  A method for perfusion imaging with DCE-MRI is developed based on two popular
paradigms: the low-rank + sparse model for optimisation-based reconstruction,
and the deep unfolding. A learnable algorithm derived from a proximal algorithm
is designed with emphasis on simplicity and interpretability. The resulting
deep network is trained and evaluated using a simulated measurement of a rat
with a brain tumor, showing large performance gain over the classical low-rank
+ sparse baseline. Moreover, quantitative perfusion analysis is performed based
on the reconstructed sequence, proving that even training based on a simple
pixel-wise error can lead to significant improvement of the quality of the
perfusion maps.
