VeCAS: Vessel-Focused Contrast-Free Angiogram Synthesis for Vascular Interventions
Not on the map. Why?
Abstract (arXiv)
X-ray angiography relies on iodinated contrast agents to visualize vascular structures during image-guided interventions. However, contrast administration carries risks of adverse events, motivating the development of contrast-free alternatives. Generating X-ray angiograms directly from non-contrast X-ray images offers a potential solution, but existing approaches remain limited by (i) insufficient control over vascular localization and (ii) inefficient modeling of redundant background content. To address these challenges, we propose VeCAS, a two-stage vessel-focused contrast-free angiogram synthesis framework that separates vascular structure localization from angiographic appearance synthesis. In Stage I, a discriminative model localizes vascular structures in non-contrast X-ray images, while cross-modality latent distillation transfers vessel-sensitive knowledge from X-ray angiograms during training. In Stage II, a vessel-focused inpainting model synthesizes angiographic appearance within the localized vascular regions while preserving the non-vascular background. Experiments on an in-house lower-limb vascular intervention dataset show that VeCAS outperforms the comparison methods in terms of vascular structural fidelity and image quality. Visual Turing tests and physician assessments indicate the perceptual realism of the synthesized angiograms. In addition, robotic guidewire navigation experiments in vascular phantoms show that VeCAS guidance reduces the time to target by 41.4% and the number of operation steps by 40.7% compared with non-contrast guidance. Together, these results suggest the potential of VeCAS to serve as ``meta contrast agent'' for vascular interventions.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 33 of its 59 references that are in the map. First posted on arXiv 24 Aug 2026.
Builds on
33 of 59 references.
References · 33
- 2020Denoising Diffusion Probabilistic Modelscited by 21,076
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
- 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
- 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
- 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,503
- 2016Feature Pyramid Networks for Object Detectioncited by 5,036
- 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,859
- 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
- 2022RePaint: Inpainting using Denoising Diffusion Probabilistic Modelscited by 1,430
- 2023T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Modelscited by 1,351
- 2024WorldSimBench: Towards Video Generation Models as World Simulatorscited by 881
21 more
- 2020Contrastive Learning for Unpaired Image-to-Image Translationcited by 639
- 2019UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentationcited by 586
- 2020clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentationcited by 294
- 2024VM-UNet: Vision Mamba UNet for Medical Image Segmentationcited by 287
- 2018Attention Gated Networks: Learning to Leverage Salient Regions in Medical Imagescited by 272
- 2022BBDM: Image-to-image Translation with Brownian Bridge Diffusion Modelscited by 225
- 2021UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformercited by 197
- 2023Denoising Diffusion Bridge Modelscited by 178
- 2021ResViT: Residual vision transformers for multi-modal medical image synthesiscited by 177
- 2021Breaking the Dilemma of Medical Image-to-image Translationcited by 82
- 2021Knowledge distillation from multi-modal to mono-modal segmentation networkscited by 68
- 2024Self-Consistent Recursive Diffusion Bridge for Medical Image Translationcited by 26
- 2023Prototype Knowledge Distillation for Medical Segmentation with Missing Modalitycited by 24
- 2024LeFusion: Controllable Pathology Synthesis via Lesion-Focused Diffusion Modelscited by 15
- 2023CASOG: Conservative Actor-critic with SmOoth Gradient for Skill Learning in Robot-Assisted Interventioncited by 6
- 2025VasTSD: Learning 3D Vascular Tree-state Space Diffusion Model for Angiography Synthesiscited by 6
- 2024Cross-modality image synthesis from TOF-MRA to CTA using diffusion-based modelscited by 4
- 2025Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentationcited by 4
- 2025Angio-Diff: Learning a Self-Supervised Adversarial Diffusion Model for Angiographic Geometry Generationcited by 4
- 2026MambaLiteUNet: Cross-Gated Adaptive Feature Fusion for Robust Skin Lesion Segmentationcited by 4
- 2024CAS-GAN for Contrast-free Angiography Synthesiscited by 2
Built on by
Not cited yet.