Neural deformation fields for template-based reconstruction of cortical surfaces from MRI (2401.12938v2)

Abstract: The reconstruction of cortical surfaces is a prerequisite for quantitative analyses of the cerebral cortex in magnetic resonance imaging (MRI). Existing segmentation-based methods separate the surface registration from the surface extraction, which is computationally inefficient and prone to distortions. We introduce Vox2Cortex-Flow (V2C-Flow), a deep mesh-deformation technique that learns a deformation field from a brain template to the cortical surfaces of an MRI scan. To this end, we present a geometric neural network that models the deformation-describing ordinary differential equation in a continuous manner. The network architecture comprises convolutional and graph-convolutional layers, which allows it to work with images and meshes at the same time. V2C-Flow is not only very fast, requiring less than two seconds to infer all four cortical surfaces, but also establishes vertex-wise correspondences to the template during reconstruction. In addition, V2C-Flow is the first approach for cortex reconstruction that models white matter and pial surfaces jointly, therefore avoiding intersections between them. Our comprehensive experiments on internal and external test data demonstrate that V2C-Flow results in cortical surfaces that are state-of-the-art in terms of accuracy. Moreover, we show that the established correspondences are more consistent than in FreeSurfer and that they can directly be utilized for cortex parcellation and group analyses of cortical thickness.

• The paper introduces Vox2Cortex-Flow, which uses a deep mesh-deformation framework to reconstruct cortical surfaces with remarkable speed and accuracy.
• The method jointly models white matter and pial surfaces by integrating convolutional and graph convolutional layers to maintain vertex-wise correspondence.
• Experimental results demonstrate that Vox2Cortex-Flow outperforms traditional tools like FreeSurfer, reducing computation time by up to 10,000 times and enhancing precision in complex regions.

Overview of "Neural Deformation Fields for Template-Based Reconstruction of Cortical Surfaces from MRI"

The paper "Neural deformation fields for template-based reconstruction of cortical surfaces from MRI" by Bongratz et al. proposes a novel approach called Vox2Cortex-Flow (V2C-Flow), aimed at efficiently reconstructing cortical surfaces from magnetic resonance imaging (MRI). The method addresses key limitations of traditional segmentation-based methods that suffer from inefficiencies and distortions due to the separation of surface registration from surface extraction.

Methodological Advances

V2C-Flow employs a deep mesh-deformation framework, which learns a deformation field from a brain template to the cortical surfaces using a geometric neural network. This network models the ordinary differential equation (ODE) that describes the deformation in a continuous manner. The architecture integrates convolutional and graph-convolutional layers, enabling simultaneous processing of images and meshes.

Key aspects of the proposed method include:

1. Vertex-Wise Correspondence: V2C-Flow maintains correspondences between the template and reconstructed surfaces, allowing direct use for cortex parcellation and group analyses.
2. Joint Modeling of Surfaces: Unlike existing methods, V2C-Flow simultaneously models white matter and pial surfaces, reducing intersection errors and improving anatomical representation.
3. Efficiency: The method is computationally efficient, reconstructing all four cortical surfaces in under two seconds due to optimized implementation and parallel processing on GPUs.

Experimental Results and Comparisons

The authors conducted comprehensive experiments on both internal and external datasets, demonstrating that V2C-Flow achieves state-of-the-art accuracy in cortical surface reconstruction. The method was shown to outperform traditional tools like FreeSurfer in both accuracy and speed, with a notable reduction in computation time by a factor of approximately 10,000. V2C-Flow also exhibited robust generalization across different datasets and showed improved consistency in reconstructed points compared to registration-based methods.

The experimental results highlighted the efficacy of the curvature-weighted Chamfer loss in enhancing accuracy in highly curved brain regions. Additionally, the proposed approach yielded superior accuracy in regions affected by anatomical anomalies, such as white matter lesions, demonstrating its potential for clinical use.

Implications and Future Directions

The introduction of neural deformation fields in the form of V2C-Flow presents significant implications for neuroimaging research. The ability to accurately and efficiently reconstruct cortical surfaces with preserved point correspondences provides a robust tool for analyzing brain morphology. This has potential applications in large-scale studies of brain structure, neurodegenerative diseases, and across various demographic factors.

Moreover, the method's integration with existing neuroimaging pipelines, such as those using FreeSurfer, can facilitate smooth adoption and expand its utility in clinical and research settings.

Future research could explore further enhancements, including the incorporation of more diverse and pathological datasets to examine the method's performance across a wider range of conditions. Additionally, the application of V2C-Flow to longitudinal studies assessing cortical changes over time presents a promising development, particularly for studying progressive neurological disorders.

In conclusion, the V2C-Flow method represents a substantive advance in cortical surface reconstruction from MRI, offering both theoretical insights and practical benefits in the field of medical imaging. Its efficiency, accuracy, and capability to maintain anatomical consistency positions it as a valuable tool in neuroimaging research.