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The Circle of Willis (CoW) is the part of cerebral vasculature responsiblefor delivering blood to the brain. Understanding the diverse anatomicalvariations and configurations of the CoW is paramount to advance research oncerebrovascular diseases and refine clinical interventions. However,comprehensive investigation of less prevalent CoW variations remainschallenging because of the dominance of a few commonly occurringconfigurations. We propose a novel generative approach utilising a conditionallatent diffusion model with shape and anatomical guidance to generate realistic3D CoW segmentations, including different phenotypical variations. Ourconditional latent diffusion model incorporates shape guidance to betterpreserve vessel continuity and demonstrates superior performance when comparedto alternative generative models, including conditional variants of 3D GAN and3D VAE. We observed that our model generated CoW variants that are morerealistic and demonstrate higher visual fidelity than competing approaches withan FID score 53\% better than the best-performing GAN-based model.

Shape-guided Conditional Latent Diffusion Models for Synthesising Brain Vasculature