Geometric deep learning for diffusion MRI signal reconstruction with continuous samplings (DISCUS).

Area of Science:

• Neuroimaging
• Biomedical Engineering
• Machine Learning

Background:

• Diffusion-weighted magnetic resonance imaging (dMRI) provides in-vivo analysis of neuroanatomical microstructure, crucial for clinical and population studies.
• Current dMRI methods face limitations due to long acquisition times and heterogeneity in acquisition schemes, hindering data combination and detailed analysis.
• Existing learning-based methods require specific acquisition schemes, limiting their applicability to arbitrary diffusion encodings.

Purpose of the Study:

• To develop a novel geometric deep learning method for continuous dMRI signal reconstruction.
• To enable dMRI signal prediction for arbitrary diffusion sampling schemes in both input and output.
• To address the challenges of long acquisition times and heterogeneous dMRI acquisition schemes.

Main Methods:

• Developed the first geometric deep learning method for continuous dMRI signal reconstruction.
• The method accepts and predicts dMRI signals for arbitrary diffusion encodings.
• Combined the accuracy of learning-based methods with the flexibility of model-based methods (e.g., spherical harmonics, SHORE).

Main Results:

• The proposed method outperforms traditional model-based approaches in dMRI signal reconstruction.
• It achieves performance comparable to discrete learning-based methods across various dMRI datasets (single-shell, multi-shell, grid-based).
• Reconstruction leads to higher-quality estimates of microstructure models, enabling robust analysis from short dMRI acquisitions.

Conclusions:

• The novel deep learning method offers a flexible and accurate solution for dMRI signal reconstruction.
• It overcomes limitations of long acquisition times and heterogeneous schemes, facilitating data integration.
• Enables high-quality neuroanatomical microstructure analysis even with limited dMRI data acquisition.