2 research outputs found
Deep MR Fingerprinting with total-variation and low-rank subspace priors
Deep learning (DL) has recently emerged to address the heavy storage and
computation requirements of the baseline dictionary-matching (DM) for Magnetic
Resonance Fingerprinting (MRF) reconstruction. Fed with non-iterated
back-projected images, the network is unable to fully resolve
spatially-correlated corruptions caused from the undersampling artefacts. We
propose an accelerated iterative reconstruction to minimize these artefacts
before feeding into the network. This is done through a convex regularization
that jointly promotes spatio-temporal regularities of the MRF time-series.
Except for training, the rest of the parameter estimation pipeline is
dictionary-free. We validate the proposed approach on synthetic and in-vivo
datasets
Compressive MRI quantification using convex spatiotemporal priors and deep auto-encoders
We propose a dictionary-matching-free pipeline for multi-parametric
quantitative MRI image computing. Our approach has two stages based on
compressed sensing reconstruction and deep learned quantitative inference. The
reconstruction phase is convex and incorporates efficient spatiotemporal
regularisations within an accelerated iterative shrinkage algorithm. This
minimises the under-sampling (aliasing) artefacts from aggressively short scan
times. The learned quantitative inference phase is purely trained on physical
simulations (Bloch equations) that are flexible for producing rich training
samples. We propose a deep and compact auto-encoder network with residual
blocks in order to embed Bloch manifold projections through multiscale
piecewise affine approximations, and to replace the nonscalable
dictionary-matching baseline. Tested on a number of datasets we demonstrate
effectiveness of the proposed scheme for recovering accurate and consistent
quantitative information from novel and aggressively subsampled 2D/3D
quantitative MRI acquisition protocols