Accurate cortical surface reconstruction from magnetic resonance imaging (MRI) data is crucial for reliable neuroanatomical analyses. Current methods have to contend with complex cortical geometries, strict topological requirements, and often produce surfaces with overlaps, self-intersections, and topological defects. To overcome these shortcomings, we introduce SimCortex, a deep learning framework that simultaneously reconstructs all brain surfaces (left/right white-matter and pial) from T1-weighted(T1w) MRI volumes while preserving topological properties. Our method first segments the T1w image into a nine-class tissue label map. From these segmentations, we generate subject-specific, collision-free initial surface meshes. These surfaces serve as precise initializations for subsequent multiscale diffeomorphic deformations. Employing stationary velocity fields (SVFs) integrated via scaling-and-squaring, our approach ensures smooth, topology-preserving transformations with significantly reduced surface collisions and self-intersections. Evaluations on standard datasets demonstrate that SimCortex dramatically reduces surface overlaps and self-intersections, surpassing current methods while maintaining state-of-the-art geometric accuracy.

翻译：从磁共振成像（MRI）数据中精确重建皮层表面对于可靠的神经解剖分析至关重要。现有方法必须应对复杂的皮层几何结构、严格的拓扑要求，且常产生具有重叠、自相交及拓扑缺陷的表面。为克服这些不足，我们提出了SimCortex——一种深度学习框架，可在保持拓扑特性的同时，从T1加权（T1w）MRI体数据中同步重建所有脑表面（左/右白质与软脑膜表面）。我们的方法首先将T1w图像分割为九类组织标签图，并基于这些分割结果生成受试者特异性、无碰撞的初始表面网格。这些表面为后续多尺度微分同胚形变提供了精确初始化。通过采用基于缩放平方积分法的静态速度场（SVFs），我们的方法确保了平滑且保持拓扑的形变过程，同时显著减少了表面碰撞与自相交现象。在标准数据集上的评估表明，SimCortex在保持顶尖几何精度的同时，能大幅减少表面重叠与自相交，其性能超越现有方法。