Synthetic neuroimaging data can mitigate critical limitations of real-world datasets, including the scarcity of rare phenotypes, domain shifts across scanners, and insufficient longitudinal coverage. However, existing generative models largely rely on weak conditioning signals, such as labels or text, which lack anatomical grounding and often produce biologically implausible outputs. To this end, we introduce Cor2Vox, a cortex-grounded generative framework for brain magnetic resonance image (MRI) synthesis that ties image generation to continuous structural priors of the cerebral cortex. It leverages high-resolution cortical surfaces to guide a 3D shape-to-image Brownian bridge diffusion process, enabling topologically faithful synthesis and precise control over underlying anatomies. To support the generation of new, realistic brain shapes, we developed a large-scale statistical shape model of cortical morphology derived from over 33,000 UK Biobank scans. We validated the fidelity of Cor2Vox based on traditional image quality metrics, advanced cortical surface reconstruction, and whole-brain segmentation quality, outperforming many baseline methods. Across three applications, namely (i) anatomically consistent synthesis, (ii) simulation of progressive gray matter atrophy, and (iii) harmonization of in-house frontotemporal dementia scans with public datasets, Cor2Vox preserved fine-grained cortical morphology at the sub-voxel level, exhibiting remarkable robustness to variations in cortical geometry and disease phenotype without retraining.

翻译：合成神经影像数据能够缓解真实世界数据集的关键局限性，包括罕见表型的稀缺性、跨扫描仪的域偏移以及纵向覆盖不足。然而，现有的生成模型主要依赖于弱条件信号（如标签或文本），这些信号缺乏解剖学基础，常常产生生物学上不可信的输出。为此，我们提出了Cor2Vox——一个基于大脑皮层的生成框架，用于脑磁共振图像（MRI）合成，该框架将图像生成与大脑皮层的连续结构先验信息相绑定。它利用高分辨率皮层表面来引导一个三维形状到图像的布朗桥扩散过程，从而实现拓扑保真的合成以及对底层解剖结构的精确控制。为了支持生成新颖且真实的脑形状，我们基于超过33,000次英国生物银行扫描，开发了一个大规模的大脑皮层形态统计形状模型。我们基于传统图像质量指标、先进的皮层表面重建以及全脑分割质量验证了Cor2Vox的保真度，其性能优于多种基线方法。在三个应用场景中，即（i）解剖学一致的合成，（ii）进行性灰质萎缩的模拟，以及（iii）内部额颞叶痴呆扫描与公共数据集的协调化，Cor2Vox在亚体素水平上保留了细粒度的皮层形态，对皮层几何形状和疾病表型的变异表现出显著的鲁棒性，且无需重新训练。