Shape-guided Conditional Latent Diffusion Models for Synthesising Brain Vasculature

TL;DR

This paper introduces a shape-guided conditional latent diffusion model that generates realistic 3D brain vasculature structures, capturing diverse anatomical variations to aid cerebrovascular research and clinical applications.

Contribution

It presents a novel diffusion-based generative model with shape guidance for realistic 3D brain vasculature synthesis, outperforming existing generative models in fidelity.

Findings

Model achieves 53% better FID score than the best GAN-based approach.

Generated vasculature variants are more realistic and diverse.

Shape guidance improves vessel continuity and anatomical accuracy.

Abstract

The Circle of Willis (CoW) is the part of cerebral vasculature responsible for delivering blood to the brain. Understanding the diverse anatomical variations and configurations of the CoW is paramount to advance research on cerebrovascular diseases and refine clinical interventions. However, comprehensive investigation of less prevalent CoW variations remains challenging because of the dominance of a few commonly occurring configurations. We propose a novel generative approach utilising a conditional latent diffusion model with shape and anatomical guidance to generate realistic 3D CoW segmentations, including different phenotypical variations. Our conditional latent diffusion model incorporates shape guidance to better preserve vessel continuity and demonstrates superior performance when compared to alternative generative models, including conditional variants of 3D GAN and 3D VAE. We observed that our model generated CoW variants that are more realistic and demonstrate higher visual fidelity than competing approaches with an FID score 53\% better than the best-performing GAN-based model.