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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.22156 |
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| _version_ | 1866915361655357440 |
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| author | Ricchi, Mattia Alfonsi, Fabrizio Marella, Camilla Barbieri, Marco Retico, Alessandra Brizi, Leonardo Gabrielli, Alessandro Testa, Claudia |
| author_facet | Ricchi, Mattia Alfonsi, Fabrizio Marella, Camilla Barbieri, Marco Retico, Alessandra Brizi, Leonardo Gabrielli, Alessandro Testa, Claudia |
| contents | Magnetic Resonance Fingerprinting (MRF) is a fast quantitative MR Imaging technique that provides multi-parametric maps with a single acquisition. Neural Networks (NNs) accelerate reconstruction but require significant resources for training. We propose an FPGA-based NN for real-time brain parameter reconstruction from MRF data. Training the NN takes an estimated 200 seconds, significantly faster than standard CPU-based training, which can be up to 250 times slower. This method could enable real-time brain analysis on mobile devices, revolutionizing clinical decision-making and telemedicine. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_22156 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hardware acceleration for ultra-fast Neural Network training on FPGA for MRF map reconstruction Ricchi, Mattia Alfonsi, Fabrizio Marella, Camilla Barbieri, Marco Retico, Alessandra Brizi, Leonardo Gabrielli, Alessandro Testa, Claudia Hardware Architecture Computer Vision and Pattern Recognition Instrumentation and Detectors Magnetic Resonance Fingerprinting (MRF) is a fast quantitative MR Imaging technique that provides multi-parametric maps with a single acquisition. Neural Networks (NNs) accelerate reconstruction but require significant resources for training. We propose an FPGA-based NN for real-time brain parameter reconstruction from MRF data. Training the NN takes an estimated 200 seconds, significantly faster than standard CPU-based training, which can be up to 250 times slower. This method could enable real-time brain analysis on mobile devices, revolutionizing clinical decision-making and telemedicine. |
| title | Hardware acceleration for ultra-fast Neural Network training on FPGA for MRF map reconstruction |
| topic | Hardware Architecture Computer Vision and Pattern Recognition Instrumentation and Detectors |
| url | https://arxiv.org/abs/2506.22156 |