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.
@article{arxiv.2506.22156,
title = {Hardware acceleration for ultra-fast Neural Network training on FPGA for MRF map reconstruction},
author = {Mattia Ricchi and Fabrizio Alfonsi and Camilla Marella and Marco Barbieri and Alessandra Retico and Leonardo Brizi and Alessandro Gabrielli and Claudia Testa},
journal= {arXiv preprint arXiv:2506.22156},
year = {2025}
}
Comments
8 pages, 2 figures, to be published in conference proceedings of SDPS 2024: 2024 International Conference of the Society for Design and Process Science on Advances and Challenges of Applying AI/GenAI in Design and Process Science