用FPGA加速神经网络训练，实现脑部MRF影像实时重建
Hardware acceleration for ultra-fast Neural Network training on FPGA for MRF map reconstruction
- 基于FPGA构建神经网络，实现快速训练
- 训练仅需200秒，比传统CPU快250倍
- 适合移动设备部署，推动临床即时诊断
磁共振指纹成像（MRF）是一种快速定量磁共振成像技术，可单次采集获得多参数图谱。神经网络（NN）虽能加速重建，但训练资源消耗大。本文提出一种基于FPGA的神经网络架构，实现从MRF数据中实时重建脑部参数。训练时间约为200秒，显著优于传统基于CPU的训练方式，后者可能慢达250倍。该方法有望在移动设备上实现实时脑分析，推动临床决策与远程医疗变革。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。