Cortical surface analysis has gained increased prominence, given its potential implications for neurological and developmental disorders. Traditional vision diffusion models, while effective in generating natural images, present limitations in capturing intricate development patterns in neuroimaging due to limited datasets. This is particularly true for generating cortical surfaces where individual variability in cortical morphology is high, leading to an urgent need for better methods to model brain development and diverse variability inherent across different individuals. In this work, we proposed a novel diffusion model for the generation of cortical surface metrics, using modified surface vision transformers as the principal architecture. We validate our method in the developing Human Connectome Project (dHCP), the results suggest our model demonstrates superior performance in capturing the intricate details of evolving cortical surfaces. Furthermore, our model can generate high-quality realistic samples of cortical surfaces conditioned on postmenstrual age(PMA) at scan.

翻译：皮层表面分析因其在神经发育及发育障碍疾病中的潜在意义而日益受到关注。传统视觉扩散模型虽在自然图像生成中表现有效，但因数据集规模有限，在捕捉神经影像中的复杂发育模式方面存在局限。这一问题在皮层表面生成中尤为突出——个体间皮层形态学差异极大，从而亟需更优的建模方法来刻画大脑发育过程及个体间固有的多样性。本研究提出了一种新颖的扩散模型，用于生成皮层表面指标，该模型以改进的表面视觉Transformer为核心架构。通过在发育中人类连接组计划（dHCP）数据集上的验证，结果表明本模型在捕捉动态演变的皮层表面精细结构方面具有优越性能。此外，本模型能够以扫描时的月经后周龄（PMA）为条件，生成高质量的皮层表面真实样本。