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arXiv · 2506.22156

Hardware acceleration for ultra-fast Neural Network training on FPGA for MRF map reconstruction

Mattia Ricchi·Fabrizio Alfonsi·Camilla Marella·Marco Barbieri·Alessandra Retico·Leonardo Brizi·Alessandro Gabrielli·Claudia Testa
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Abstract

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.

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Mattia Ricchi, Fabrizio Alfonsi, Camilla Marella, Marco Barbieri, Alessandra Retico, Leonardo Brizi, Alessandro Gabrielli, Claudia Testa. 2025-06-27. Hardware acceleration for ultra-fast Neural Network training on FPGA for MRF map reconstruction. https://arxiv.org/abs/2506.22156

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