mlsanityData to 14 Oct 2026

Semantically Conditioned Diffusion Models for Cerebral DSA Synthesis

arXiv 2602.11703 ↗First posted 12 Feb 2026cs.CV cs.AIcited by 0

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Digital subtraction angiography (DSA) plays a central role in the diagnosis and treatment of cerebrovascular disease, yet its invasive nature and high acquisition cost severely limit large-scale data collection and public data sharing. Therefore, we developed a semantically conditioned latent diffusion model (LDM) that synthesizes arterial-phase cerebral DSA frames under explicit control of anatomical circulation (anterior vs. posterior) and canonical C-arm positions. We curated a large single-centre DSA dataset of 99,349 frames and trained a conditional LDM using text embeddings that encoded anatomy and acquisition geometry. To assess clinical realism, four medical experts, including two neuroradiologists, one neurosurgeon, and one internal medicine expert, systematically rated 400 synthetic DSA images using a 5-grade Likert scale for evaluating proximal large, medium, and small peripheral vessels. The generated images achieved image-wise overall Likert scores ranging from 3.1 to 3.3, with high inter-rater reliability (ICC(2,k) = 0.80--0.87). Distributional similarity to real DSA frames was supported by a low median Fr'echet inception distance (FID) of 15.27. Our results indicate that semantically controlled LDMs can produce realistic synthetic DSAs suitable for downstream algorithm development, research, and training.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 11 of its 23 references that are in the map. First posted on arXiv 12 Feb 2026.

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11 of 23 references.

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  1. 2024Cross-modality image synthesis from TOF-MRA to CTA using diffusion-based models16 Sep
  2. 2023Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis20 Mar
  3. 2022Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models7 Jun
  4. 2022Diffusion Models for Medical Anomaly Detection8 Mar

Other references · 7

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  4. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  5. 2022Diffusion Adversarial Representation Learning for Self-supervised Vessel Segmentationcited by 55
  6. 2023DIAS: A Dataset and Benchmark for Intracranial Artery Segmentation in DSA sequencescited by 10
  7. 2024DSCA: A Digital Subtraction Angiography Sequence Dataset and Spatio-Temporal Model for Cerebral Artery Segmentationcited by 8

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