Trans. Med. Imaging37 1440-53
[21] Hauptmann A, Lucka F, Betcke M, Huynh N, Adler J, Cox B, Beard P, Ourselin S and Arridge S 2018 Model-based learning for accelerated, limited-view 3-D photoacoustic tomography IEEE Trans. Med. Imaging37 1382-93
[22] He J, Wang Y and Ma J 2020 Radon inversion via deep learning IEEE Trans. Med. Imaging39 2076-87
[23] He K, Zhang X, Ren S and Sun J 2016 Deep residual learning for image recognition 2016 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) pp 770-8
[24] Heckel R and Soltanolkotabi M 2020 Denoising and regularization via exploiting the structural bias of convolutional generators Int. Conf. on Learning Representations
[25] Hofmann B, Kaltenbacher B, Pöschl C and Scherzer O 2007 A convergence rates result for tikhonov regularization in banach spaces with non-smooth operators Inverse Problems23 987-1010 · Zbl 1131.65046
[26] Hoyer S, Sohl-Dickstein J and Greydanus S 2019 Neural reparameterization improves structural optimization (arXiv:1909.04240)
[27] Jin K H, Gupta H, Yerly J, Stuber M and Unser M 2019 Time-dependent deep image prior for dynamic MRI (arXiv:1910.01684)
[28] Jin K H, McCann M T, Froustey E and Unser M 2017 Deep convolutional neural network for inverse problems in imaging IEEE Trans. Image Process.26 4509-22 · Zbl 1409.94275
[29] Kingma D P and Ba J 2015 Adam: a method for stochastic optimization 3rd Int. Conf. on Learning Representations, ICLR 2015(San Diego, CA, USA, May 7-9, 2015) ed Y Bengio and Y LeCun
[30] Knoll F et al 2020 fastMRI: a publicly available raw k-space and DICOM dataset of knee images for accelerated MR image reconstruction using machine learning Radiology. Artificial intelligence2 e190007
[31] Lempitsky V, Vedaldi A and Ulyanov D 2018 Deep image prior 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition pp 9446-54
[32] Leuschner J, Schmidt M, Otero Baguer D and Maass P 2019 The LoDoPaB-CT dataset: a benchmark dataset for low-dose CT reconstruction methods (arXiv:1910.01113)
[33] Leuschner J, Schmidt M and Erzmann D 2019 Deep inversion validation library https://github.com/jleuschn/dival
[34] Li H, Schwab J, Antholzer S and Haltmeier M 2020 NETT: Solving inverse problems with deep neural networks Inverse Problems (accepted manuscript) · Zbl 07211715
[35] Liu J, Sun Y, Xu X and Kamilov U S 2019 Image restoration using total variation regularized deep image prior ICASSP 2019—2019 IEEE Int. Conf. on Acoustics Speech and Signal Processing (ICASSP) pp 7715-9
[36] Louis A K 1989 Inverse und schlecht gestellte Probleme Vieweg+Teubner Verlag
[37] Lunz S, Öktem O and Schönlieb C-B 2018 Adversarial regularizers in inverse problems Proc. 32nd Int. Conf. on Neural Information Processing Systems, NIPS’18 Red Hook, NY, USA pp 8516-25
[38] Mataev G, Elad M and Milanfar P 2019 DeepRED: deep image prior powered by RED (arXiv:1903.10176)
[39] Zuhair Nashed M 1987 A new approach to classification and regularization of ill-posed operator equations Inverse and Ill-Posed Problems ed H W Engl and C W Groetsch (New York: Academic) pp 53-75
[40] Natterer F 2001 The mathematics of computerized tomography Classics in Applied Mathematics (Philadelphia: Society for Industrial and Applied Mathematics) · Zbl 0973.92020
[41] Paszke A et al 2017 Automatic differentiation in PyTorch NIPS 2017 Workshop on Autodiff
[42] Pelt D, Batenburg K and Sethian J 2018 Improving tomographic reconstruction from limited data using mixed-scale dense convolutional neural networks J. Imaging4 128
[43] Radon J 1986 On the determination of functions from their integral values along certain manifolds IEEE Trans. Med. Imaging5 170-6
[44] Rieder A 2003 Keine Probleme mit inversen Problemen: eine Einführung in ihre stabile Lösung (Braunschweig: Vieweg) · Zbl 1057.65035
[45] Ronneberger O, Fischer P and Brox T 2015 U-Net: convolutional networks for biomedical image segmentation Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015 ed N Navab, J Hornegger, W M Wells and A F Frangi (Berlin: Springer) pp 234-41
[46] Schwab J, Antholzer S and Haltmeier M 2019 Deep null space learning for inverse problems: convergence analysis and rates Inverse Problems35 025008 · Zbl 07027425
[47] Van Veen D, Jalal A, Soltanolkotabi M, Price E, Vishwanath S and Dimakis A G 2018 Compressed sensing with deep image prior and learned regularization (arXiv:1806.06438)
[48] Yang Q et al 2018 Low-dose CT image denoising using a generative adversarial network with wasserstein distance and perceptual loss IEEE Trans. Med. Imaging37 1348-57
[49] Zhu B, Liu J Z, Cauley S F, Rosen B R and Rosen M S 2018 Image reconstruction by domain-transform manifold learning Nature555 487-92
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