AI 中文总结
本研究开发了DeepFOCUS方法，基于强度的双光子散射校正技术，实现小鼠脑内1毫米以上深度的在体双光子成像，可分辨神经元与血管，拓展了双光子成像的应用范围。
AI 中文摘要
具有亚细胞分辨率的大脑深部光学成像是神经科学研究的重要需求，但非侵入式成像穿透皮层、穿过散射性白质到达海马体的区域，通常需要使用更长激发波长的三光子显微镜。本研究提出了深度学习增强的频域强度耦合散射校正方法（DeepFOCUS），这是一种基于强度的双光子技术，利用深度学习计算强度调制掩模，在图像采集过程中实时调制激发光。与基于深度学习的图像恢复不同，该方法通过计算强度调制掩模直接优化成像过程，掩模会根据采集到的荧光信号进行实验验证，避免产生幻觉伪影。使用1035 nm激发光，研究人员在完整小鼠脑中实现了深度超过1毫米的在体双光子成像，成功分辨出YFP标记的神经元和FITC标记的血管，覆盖了整个皮层和白质，直至海马体的CA1区域。DeepFOCUS将双光子成像的深度扩展到此前主要依靠三光子显微镜才能达到的范围，通过升级现有的双光子系统，有望推动海马体成像的更广泛应用。
英文摘要
Optical imaging of the deep brain with subcellular resolution is essential for neuroscience, but noninvasive imaging beyond the cortex, through scattering white matter and into the hippocampus, has generally required three-photon microscopy at longer excitation wavelengths. Here, we introduce deep-learning-enhanced Fourier-domain intensity coupling for scattering correction (DeepFOCUS), an intensity-based two-photon approach that uses deep learning to compute intensity-modulation masks for real-time modulation of excitation light during image acquisition. Unlike deep-learning-based image restoration, this method directly improves image formation by computing intensity-modulation masks that shape the excitation light in real time, with each mask experimentally validated by the acquired fluorescence signal to avoid hallucination artifacts. Using 1035 nm excitation, we achieved in vivo two-photon imaging beyond 1 mm depth in the intact mouse brain, resolving YFP-labeled neurons and FITC-labeled blood vessels through the entire cortex and white matter down to the CA1 region of the hippocampus. DeepFOCUS extends two-photon imaging to depths previously accessible mainly with three-photon microscopy and could enable broader adoption of hippocampal imaging by upgrading existing two-photon systems.
