Physics-Informed autoencoder for DSC-MRI Perfusion post-processing: application to glioma grading
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917018286948352 |
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| author | Fayolle, Pierre Bône, Alexandre Debs, Noëlie Naudin, Mathieu Bourdon, Pascal Guillevin, Remy Helbert, David |
| author_facet | Fayolle, Pierre Bône, Alexandre Debs, Noëlie Naudin, Mathieu Bourdon, Pascal Guillevin, Remy Helbert, David |
| contents | DSC-MRI perfusion is a medical imaging technique for diagnosing and prognosing brain tumors and strokes. Its analysis relies on mathematical deconvolution, but noise or motion artifacts in a clinical environment can disrupt this process, leading to incorrect estimate of perfusion parameters. Although deep learning approaches have shown promising results, their calibration typically rely on third-party deconvolution algorithms to generate reference outputs and are bound to reproduce their limitations.
To adress this problem, we propose a physics-informed autoencoder that leverages an analytical model to decode the perfusion parameters and guide the learning of the encoding network. This autoencoder is trained in a self-supervised fashion without any third-party software and its performance is evaluated on a database with glioma patients. Our method shows reliable results for glioma grading in accordance with other well-known deconvolution algorithms despite a lower computation time. It also achieved competitive performance even in the presence of high noise which is critical in a medical environment. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_13886 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Physics-Informed autoencoder for DSC-MRI Perfusion post-processing: application to glioma grading Fayolle, Pierre Bône, Alexandre Debs, Noëlie Naudin, Mathieu Bourdon, Pascal Guillevin, Remy Helbert, David Quantitative Methods Artificial Intelligence Image and Video Processing Signal Processing DSC-MRI perfusion is a medical imaging technique for diagnosing and prognosing brain tumors and strokes. Its analysis relies on mathematical deconvolution, but noise or motion artifacts in a clinical environment can disrupt this process, leading to incorrect estimate of perfusion parameters. Although deep learning approaches have shown promising results, their calibration typically rely on third-party deconvolution algorithms to generate reference outputs and are bound to reproduce their limitations. To adress this problem, we propose a physics-informed autoencoder that leverages an analytical model to decode the perfusion parameters and guide the learning of the encoding network. This autoencoder is trained in a self-supervised fashion without any third-party software and its performance is evaluated on a database with glioma patients. Our method shows reliable results for glioma grading in accordance with other well-known deconvolution algorithms despite a lower computation time. It also achieved competitive performance even in the presence of high noise which is critical in a medical environment. |
| title | Physics-Informed autoencoder for DSC-MRI Perfusion post-processing: application to glioma grading |
| topic | Quantitative Methods Artificial Intelligence Image and Video Processing Signal Processing |
| url | https://arxiv.org/abs/2510.13886 |