Sparse-view CT Reconstruction using Wasserstein GANs

Franz Thaler; Kerstin Hammernik; Christian Payer; Martin Urschler; Darko Štern

doi:10.1007/978-3-030-00129-2_9

Sparse-view CT Reconstruction using Wasserstein GANs

Franz Thaler^*, Kerstin Hammernik, Christian Payer, Martin Urschler, Darko Štern

^*Corresponding author for this work

Institute of Computer Graphics and Vision (7100)

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Abstract

We propose a 2D computed tomography (CT) slice image reconstruction method from a limited number of projection images using Wasserstein generative adversarial networks (wGAN). Our wGAN optimizes the 2D CT image reconstruction by utilizing an adversarial loss to improve the perceived image quality as well as an L₁ content loss to enforce structural similarity to the target image. We evaluate our wGANs using different weight factors between the two loss functions and compare to a convolutional neural network (CNN) optimized on L₁ and the Filtered Backprojection (FBP) method. The evaluation shows that the results generated by the machine learning based approaches are substantially better than those from the FBP method. In contrast to the blurrier looking images generated by the CNNs trained on L₁, the wGANs results appear sharper and seem to contain more structural information. We show that a certain amount of projection data is needed to get a correct representation of the anatomical correspondences.

Original language	English
Title of host publication	Machine Learning for Medical Image Reconstruction - First International Workshop, MLMIR 2018, Held in Conjunction with MICCAI 2018, Proceedings
Publisher	Springer Verlag Heidelberg
Pages	75-82
Number of pages	8
Volume	11074 LNCS
ISBN (Print)	9783030001285
DOIs	https://doi.org/10.1007/978-3-030-00129-2_9
Publication status	Published - 16 Sept 2018
Event	1st Workshop on Machine Learning for Medical Image Reconstruction, MLMIR 2018 Held in Conjunction with 21st Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 - Granada, Spain Duration: 16 Sept 2018 → 16 Sept 2018

Publication series

Name	Lecture Notes in Computer Science
Volume	11074
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	1st Workshop on Machine Learning for Medical Image Reconstruction, MLMIR 2018 Held in Conjunction with 21st Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
Country/Territory	Spain
City	Granada
Period	16/09/18 → 16/09/18

Keywords

Computed tomography
Convolutional neural networks
Generative adversarial networks
L1 loss
Sparse-view reconstruction

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Fields of Expertise

Information, Communication & Computing

Cooperations

BioTechMed-Graz

Access to Document

10.1007/978-3-030-00129-2_9

Cite this

Thaler, F., Hammernik, K., Payer, C., Urschler, M., & Štern, D. (2018). Sparse-view CT Reconstruction using Wasserstein GANs. In Machine Learning for Medical Image Reconstruction - First International Workshop, MLMIR 2018, Held in Conjunction with MICCAI 2018, Proceedings (Vol. 11074 LNCS, pp. 75-82). (Lecture Notes in Computer Science ; Vol. 11074). Springer Verlag Heidelberg. https://doi.org/10.1007/978-3-030-00129-2_9

Sparse-view CT Reconstruction using Wasserstein GANs. / Thaler, Franz; Hammernik, Kerstin; Payer, Christian et al.
Machine Learning for Medical Image Reconstruction - First International Workshop, MLMIR 2018, Held in Conjunction with MICCAI 2018, Proceedings. Vol. 11074 LNCS Springer Verlag Heidelberg, 2018. p. 75-82 (Lecture Notes in Computer Science ; Vol. 11074).

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Thaler, F, Hammernik, K, Payer, C, Urschler, M & Štern, D 2018, Sparse-view CT Reconstruction using Wasserstein GANs. in Machine Learning for Medical Image Reconstruction - First International Workshop, MLMIR 2018, Held in Conjunction with MICCAI 2018, Proceedings. vol. 11074 LNCS, Lecture Notes in Computer Science , vol. 11074, Springer Verlag Heidelberg, pp. 75-82, 1st Workshop on Machine Learning for Medical Image Reconstruction, MLMIR 2018 Held in Conjunction with 21st Medical Image Computing and Computer Assisted Intervention, MICCAI 2018, Granada, Spain, 16/09/18. https://doi.org/10.1007/978-3-030-00129-2_9

