The thickness of the cortical band is linked to various neurological and psychiatric conditions, and is often estimated through surface-based methods such as Freesurfer in MRI studies. The DiReCT method, which calculates cortical thickness using a diffeomorphic deformation of the gray-white matter interface towards the pial surface, offers an alternative to surface-based methods. Recent studies using a synthetic cortical thickness phantom have demonstrated that the combination of DiReCT and deep-learning-based segmentation is more sensitive to subvoxel cortical thinning than Freesurfer. While anatomical segmentation of a T1-weighted image now takes seconds, existing implementations of DiReCT rely on iterative image registration methods which can take up to an hour per volume. On the other hand, learning-based deformable image registration methods like VoxelMorph have been shown to be faster than classical methods while improving registration accuracy. This paper proposes CortexMorph, a new method that employs unsupervised deep learning to directly regress the deformation field needed for DiReCT. By combining CortexMorph with a deep-learning-based segmentation model, it is possible to estimate region-wise thickness in seconds from a T1-weighted image, while maintaining the ability to detect cortical atrophy. We validate this claim on the OASIS-3 dataset and the synthetic cortical thickness phantom of Rusak et al.

翻译：皮层带的厚度与多种神经及精神疾病相关，在MRI研究中常通过基于表面的方法（如Freesurfer）进行估计。DiReCT方法通过将灰质-白质界面进行微分同胚形变以匹配软脑膜表面来计算皮层厚度，为基于表面的方法提供了替代方案。近期使用合成皮层厚度体模的研究表明，DiReCT与深度学习分割相结合对亚体素级皮层变薄的检测灵敏度优于Freesurfer。虽然T1加权图像的解剖分割现已能在数秒内完成，但现有DiReCT实现依赖迭代式图像配准方法，每例体数据可能耗时长达一小时。另一方面，基于学习的可变形图像配准方法（如VoxelMorph）已被证明比经典方法更快且配准精度更高。本文提出CortexMorph这一新方法，采用无监督深度学习直接回归DiReCT所需的形变场。通过将CortexMorph与基于深度学习的分割模型结合，可在数秒内从T1加权图像估算区域厚度，同时保持检测皮层萎缩的能力。我们在OASIS-3数据集及Rusak等人的合成皮层厚度体模上验证了这一结论。