TomoRay cranial: synthesis of cranial CT imaging from biplanar radiographs using a generative adversarial network
Question Cranial computed tomography involves radiation, logistical challenges, and access is limited in rural areas. Generating synthetic CT images with deep learning could address these challenges. Findings Two deep-learning models were trained to produce CT images from radiographs. Reconstruction from DRRs is promising, but using real X-rays remains more challenging. Clinical relevance As a proof-of-concept, the models' exact clinical relevance remains to be defined. The proposed approach may broaden access to tomographic neuroimaging, reduce radiation, and enhance intraoperative and maybe even diagnostic support, potentially improving outcomes in neurosurgery and neuro-critical care.
Authors
- Victor E. Staartjes (ORCID: https://orcid.org/0000-0003-1039-2098)
- Carlo Serra (ORCID: https://orcid.org/0000-0002-7305-550X)
- D. Mazzatenta
- Raffaele Da Mutten (ORCID: https://orcid.org/0000-0002-2645-0865)
- Olivier Zanier (ORCID: https://orcid.org/0000-0002-3286-3770)
- Giorgio Palandri (ORCID: https://orcid.org/0000-0002-9306-0242)
- Sven Theiler (ORCID: https://orcid.org/0009-0008-0316-9799)
- Luca Regli
- S Y Ryu
- Alessandro Carretta
Institutions
- University of Zurich (CH)
- Eulji University (KR)
- University of Oxford (GB)
- University Hospital of Zurich (CH)
- Institute for Basic Science (KR)
- Istituto delle Scienze Neurologiche di Bologna (IT)
- University of Bologna (IT)
Publication Details
- Journal
- European Radiology
- Published
- 2026-01-15
- DOI
- https://doi.org/10.1007/s00330-025-12253-1
- Citations
- 2
- Primary Topic
- Generative Adversarial Networks and Image Synthesis
- Type
- article
- Field-Weighted Citation Impact
- 93.68
Funders
- Universität Zürich