Keywords: Sparse & Low-Rank Models, Quantitative Imaging, Image reconstructionQuantitative MRI offers unique opportunities for compressed sensing reconstructions because the signal evolution is known. Here we compare a principal component subspace reconstruction to a standard total-variation regularized compressed sensing approach with a 3-D radial variable flip angle simulation data. The simulation data allows us to measure only the effect of the chosen reconstruction. The subspace approach consistently yields similar or better image quality and does not seem to require contrast dimension regularization to achieve it.
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