Deep generative models in digital subtraction angiography (DSA) and X-ray angiography: a systematic review
Abstract
Abstract 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.
How strong is the evidence?
Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
Read the paper
Related studies
- Learning from First-Generation Qualitative Approaches in the IS Discipline: An Evolutionary View and Some Implications for Authors and Evaluators (PART 1/2)
- Commentary on the clinical and preclinical dosage limits of interstitially administered magnetic fluids for therapeutic hyperthermia based on current practice and efficacy models
- Prosthesis: New Technological Opportunities and Innovative Biomedical Devices
- The history and future of neural modeling for cochlear implants
- Ryanodine receptors: waking up from refractoriness
- Novel Transgenic Rabbit Model Sheds Light on the Puzzling Role of Matrix Metalloproteinase-12 in Atherosclerosis
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.