Improving DCE-MRI through unfolded low-rank + sparse optimisation
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866929560216403968 |
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| author | Mokrý, Ondřej Vitouš, Jiří Rajmic, Pavel Jiřík, Radovan |
| author_facet | Mokrý, Ondřej Vitouš, Jiří Rajmic, Pavel Jiřík, Radovan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_07222 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Improving DCE-MRI through unfolded low-rank + sparse optimisation Mokrý, Ondřej Vitouš, Jiří Rajmic, Pavel Jiřík, Radovan Signal Processing 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. |
| title | Improving DCE-MRI through unfolded low-rank + sparse optimisation |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2312.07222 |