Magnetic Resonance in Medicine 2007 Jun;57(6):1086-98.
This early work demonstrated how iterative reconstruction with sparsity-enforcing total-variation (TV) regularization (today well-known as "compressed sensing") can be employed to obtain images with reasonable quality from significantly undersampled radial MRI data. The approach additionally includes a CG SENSE-type parallel-imaging mechanism with inherent self-calibration of the coil profiles to achieve higher acceleration rates.
Abstract
The reconstruction of artifact-free images from radially encoded MRI acquisitions
poses a difficult task for undersampled data sets, that is for a much lower
number of spokes in k-space than data samples per spoke. Here, we developed an
iterative reconstruction method for undersampled radial MRI which (i) is based on
a nonlinear optimization, (ii) allows for the incorporation of prior knowledge
with use of penalty functions, and (iii) deals with data from multiple coils. The
procedure arises as a two-step mechanism which first estimates the coil profiles
and then renders a final image that complies with the actual observations. Prior
knowledge is introduced by penalizing edges in coil profiles and by a total
variation constraint for the final image. The latter condition leads to an
effective suppression of undersampling (streaking) artifacts and further adds a
certain degree of denoising. Apart from simulations, experimental results for a
radial spin-echo MRI sequence are presented for phantoms and human brain in vivo
at 2.9 T using 24, 48, and 96 spokes with 256 data samples. In comparison to
conventional reconstructions (regridding) the proposed method yielded visually
improved image quality in all cases.