1 research outputs found
Model-Based Deep Learning for Reconstruction of Joint k-q Under-sampled High Resolution Diffusion MRI
We propose a model-based deep learning architecture for the reconstruction of
highly accelerated diffusion magnetic resonance imaging (MRI) that enables high
resolution imaging. The proposed reconstruction jointly recovers all the
diffusion weighted images in a single step from a joint k-q under-sampled
acquisition in a parallel MRI setting. We propose the novel use of a
pre-trained denoiser as a regularizer in a model-based reconstruction for the
recovery of highly under-sampled data. Specifically, we designed the denoiser
based on a general diffusion MRI tissue microstructure model for
multi-compartmental modeling. By using a wide range of biologically plausible
parameter values for the multi-compartmental microstructure model, we simulated
diffusion signal that spans the entire microstructure parameter space. A neural
network was trained in an unsupervised manner using an autoencoder to learn the
diffusion MRI signal subspace. We employed the autoencoder in a model-based
reconstruction and show that the autoencoder provides a strong denoising prior
to recover the q-space signal. We show reconstruction results on a simulated
brain dataset that shows high acceleration capabilities of the proposed method.Comment: 4 pages, 4 figures, and 1 tabl