Magnetic Resonance in Medicine | VOL. 86
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QModeL: A plug-and-play model-based reconstruction for highly accelerated multi-shot diffusion MRI using learned priors.

Publication Date Aug 1, 2021

Abstract

To introduce a joint reconstruction method for highly undersampled multi-shot diffusion weighted (msDW) scans. 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. The qModeL joint...

Concepts

Model-based Reconstruction Highly Accelerated Total Variation Denoising Resolution Acceleration High Acceleration Factors High Acceleration Multi-shot Diffusion Factor S Multi-shell Data

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