Transformer-based Parameter Fitting of Models derived from Bloch-McConnell Equations for CEST MRI Analysis
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866915779796008960 |
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| author | Duhme, Christof Lippe, Chris Hoerr, Verena Jiang, Xiaoyi |
| author_facet | Duhme, Christof Lippe, Chris Hoerr, Verena Jiang, Xiaoyi |
| contents | Chemical exchange saturation transfer (CEST) MRI is a non-invasive imaging modality for detecting metabolites. It offers higher resolution and sensitivity compared to conventional magnetic resonance spectroscopy (MRS). However, quantification of CEST data is challenging because the measured signal results from a complex interplay of many physiological variables. Here, we introduce a transformer-based neural network to fit parameters such as metabolite concentrations, exchange and relaxation rates of a physical model derived from Bloch-McConnell equations to in-vitro CEST spectra. We show that our self-supervised trained neural network clearly outperforms the solution of classical gradient-based solver. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_06574 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Transformer-based Parameter Fitting of Models derived from Bloch-McConnell Equations for CEST MRI Analysis Duhme, Christof Lippe, Chris Hoerr, Verena Jiang, Xiaoyi Machine Learning Artificial Intelligence Chemical exchange saturation transfer (CEST) MRI is a non-invasive imaging modality for detecting metabolites. It offers higher resolution and sensitivity compared to conventional magnetic resonance spectroscopy (MRS). However, quantification of CEST data is challenging because the measured signal results from a complex interplay of many physiological variables. Here, we introduce a transformer-based neural network to fit parameters such as metabolite concentrations, exchange and relaxation rates of a physical model derived from Bloch-McConnell equations to in-vitro CEST spectra. We show that our self-supervised trained neural network clearly outperforms the solution of classical gradient-based solver. |
| title | Transformer-based Parameter Fitting of Models derived from Bloch-McConnell Equations for CEST MRI Analysis |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2602.06574 |