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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.<\/jats:p>","DOI":"10.1007\/s10462-026-11498-6","type":"journal-article","created":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T10:43:58Z","timestamp":1768733038000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep generative models in digital subtraction angiography (DSA) and X-ray angiography: a systematic review"],"prefix":"10.1007","volume":"59","author":[{"given":"Qiwen","family":"Xu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"R\u00fcgamer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Bender","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1589-8699","authenticated-orcid":false,"given":"M\u00e1t\u00e9 E.","family":"Maros","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,18]]},"reference":[{"issue":"10","key":"11498_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-025-11311-w","volume":"58","author":"M Alkhodari","year":"2025","unstructured":"Alkhodari M, Alefisha E, Jelinek HF et al (2025) Enhancing ccta image quality: a review of deep learning approaches for advanced artifact correction and denoising. 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