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

Improving DCE-MRI through unfolded low-rank + sparse optimisation