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qModeL: A plug-and-play model-based reconstruction for highly accelerated multi-shot diffusion MRI using learned

Merry Mani¹, Vincent A Magnotta¹, Mathews Jacob²

¹Department of Radiology, University of Iowa, Iowa City, IA, USA.

Magnetic Resonance in Medicine

|March 24, 2021

Summary

A new method called qModeL reconstructs highly accelerated multi-shot diffusion weighted (msDW) scans. This technique enables accurate diffusion MRI microstructure studies with significantly reduced scan times.

Keywords:

NODDI autoencoder neural network deep learning k-q acceleration machine learning multi-shell multi-shot diffusion

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

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Area of Science:

Medical Imaging
Neuroimaging
Diffusion MRI

Background:

Multi-shot Echo Planar Imaging (EPI) offers higher spatial resolution in diffusion MRI but requires long scan times.
Accelerated multi-shot diffusion weighted (msDW) scans are crucial for advanced microstructure studies needing extensive q-space coverage.
Existing joint k-q undersampling methods with compressed sensing struggle with multi-shell data reconstruction due to limitations of sparsity priors.

Purpose of the Study:

To introduce a novel joint reconstruction method, qModeL, for highly undersampled multi-shot diffusion weighted (msDW) scans.
To enable accurate reconstruction of diffusion MRI data from accelerated multi-shot acquisitions, facilitating advanced microstructure analysis.

Main Methods:

Proposed qModeL reconstruction integrates model-based iterative techniques with machine learning, extending plug-and-play algorithms.
Deep learning is used to pre-learn the signal manifold in the diffusion measurement space, providing voxel-wise q-dimension regularization.
The framework incorporates plug-and-play total variation denoising for spatial dimension regularization and is validated on single-shell and multi-shell msDW data.

Main Results:

qModeL successfully reconstructs diffusion weighted images (DWIs) from 8-fold accelerated msDW acquisitions with less than 5% error for both single-shell and multi-shell data.
Advanced microstructural analyses performed using the qModeL-reconstructed data demonstrate reasonable accuracy.

Conclusions:

qModeL facilitates joint recovery of highly accelerated multi-shot diffusion MRI data using learned, biophysically-driven priors.
This approach supports the utilization of accelerated multi-shot imaging for multi-shell sampling and in-depth microstructure studies.