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arXiv · 2312.07222

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

Ondřej Mokrý·Jiří Vitouš·Pavel Rajmic·Radovan Jiřík
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Abstract

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.

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Ondřej Mokrý, Jiří Vitouš, Pavel Rajmic, Radovan Jiřík. 2023-12-12. Improving DCE-MRI through unfolded low-rank + sparse optimisation. https://arxiv.org/abs/2312.07222

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