DDCSR: A Novel End-to-End Deep Learning Framework for Cortical Surface Reconstruction from Diffusion MRI.

Area of Science:

• Neuroimaging
• Medical Image Analysis
• Computational Neuroscience

Background:

• Diffusion MRI (dMRI) is vital for studying brain white matter connectivity.
• Cortical surface reconstruction (CSR) is essential for dMRI analyses like fiber tractography.
• Current CSR methods use T1-weighted MRI, facing challenges with dMRI's low resolution and distortions.

Purpose of the Study:

• To develop a novel framework for direct cortical surface reconstruction from dMRI data.
• To overcome limitations of traditional CSR methods relying on inter-modality registration.
• To enhance the accuracy and efficiency of white matter analysis using dMRI.

Main Methods:

• Proposed DDCSR, an end-to-end deep learning framework for direct CSR from dMRI.
• Implemented an implicit learning module for intermediate surface representation.
• Utilized an explicit learning module for 3D mesh surface prediction.

Main Results:

• DDCSR significantly increased accuracy and efficiency compared to baseline and advanced CSR methods.
• Demonstrated high generalization ability across different dMRI data sources and populations.
• Successfully enabled CSR directly from dMRI, bypassing registration challenges.

Conclusions:

• DDCSR offers a robust and efficient solution for cortical surface reconstruction directly from dMRI.
• The framework shows promise for advancing dMRI-based brain connectivity studies.
• DDCSR's generalization capability supports its broad applicability in neuroimaging research.