  Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is used to
study microvascular structure and tissue perfusion. In DCE-MRI a bolus of
gadolinium based contrast agent is injected into the blood stream and
spatiotemporal changes induced by the contrast agent flow are estimated from a
time series of MRI data. Sufficient time resolution can often only be obtained
by using an imaging protocol which produces undersampled data for each image in
the time series. This has led to the popularity of compressed sensing based
image reconstruction approaches, where all the images in the time series are
reconstructed simultaneously, and temporal coupling between the images is
introduced into the problem by a sparsity promoting regularization functional.
We propose the use of Huber penalty for temporal regularization in DCE-MRI, and
compare it to total variation, total generalized variation and smoothness based
temporal regularization models. We also study the effect of spatial
regularization to the reconstruction and compare the reconstruction accuracy
with different temporal resolutions due to varying undersampling. The
approaches are tested using simulated and experimental radial golden angle
DCE-MRI data from a rat brain specimen. The results indicate that Huber
regularization produces similar reconstruction accuracy with the total
variation based models, but the computation times are significantly faster.
