Publication detail

Improving DCE-MRI through Unfolded Low-Rank + Sparse Optimisation

MOKRÝ, O. VITOUŠ, J. RAJMIC, P. JIŘÍK, R.

Original Title

Improving DCE-MRI through Unfolded Low-Rank + Sparse Optimisation

Type

conference paper

Language

English

Original Abstract

A method for perfusion imaging with DCE-MRI is developed based on a combination of two popular paradigms: the low-rank + sparse model for optimisation-based reconstruction, and the deep unfolding. A learnable algorithm derived from a proximal method 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, a quantitative perfusion analysis is performed based on the reconstructed sequence, proving that even training based on a simple pixel-wise error can lead to a significant improvement of the quality of the perfusion maps.

Keywords

DCE-MRI; proximal splitting algorithms; deep unfolding; L+S model; perfusion analysis

Authors

MOKRÝ, O.; VITOUŠ, J.; RAJMIC, P.; JIŘÍK, R.

Released

27. 5. 2024

Publisher

IEEE

Location

Athens, Greece

ISBN

979-8-3503-1333-8

Book

2024 IEEE International Symposium on Biomedical Imaging (ISBI)

Pages count

5

URL

https://ieeexplore.ieee.org/document/10635295/

BibTex

@inproceedings{BUT189442,
  author="Ondřej {Mokrý} and Jiří {Vitouš} and Pavel {Rajmic} and Radovan {Jiřík}",
  title="Improving DCE-MRI through Unfolded Low-Rank + Sparse Optimisation",
  booktitle="2024 IEEE International Symposium on Biomedical Imaging (ISBI)",
  year="2024",
  pages="5",
  publisher="IEEE",
  address="Athens, Greece",
  doi="10.1109/ISBI56570.2024.10635295",
  isbn="979-8-3503-1333-8",
  url="https://ieeexplore.ieee.org/document/10635295/"
}