Deep generative models in digital subtraction angiography (DSA) and X-ray angiography: a systematic review
Deep generative models, such as generative adversarial networks (GANs), variational autoencoders (VAEs), and diffusion models, have demonstrated remarkable success in static medical imaging tasks, including image synthesis, domain translation, and data augmentation. However, their application to dyn...
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| Hauptverfasser: | , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
18 January 2026
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| In: |
Artificial intelligence review
Year: 2026, Jahrgang: 59, Heft: 2, Pages: 1-32 |
| ISSN: | 1573-7462 |
| DOI: | 10.1007/s10462-026-11498-6 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1007/s10462-026-11498-6 Verlag, kostenfrei, Volltext: https://link.springer.com/article/10.1007/s10462-026-11498-6 
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| Verfasserangaben: | Qiwen Xu, David Rügamer, Andreas Bender, Máté E. Maros |
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| 245 | 1 | 0 | |a Deep generative models in digital subtraction angiography (DSA) and X-ray angiography |b a systematic review |c Qiwen Xu, David Rügamer, Andreas Bender, Máté E. Maros |
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| 520 | |a Deep generative models, such as generative adversarial networks (GANs), variational autoencoders (VAEs), and diffusion models, have demonstrated remarkable success in static medical imaging tasks, including image synthesis, domain translation, and data augmentation. However, their application to dynamic vascular modalities such as digital subtraction angiography (DSA) and X-ray angiography (XA) has received comparatively limited attention. This systematic review synthesizes 20 peer-reviewed studies published between January 2018 and July 2025, offering a comprehensive analysis of generative model usage in angiographic imaging. We categorize methods by model type, learning paradigm, and application focus, covering tasks such as image generation, vessel segmentation, and stenosis detection. GANs dominate the field, while diffusion models show emerging promise. Key limitations include the scarcity of public datasets, inconsistent evaluation metrics, and limited code availability, which hinder reproducibility and benchmarking. We conclude by outlining methodological trends and highlighting future directions, including the development of anatomically conditioned generative models and the need for large-scale, open-access datasets to support robust evaluation and clinical translation. | ||
| 650 | 4 | |a Diffusion models | |
| 650 | 4 | |a Digital subtraction angiography | |
| 650 | 4 | |a Generative models | |
| 650 | 4 | |a Medical image synthesis | |
| 650 | 4 | |a Vessel segmentation | |
| 650 | 4 | |a X-ray angiography | |
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