VasTSD: Learning 3D Vascular Tree-state Space Diffusion Model for Angiography Synthesis
Journal:
arXiv
Published Date:
Mar 17, 2025
Abstract
Angiography imaging is a medical imaging technique that enhances the
visibility of blood vessels within the body by using contrast agents.
Angiographic images can effectively assist in the diagnosis of vascular
diseases. However, contrast agents may bring extra radiation exposure which is
harmful to patients with health risks. To mitigate these concerns, in this
paper, we aim to automatically generate angiography from non-angiographic
inputs, by leveraging and enhancing the inherent physical properties of
vascular structures. Previous methods relying on 2D slice-based angiography
synthesis struggle with maintaining continuity in 3D vascular structures and
exhibit limited effectiveness across different imaging modalities. We propose
VasTSD, a 3D vascular tree-state space diffusion model to synthesize
angiography from 3D non-angiographic volumes, with a novel state space
serialization approach that dynamically constructs vascular tree topologies,
integrating these with a diffusion-based generative model to ensure the
generation of anatomically continuous vasculature in 3D volumes. A pre-trained
vision embedder is employed to construct vascular state space representations,
enabling consistent modeling of vascular structures across multiple modalities.
Extensive experiments on various angiographic datasets demonstrate the
superiority of VasTSD over prior works, achieving enhanced continuity of blood
vessels in synthesized angiographic synthesis for multiple modalities and
anatomical regions.
