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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

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Fast multi-compartment Microstructure Fingerprinting in brain white matter.

Quentin Dessain^1,2, Clément Fuchs¹, Benoît Macq¹

¹Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), UCLouvain, Louvain-la-Neuve, Belgium.

Frontiers in Neuroscience

|August 5, 2024

Summary

We developed two deep neural network methods to speed up the analysis of white matter microstructure. These techniques accelerate Microstructure Fingerprinting in diffusion MRI, enabling faster quantitative feature estimation in complex brain structures.

Keywords:

crossing bundles deep learning diffusion MRI fingerprinting microstructure non-negative linear least-squares

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Area of Science:

Neuroimaging
Biomedical Engineering
Computational Neuroscience

Background:

Diffusion MRI is crucial for mapping white matter microstructure.
Estimating microstructural features, especially in crossing fascicles, is computationally intensive.
Microstructure Fingerprinting (MF) extends Magnetic Resonance Fingerprinting (MRF) for diffusion MRI but requires significant computation.

Purpose of the Study:

To accelerate the estimation of microstructural features in white matter, particularly for complex crossing fascicles.
To improve the efficiency of the multi-dictionary matching problem central to Microstructure Fingerprinting.
To enable faster quantitative analysis of brain white matter in vivo.

Main Methods:

Proposed two deep neural network (DNN) based acceleration methods for Microstructure Fingerprinting.
Method 1: Utilized efficient sparse optimization and a feed-forward DNN to address combinatorial complexity.
Method 2: Employed a feed-forward DNN using spherical harmonics representation of diffusion-weighted MRI (DW-MRI) signals as input.

Main Results:

Both DNN methods significantly accelerated the estimation process.
Method 1 offered high interpretability.
Method 2 achieved a greater speedup factor, with several orders of magnitude improvement.
Accurate results were validated on in vivo brain data.

Conclusions:

The developed DNN-based methods offer substantial speedup for Microstructure Fingerprinting.
These techniques hold promise for rapid quantitative estimation of white matter microstructural features.
The findings are particularly relevant for analyzing complex white matter configurations in diffusion MRI studies.