Simulating dynamic tumor contrast enhancement in breast MRI using conditional generative adversarial networks
Virtual contrast injection can result in accurate synthetic DCE-MRI images, potentially enhancing breast cancer diagnosis and treatment protocols. We demonstrate that detecting, localizing, and segmenting tumors using synthetic DCE-MRI is feasible and promising, particularly considering patients where contrast agent injection is risky or contraindicated. Jointly generating multiple subsequent DCE-MRI sequences can increase image quality and unlock clinical applications assessing tumor characteristics related to its response to contrast media injection as a pillar for personalized treatment planning.
Authors
- Oliver Díaz (ORCID: https://orcid.org/0000-0001-6789-5177)
- Richard Osuala (ORCID: https://orcid.org/0000-0003-1835-8564)
- Julia A. Schnabel (ORCID: https://orcid.org/0000-0001-6755-8672)
- Apostolia Tsirikoglou (ORCID: https://orcid.org/0000-0003-0298-937X)
- Lidia Garrucho (ORCID: https://orcid.org/0000-0002-3105-2773)
- Daniel M. Lang (ORCID: https://orcid.org/0000-0003-0274-9069)
- Walter Hugo Lopez Pinaya (ORCID: https://orcid.org/0000-0003-3739-1087)
- Karim Lekadir (ORCID: https://orcid.org/0000-0002-9456-1612)
- Saurabh Joshi (ORCID: https://orcid.org/0009-0006-1533-964X)
Institutions
- King's College London (GB)
- Karolinska Institutet (SE)
- Helmholtz Zentrum München (DE)
- Universitat de Barcelona (ES)
Publication Details
- Journal
- Journal of Medical Imaging
- Published
- 2025-06-28
- DOI
- https://doi.org/10.1117/1.jmi.12.s2.s22014
- Citations
- 5
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 12.40
Funders
- Ministerio de Ciencia, Innovación y Universidades
- European Commission
- Radboud Universiteit
- Radboud Universitair Medisch Centrum
- HORIZON EUROPE Framework Programme