Temporal and spectral unmixing of photoacoustic signals by deep learning.
Temporal and spectral unmixing of photoacoustic signals by deep learning.
复制标题
通过深度学习实现光声信号的时间和光谱解混。
DOI：
10.1364/ol.426678
复制
发表时间：
2021-06-01
期刊：
影响因子：
3.6
通讯作者：
Hu, Song
中科院分区：
文献类型：
--
作者：
Zhou, Yifeng;Zhong, Fenghe;Hu, Song
提高多参数光声显微镜（PAM）成像速度是发挥其在生物医学领域作用的关键。然而，为了避免时间重叠，a线速率被生物组织中的声速限制在几兆赫兹。此外，为了实现血红蛋白（sO2）氧饱和度的高速PAM，光纤中的受激拉曼散射效应已被广泛应用于从商用532 nm激光器双波长激发产生558 nm。然而，用于有效波长转换的光纤长度通常很短，对应于导致在两个波长处获得的a线明显重叠的小时间延迟。增加光纤长度延长了时间间隔，但将脉冲能量限制在558nm。在这篇论文中，我们报告了一种基于条件生成对抗网络的方法，该方法可以实现光声a线信号的时间解混，时间间隔短至~38 ns，打破了a线速率的物理限制。此外，这种深度学习方法允许使用多光谱激光脉冲进行PAM激发，解决了单色激光脉冲能量不足的问题。该技术为超高速多参数PAM的实现奠定了基础。
Improving the imaging speed of multi-parametric photoacoustic microscopy (PAM) is essential to leveraging its impact in biomedicine. However, to avoid temporal overlap, the A-line rate is limited by the acoustic speed in biological tissues to a few MHz. Moreover, to achieve high-speed PAM of the oxygen saturation of hemoglobin (sO2), the stimulated Raman scattering effect in optical fibers has been widely used to generate 558 nm from a commercial 532 nm laser for dual-wavelength excitation. However, the fiber length for effective wavelength conversion is typically short, corresponding to a small time delay that leads to a significant overlap of the A-lines acquired at the two wavelengths. Increasing the fiber length extends the time interval, but limits the pulse energy at 558 nm. In this Letter, we report a conditional generative adversarial network-based approach, which enables temporal unmixing of photoacoustic A-line signals with an interval as short as ~38 ns, breaking the physical limit on the A-line rate. Moreover, this deep learning approach allows the use of multi-spectral laser pulses for PAM excitation, addressing the insufficient energy of monochromatic laser pulses. This technique lays the foundation for ultra-high-speed multi-parametric PAM.