CortexMorph: fast cortical thickness estimation via diffeomorphic registration using VoxelMorph

TL;DR

CortexMorph is a novel deep learning method that rapidly estimates cortical thickness from MRI scans by regressing deformation fields, significantly reducing computation time while maintaining accuracy for detecting cortical atrophy.

Contribution

It introduces CortexMorph, an unsupervised deep learning approach that accelerates diffeomorphic registration-based cortical thickness estimation using VoxelMorph, enabling real-time analysis.

Findings

Estimates cortical thickness in seconds from T1-weighted images.

Maintains sensitivity to cortical atrophy detection.

Validated on OASIS-3 and synthetic phantom datasets.

Abstract

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.