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We propose a model-based deep learning architecture for the reconstruction ofhighly accelerated diffusion magnetic resonance imaging (MRI) that enables highresolution imaging. The proposed reconstruction jointly recovers all thediffusion weighted images in a single step from a joint k-q under-sampledacquisition in a parallel MRI setting. We propose the novel use of apre-trained denoiser as a regularizer in a model-based reconstruction for therecovery of highly under-sampled data. Specifically, we designed the denoiserbased on a general diffusion MRI tissue microstructure model formulti-compartmental modeling. By using a wide range of biologically plausibleparameter values for the multi-compartmental microstructure model, we simulateddiffusion signal that spans the entire microstructure parameter space. A neuralnetwork was trained in an unsupervised manner using an autoencoder to learn thediffusion MRI signal subspace. We employed the autoencoder in a model-basedreconstruction and show that the autoencoder provides a strong denoising priorto recover the q-space signal. We show reconstruction results on a simulatedbrain dataset that shows high acceleration capabilities of the proposed method.

Model-Based Deep Learning for Reconstruction of Joint k-q Under-sampled High Resolution Diffusion MRI