Unified Brain Surface and Volume Registration

TL;DR

This paper introduces NeurAlign, a deep learning framework that jointly registers brain surface and volume MRI scans using a unified spherical representation, achieving superior accuracy and speed over traditional methods.

Contribution

NeurAlign is the first method to integrate surface and volume registration into a single deep learning framework with a spherical intermediate space.

Findings

Outperforms classical and ML registration methods with up to 7-point Dice score improvement.

Significantly faster inference than standard registration techniques.

Requires no additional inputs beyond MRI scans.

Abstract

Accurate registration of brain MRI scans is fundamental for cross-subject analysis in neuroscientific studies. This involves aligning both the cortical surface of the brain and the interior volume. Traditional methods treat volumetric and surface-based registration separately, which often leads to inconsistencies that limit downstream analyses. We propose a deep learning framework, NeurAlign, that registers $3$D brain MRI images by jointly aligning both cortical and subcortical regions through a unified volume-and-surface-based representation. Our approach leverages an intermediate spherical coordinate space to bridge anatomical surface topology with volumetric anatomy, enabling consistent and anatomically accurate alignment. By integrating spherical registration into the learning, our method ensures geometric coherence between volume and surface domains. In a series of experiments on both in-domain and out-of-domain datasets, our method consistently outperforms both classical and machine learning-based registration methods -- improving the Dice score by up to 7 points while maintaining regular deformation fields. Additionally, it is orders of magnitude faster than the standard method for this task, and is simpler to use because it requires no additional inputs beyond an MRI scan. With its superior accuracy, fast inference, and ease of use, NeurAlign sets a new standard for joint cortical and subcortical registration.