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Respiratory Motion Modelling Using cGANs

Giger, Alina and Sandkühler, Robin and Jud, Christoph and Bauman, Grzegorz and Bieri, Oliver and Salomir, Rares and Cattin, Philippe C.. (2018) Respiratory Motion Modelling Using cGANs. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. Cham, pp. 81-88.

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Official URL: https://edoc.unibas.ch/71152/

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Abstract

Respiratory motion models in radiotherapy are considered as one possible approach for tracking mobile tumours in the thorax and abdomen with the goal to ensure target coverage and dose conformation. We present a patient-specific motion modelling approach which combines navigator-based 4D MRI with recent developments in deformable image registration and deep neural networks. The proposed regression model based on conditional generative adversarial nets (cGANs) is trained to learn the relation between temporally related US and MR navigator images. Prior to treatment, simultaneous ultrasound (US) and 4D MRI data is acquired. During dose delivery, online US imaging is used as surrogate to predict complete 3D MR volumes of different respiration states ahead of time. Experimental validations on three volunteer lung datasets demonstrate the potential of the proposed model both in terms of qualitative and quantitative results, and computational time required.
Faculties and Departments:03 Faculty of Medicine > Departement Biomedical Engineering > Imaging and Computational Modelling > Center for medical Image Analysis & Navigation (Cattin)
UniBasel Contributors:Jud, Christoph
Item Type:Conference or Workshop Item, refereed
Conference or workshop item Subtype:Conference Paper
Publisher:Springer
ISBN:978-3-030-00936-6
e-ISBN:978-3-030-00937-3
Series Name:Lecture Notes in Computer Science
Issue Number:11073
Note:Publication type according to Uni Basel Research Database: Conference paper
Identification Number:
Last Modified:11 Mar 2020 13:26
Deposited On:11 Mar 2020 13:26

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