CortexMorph: fast cortical thickness estimation via diffeomorphic registration using VoxelMorph
This addresses the problem of time-consuming cortical thickness analysis in MRI studies for researchers and clinicians, though it is incremental as it builds on existing methods like DiReCT and VoxelMorph.
The paper tackles the slow speed of cortical thickness estimation by proposing CortexMorph, which uses unsupervised deep learning to regress deformation fields for DiReCT, enabling region-wise thickness estimation in seconds from T1-weighted images while maintaining detection of cortical atrophy.
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.