Hardware acceleration for ultra-fast Neural Network training on FPGA for MRF map reconstruction
This could enable real-time brain analysis on mobile devices, benefiting clinical decision-making and telemedicine, though it appears incremental as it applies an existing method (hardware acceleration) to a specific domain.
The paper tackles the problem of slow neural network training for Magnetic Resonance Fingerprinting (MRF) map reconstruction by proposing an FPGA-based hardware acceleration method, achieving training in 200 seconds compared to up to 250 times slower CPU-based training.
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.