HUG-VAS: A Hierarchical NURBS-Based Generative Model for Aortic Geometry Synthesis and Controllable Editing

TL;DR

HUG-VAS is a novel hierarchical NURBS-based generative model that synthesizes realistic, CFD-ready aortic geometries from limited clinical data, enabling controllable editing and robust reconstruction.

Contribution

It introduces a unified NURBS-based framework combining hierarchical diffusion models with image-derived priors for vascular shape synthesis and editing.

Findings

Generates anatomically realistic multi-branch aortas.

Achieves biomarker distribution similarity to source cohort.

Supports zero-shot conditional shape generation from sparse inputs.

Abstract

Accurate, patient-specific vascular geometry is pivotal for diagnosis, planning, and device design, yet existing statistical shape modeling (SSM) pipelines rely on linear priors and topology-specific preprocessing that limit realism, scalability, and interoperability. We present HUG-VAS, a Hierarchical NURBS Generative framework for Vascular models, that unifies NURBS-based 3D shape encoding with diffusion-based generative modeling to synthesize fine-grained, CFD-ready aortic anatomies. HUG-VAS factorizes shape into (i) vessel centerlines generated by a denoising diffusion model and (ii) cross-sectional radius profiles synthesized by a classifier-free guided diffusion model conditioned on the centerline, thereby decoupling and preserving stochastic variability across these two anatomical layers. Beyond unconditional synthesis, we enable training-free, zero-shot conditional generation via diffusion posterior sampling from image-derived prompts (e.g., sparse 3D points, slice contours, or partial surface patches), supporting interactive semi-automatic segmentation, editing and robust reconstruction under degraded imaging. Trained on 21 patient-specific MRA cases, HUG-VAS generates multi-branch aortas with supra-aortic vessels whose biomarker distributions closely match the source cohort, and whose watertight NURBS outputs directly integrate with downstream CFD solvers. To our knowledge, this is the first SSM framework that bridges image-derived priors and generative shape synthesis through a unified combination of NURBS parameterization, hierarchical diffusion, and DPS, enabling a practical path from limited clinical anatomic information to simulation-ready vascular geometry.