Zheng Hao, Li Hongming, Fan Yong
IEEE Trans Med Imaging. 2025 Jul 2;PP. doi: 10.1109/TMI.2025.3585088.
To achieve fast and accurate cortical surface reconstruction from brain magnetic resonance images (MRIs), we develop a method to jointly reconstruct the inner (white-gray matter interface), outer (pial), and midthickness surfaces, regularized by their interdependence. Rather than reconstructing these surfaces separately without taking into consideration their interdependence as in most existing methods, our method learns three diffeomorphic deformations jointly to optimize the midthickness surface to lie halfway between the inner and outer cortical surfaces and simultaneously deforms it inward and outward towards the inner and outer cortical surfaces, respectively. The surfaces are encouraged to have a spherical topology by regularization terms for non-negativeness of the cortical thickness and symmetric cycle-consistency of the coupled surface deformations. The coupled reconstruction of cortical surfaces also facilitates an accurate estimation of the cortical thickness based on the diffeomorphic deformation trajectory of each vertex on the surfaces. Validation experiments have demonstrated that our method achieves state-of-the-art cortical surface reconstruction performance in terms of accuracy and surface topological correctness on large-scale MRI datasets, including ADNI, HCP, and OASIS. The code is available at: https://github.com/MLDataAnalytics/SurfNet.
为了从脑磁共振图像（MRI）中实现快速准确的皮质表面重建，我们开发了一种方法，通过相互依赖关系进行正则化，联合重建内部（白质-灰质界面）、外部（软脑膜）和中间厚度表面。与大多数现有方法不同，现有方法在不考虑它们相互依赖关系的情况下分别重建这些表面，我们的方法联合学习三种微分同胚变形，以优化中间厚度表面使其位于内部和外部皮质表面之间的中间位置，并同时分别向内部和外部皮质表面向内和向外变形。通过用于皮质厚度非负性的正则化项和耦合表面变形的对称循环一致性，鼓励表面具有球形拓扑结构。皮质表面的耦合重建还有助于基于表面上每个顶点的微分同胚变形轨迹准确估计皮质厚度。验证实验表明，我们的方法在包括ADNI、HCP和OASIS在内的大规模MRI数据集上，在准确性和表面拓扑正确性方面实现了领先的皮质表面重建性能。代码可在以下网址获取：https://github.com/MLDataAnalytics/SurfNet。
