使用结构化照明和自我监督的消噪的高速度体内记录
Forest Speed1, Catherine A Saladrigas2, Alec Teel3
1Department of Bioengineering, University of Colorado Anschutz, Aurora, CO 80045, USA.
概括
我们开发了一种新的成像方法,即伪HiLo (pHiLo),以减少神经活动记录中的噪声. 这种技术与深度学习相结合,显著提高了研究大脑功能的信号清晰度.
科学领域:
- 神经科学是一个神经科学.
- 生物物理学的生物物理.
- 光学成像技术的成像
背景情况:
- 神经活动的高速广场光成像对于理解大脑功能至关重要.
- 来自背景和噪声的信号波动限制了当前的成像技术.
研究的目的:
- 开发和评估新的计算图像重建方法,以减少高速in vivo神经成像中的噪声.
- 改进超快基因编码指标的信号噪声比和数据质量.
主要方法:
- 实施一种新的伪HiLo (pHiLo) 重建方法,将伪宽场 (pWF) 和结构化照明 (SI) 数据结合起来.
- 比较pHiLo与间接光学切割结构化照明显微镜 (OS-SIM) 和pWF重建.
- 集成基于深度学习的噪音抑制方法 (DeepCAD-RT) 进行实时处理.
主要成果:
- 与500Hz的pWF相比,denoised pHiLo重建实现了峰值与噪声比率 (PNR) 的75%增加.
- 通过OS-SIM重建,DeepCAD-RT进一步提高了PNR的59%.
- 无论是pHiLo还是OS-SIM的重建,都将背景信号相关性降低了约65%.
结论:
- 开发的pHiLo重建方法有效地减少了高速神经成像中的背景和镜头噪声.
- 将pHiLo或OS-SIM与DeepCAD-RT相结合,可以显著提高图像质量和数据可靠性.
- 这些进步使神经活动的可视化更加清晰,并促进神经元动态的研究.
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