将循环神经网络连接到SPAD TCSPC系统,用于实时光寿命成像
Yang Lin1, Paul Mos1, Andrei Ardelean1
1Advanced Quantum Architecture Laboratory, École polytechnique fédérale de Lausanne, Neuchâtel, 2002, Switzerland.
Scientific reports
|February 8, 2024
概括
边缘人工智能通过循环神经网络 (RNN) 增强光终身成像 (FLI),使医学诊断更快,更准确. 这种强大的方法直接处理数据,改善实时成像能力.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
- 光子学 是一个光子学.
背景情况:
- 光终身成像 (FLI) 是生物学和医学中一个有价值的诊断工具.
- 目前的FLI系统在速度,准确性和稳定性方面面临限制.
- 边缘人工智能 (Edge AI) 为这些挑战提供了潜在的解决方案.
研究的目的:
- 使用Edge AI开发一种强大而快速的FLI方法.
- 为了实现实时的FLI采集,而不会降低准确度.
- 为了减少FLI系统中的数据传输量和硬件资源利用量.
主要方法:
- 将循环神经网络 (RNN),专门封闭的循环单元 (GRU) 和长短期记忆 (LSTM) 连接到SPAD TCSPC系统.
- 训练RNN从原始时间直接估计光寿命,绕过直方图结构.
- 在FPGA上实现量子化GRU核心,用于实时数据处理.
主要成果:
- 基于RNN的FLI表现出与传统方法 (CMM,LS装配) 相比的准确性.
- 在存在背景噪音的情况下,RNN显著优于传统方法.
- 导出的克拉默-拉奥下界表示RNN寿命估计的近最佳精度.
- 一个实时FLI显微镜达到每秒10.
结论:
- 拟议的Edge AI驱动的FLI系统为生物医学应用提供了强大,快速和准确的解决方案.
- 这种方法显著提高了用于诊断和研究的实时成像能力.
- 该系统非常适合用于生物成像,医学诊断和光辅助手术.
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