Thaler F, Hammernik K, Payer C, Urschler M, Štern D. Sparse-view CT Reconstruction using Wasserstein GANs. In Machine Learning for Medical Image Reconstruction - First International Workshop, MLMIR 2018, Held in Conjunction with MICCAI 2018, Proceedings. Vol. 11074 LNCS. Springer Verlag Heidelberg. 2018. p. 75-82. (Lecture Notes in Computer Science ). doi: 10.1007/978-3-030-00129-2_9

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abstract = "We propose a 2D computed tomography (CT) slice image reconstruction method from a limited number of projection images using Wasserstein generative adversarial networks (wGAN). Our wGAN optimizes the 2D CT image reconstruction by utilizing an adversarial loss to improve the perceived image quality as well as an L1 content loss to enforce structural similarity to the target image. We evaluate our wGANs using different weight factors between the two loss functions and compare to a convolutional neural network (CNN) optimized on L1 and the Filtered Backprojection (FBP) method. The evaluation shows that the results generated by the machine learning based approaches are substantially better than those from the FBP method. In contrast to the blurrier looking images generated by the CNNs trained on L1, the wGANs results appear sharper and seem to contain more structural information. We show that a certain amount of projection data is needed to get a correct representation of the anatomical correspondences.",

keywords = "Computed tomography, Convolutional neural networks, Generative adversarial networks, L1 loss, Sparse-view reconstruction",

author = "Franz Thaler and Kerstin Hammernik and Christian Payer and Martin Urschler and Darko {\v S}tern",

year = "2018",

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T1 - Sparse-view CT Reconstruction using Wasserstein GANs

AU - Thaler, Franz

AU - Hammernik, Kerstin

AU - Payer, Christian

AU - Urschler, Martin

AU - Štern, Darko

PY - 2018/9/16

Y1 - 2018/9/16

N2 - We propose a 2D computed tomography (CT) slice image reconstruction method from a limited number of projection images using Wasserstein generative adversarial networks (wGAN). Our wGAN optimizes the 2D CT image reconstruction by utilizing an adversarial loss to improve the perceived image quality as well as an L1 content loss to enforce structural similarity to the target image. We evaluate our wGANs using different weight factors between the two loss functions and compare to a convolutional neural network (CNN) optimized on L1 and the Filtered Backprojection (FBP) method. The evaluation shows that the results generated by the machine learning based approaches are substantially better than those from the FBP method. In contrast to the blurrier looking images generated by the CNNs trained on L1, the wGANs results appear sharper and seem to contain more structural information. We show that a certain amount of projection data is needed to get a correct representation of the anatomical correspondences.

AB - We propose a 2D computed tomography (CT) slice image reconstruction method from a limited number of projection images using Wasserstein generative adversarial networks (wGAN). Our wGAN optimizes the 2D CT image reconstruction by utilizing an adversarial loss to improve the perceived image quality as well as an L1 content loss to enforce structural similarity to the target image. We evaluate our wGANs using different weight factors between the two loss functions and compare to a convolutional neural network (CNN) optimized on L1 and the Filtered Backprojection (FBP) method. The evaluation shows that the results generated by the machine learning based approaches are substantially better than those from the FBP method. In contrast to the blurrier looking images generated by the CNNs trained on L1, the wGANs results appear sharper and seem to contain more structural information. We show that a certain amount of projection data is needed to get a correct representation of the anatomical correspondences.

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KW - Convolutional neural networks

KW - Generative adversarial networks

KW - L1 loss

KW - Sparse-view reconstruction

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BT - Machine Learning for Medical Image Reconstruction - First International Workshop, MLMIR 2018, Held in Conjunction with MICCAI 2018, Proceedings

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ER -

Sparse-view CT Reconstruction using Wasserstein GANs

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Fields of Expertise

Cooperations

Access to Document

Other files and links

FWF - FAME - Fully Automatic MRI-based Age Estimation of Adolescents

Cite this

Sparse-view CT Reconstruction using Wasserstein GANs

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Fields of Expertise

Cooperations

Access to Document

Other files and links

Projects

FWF - FAME - Fully Automatic MRI-based Age Estimation of Adolescents

Cite this