X2Vision: 3D CT Reconstruction from Biplanar X-Rays with Deep Structure Prior

Alexandre Cafaro; Quentin Spinat; Amaury Leroy; Pauline Maury; Alexandre Munoz; Guillaume Beldjoudi; Charlotte Robert; Eric Deutsch; Vincent Grégoire; Vincent Lepetit; Nikos Paragios

doi:10.1007/978-3-031-43999-5_66

X2Vision: 3D CT Reconstruction from Biplanar X-Rays with Deep Structure Prior

Alexandre Cafaro, Quentin Spinat, Amaury Leroy, Pauline Maury, Alexandre Munoz, Guillaume Beldjoudi, Charlotte Robert, Eric Deutsch, Vincent Grégoire, Vincent Lepetit, Nikos Paragios

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › !!Conference contribution › Revue par des pairs

Résumé

We propose an unsupervised deep learning method to reconstruct a 3D tomographic image from biplanar X-rays, to reduce the number of required projections, the patient dose, and the acquisition time. To address this ill-posed problem, we introduce prior knowledge of anatomic structures by training a generative model on 3D CTs of head and neck. We optimize the latent vectors of the generative model to recover a volume that both integrates this prior knowledge and ensures consistency between the reconstructed image and input projections. Our method outperforms recent methods in terms of reconstruction error while being faster and less radiating than current clinical workflow. We evaluate our method in a clinical configuration for radiotherapy.

langue originale	Anglais
titre	Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings
rédacteurs en chef	Hayit Greenspan, Hayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor
Editeur	Springer Science and Business Media Deutschland GmbH
Pages	699-709
Nombre de pages	11
ISBN (imprimé)	9783031439988
Les DOIs	https://doi.org/10.1007/978-3-031-43999-5_66
état	Publié - 1 janv. 2023
Evénement	26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023 - Vancouver, Canada Durée: 8 oct. 2023 → 12 oct. 2023

Série de publications

Nom	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14229 LNCS
ISSN (imprimé)	0302-9743
ISSN (Electronique)	1611-3349

Une conférence

Une conférence	26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023
Pays/Territoire	Canada
La ville	Vancouver
période	8/10/23 → 12/10/23

Accès au document

10.1007/978-3-031-43999-5_66

Autres fichiers et liens

Lien vers la publication dans Scopus

Contient cette citation

Cafaro, A., Spinat, Q., Leroy, A., Maury, P., Munoz, A., Beldjoudi, G., Robert, C., Deutsch, E., Grégoire, V., Lepetit, V., & Paragios, N. (2023). X2Vision: 3D CT Reconstruction from Biplanar X-Rays with Deep Structure Prior. Dans H. Greenspan, H. Greenspan, A. Madabhushi, P. Mousavi, S. Salcudean, J. Duncan, T. Syeda-Mahmood, & R. Taylor (eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings (p. 699-709). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol 14229 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-43999-5_66

Cafaro, Alexandre ; Spinat, Quentin ; Leroy, Amaury et al. / X2Vision : 3D CT Reconstruction from Biplanar X-Rays with Deep Structure Prior. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings. Editeur / Hayit Greenspan ; Hayit Greenspan ; Anant Madabhushi ; Parvin Mousavi ; Septimiu Salcudean ; James Duncan ; Tanveer Syeda-Mahmood ; Russell Taylor. Springer Science and Business Media Deutschland GmbH, 2023. p. 699-709 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "We propose an unsupervised deep learning method to reconstruct a 3D tomographic image from biplanar X-rays, to reduce the number of required projections, the patient dose, and the acquisition time. To address this ill-posed problem, we introduce prior knowledge of anatomic structures by training a generative model on 3D CTs of head and neck. We optimize the latent vectors of the generative model to recover a volume that both integrates this prior knowledge and ensures consistency between the reconstructed image and input projections. Our method outperforms recent methods in terms of reconstruction error while being faster and less radiating than current clinical workflow. We evaluate our method in a clinical configuration for radiotherapy.",

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Cafaro, A, Spinat, Q, Leroy, A, Maury, P, Munoz, A, Beldjoudi, G, Robert, C, Deutsch, E, Grégoire, V, Lepetit, V & Paragios, N 2023, X2Vision: 3D CT Reconstruction from Biplanar X-Rays with Deep Structure Prior. Dans H Greenspan, H Greenspan, A Madabhushi, P Mousavi, S Salcudean, J Duncan, T Syeda-Mahmood & R Taylor (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), VOL. 14229 LNCS, Springer Science and Business Media Deutschland GmbH, p. 699-709, 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, Vancouver, Canada, 8/10/23. https://doi.org/10.1007/978-3-031-43999-5_66

X2Vision: 3D CT Reconstruction from Biplanar X-Rays with Deep Structure Prior. / Cafaro, Alexandre; Spinat, Quentin; Leroy, Amaury et al.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings. Ed. / Hayit Greenspan; Hayit Greenspan; Anant Madabhushi; Parvin Mousavi; Septimiu Salcudean; James Duncan; Tanveer Syeda-Mahmood; Russell Taylor. Springer Science and Business Media Deutschland GmbH, 2023. p. 699-709 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol 14229 LNCS).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › !!Conference contribution › Revue par des pairs

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AU - Maury, Pauline

AU - Munoz, Alexandre

AU - Beldjoudi, Guillaume

AU - Robert, Charlotte

AU - Deutsch, Eric

AU - Grégoire, Vincent

AU - Lepetit, Vincent

AU - Paragios, Nikos

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

PY - 2023/1/1

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N2 - We propose an unsupervised deep learning method to reconstruct a 3D tomographic image from biplanar X-rays, to reduce the number of required projections, the patient dose, and the acquisition time. To address this ill-posed problem, we introduce prior knowledge of anatomic structures by training a generative model on 3D CTs of head and neck. We optimize the latent vectors of the generative model to recover a volume that both integrates this prior knowledge and ensures consistency between the reconstructed image and input projections. Our method outperforms recent methods in terms of reconstruction error while being faster and less radiating than current clinical workflow. We evaluate our method in a clinical configuration for radiotherapy.

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KW - Deep generative model

KW - Image reconstruction

KW - Inverse problem

KW - Sparse sampling

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings

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PB - Springer Science and Business Media Deutschland GmbH

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Cafaro A, Spinat Q, Leroy A, Maury P, Munoz A, Beldjoudi G et al. X2Vision: 3D CT Reconstruction from Biplanar X-Rays with Deep Structure Prior. Dans Greenspan H, Greenspan H, Madabhushi A, Mousavi P, Salcudean S, Duncan J, Syeda-Mahmood T, Taylor R, rédacteurs en chef, Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 699-709. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-43999-5_66