IEEE VIS 2025

AortaDiff: Volume-Guided Conditional Diffusion Models for Multi-Branch Aortic Surface Generation

Delin An, Pan Du, Jian-Xun Wang, and Chaoli Wang

An end-to-end path from volumetric medical images to analysis-ready, multi-branch aortic surface geometry.

Loading subject005 V2 mesh…

Illustrative transition · stylized volume, subject005 V2 surface mesh

Discover AortaDiff+

01 · What’s next

AortaDiff+

AortaDiff+ is under active development.

02 · Overview

From an image volume to a simulation-ready surface.

AortaDiff conditions a diffusion model on CT or MRI volume information to generate multi-branch aortic centerlines. The resulting contours are reconstructed as a smooth surface suitable for downstream analysis.

Why it matters

High-quality vascular geometry is a prerequisite for computational fluid dynamics, yet manual and semi-automatic mesh preparation can be time-consuming and sensitive to image quality.

Data provenance

All subject cases and interactive meshes displayed on this homepage are derived from the AortaSeg24 dataset: Multi-Class Segmentation of Aortic Branches and Zones in CTA.

03 · Contributions

Volume-aware generation, built for vascular geometry.

01

Volume-guided diffusion

A conditional diffusion formulation uses volumetric context to generate the complete aortic centerline tree.

02

Multi-branch reconstruction

Generated centerlines guide contour extraction before a smooth NURBS surface is reconstructed across the aorta and its branches.

03

CFD-oriented evaluation

Surface quality and OpenFOAM simulations are compared with reference geometry using velocity, pressure, and wall shear stress.

04 · Interactive comparison

One difficult junction.
Two generations.

Subject005 exposes a V1 failure case near the junction of the right subclavian artery (RSA) and right common carotid artery (RCCA). V2 (AortaDiff+) no longer shows this unintended merge in this case.

V1 · failure case

subject005

RSA / RCCA region
Loads when visible

The warm red vessels and ROI marker call attention to the unintended V1 connection.

V2 · updated result

subject005

RSA / RCCA region
Loads when visible

The corrected region is highlighted in teal. Camera movement and zoom are synchronized across both viewers.

Drag to rotate · Scroll to zoom · Press R to reset

06 · Method

An end-to-end geometry generation pipeline.

AortaDiff pipeline: medical image volume, volume-guided conditional diffusion of centerlines, contour extraction, and NURBS surface reconstruction
Pipeline overview A volume guides centerline generation; the predicted centerlines support contour extraction and NURBS surface reconstruction.

07 · Results & CFD

Geometry evaluated all the way to hemodynamics.

The paper evaluates generated meshes against reference surfaces and carries them into OpenFOAM simulations, comparing velocity, pressure, and wall shear stress.

Qualitative comparison of generated aortic meshes across methods and reference meshes
Surface generation Qualitative comparison across normal and pathological aortic geometries from the study.
CFD comparison of velocity, pressure, and wall shear stress on generated and reference aortic meshes
CFD validation Velocity, pressure, and wall shear stress comparisons between generated and reference geometry.

08 · Video

See the complete AortaDiff story.

A paper presentation covering the problem, the volume-guided diffusion pipeline, mesh evaluation, and downstream CFD analysis.

04:57 · presentation video

09 · Open collaboration

Help us evaluate the next 1,000+ aortic meshes.

We plan to generate and evaluate 1,000+ aortic meshes and welcome collaborations with vascular imaging, cardiovascular mechanics, CFD, and clinical research teams.

Vascular imagingCardiovascular mechanicsCFDClinical research

10 · Citation

Build on AortaDiff.

BibTeX
@article{an2026aortadiff,
  title   = {AortaDiff: Volume-Guided Conditional Diffusion Models for Multi-Branch Aortic Surface Generation},
  author  = {An, Delin and Du, Pan and Wang, Jian-Xun and Wang, Chaoli},
  journal = {IEEE Transactions on Visualization and Computer Graphics},
  volume  = {32},
  number  = {1},
  pages   = {922--932},
  year    = {2026},
  doi     = {10.1109/TVCG.2025.3634652}
}