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qModeL: A plug‐and‐play model‐based reconstruction for highly accelerated multi‐shot diffusion MRI using learned priors
Journal article   Open access   Peer reviewed

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

Merry Mani, Vincent A Magnotta and Mathews Jacob
Magnetic resonance in medicine, Vol.86(2), pp.835-851
08/2021
DOI: 10.1002/mrm.28756
PMCID: PMC8076086
PMID: 33759240

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Abstract

Purpose To introduce a joint reconstruction method for highly undersampled multi‐shot diffusion weighted (msDW) scans. Methods Multi‐shot EPI methods enable higher spatial resolution for diffusion MRI, but at the expense of long scan‐time. Highly accelerated msDW scans are needed to enable their utilization in advanced microstructure studies, which require high q‐space coverage. Previously, joint k‐q undersampling methods coupled with compressed sensing were shown to enable very high acceleration factors. However, the reconstruction of this data using sparsity priors is challenging and is not suited for multi‐shell data. We propose a new reconstruction that recovers images from the combined k‐q data jointly. The proposed qModeL reconstruction brings together the advantages of model‐based iterative reconstruction and machine learning, extending the idea of plug‐and‐play algorithms. Specifically, qModeL works by prelearning the signal manifold corresponding to the diffusion measurement space using deep learning. The prelearned manifold prior is incorporated into a model‐based reconstruction to provide a voxel‐wise regularization along the q‐dimension during the joint recovery. Notably, the learning does not require in vivo training data and is derived exclusively from biophysical modeling. Additionally, a plug‐and‐play total variation denoising provides regularization along the spatial dimension. The proposed framework is tested on k‐q undersampled single‐shell and multi‐shell msDW acquisition at various acceleration factors. Results The qModeL joint reconstruction is shown to recover DWIs from 8‐fold accelerated msDW acquisitions with error less than 5% for both single‐shell and multi‐shell data. Advanced microstructural analysis performed using the undersampled reconstruction also report reasonable accuracy. Conclusion qModeL enables the joint recovery of highly accelerated multi‐shot dMRI utilizing learning‐based priors. The bio‐physically driven approach enables the use of accelerated multi‐shot imaging for multi‐shell sampling and advanced microstructure studies.
 deep learning multi‐shot diffusion multi‐shell autoencoder neural network NODDI k‐q acceleration machine learning

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