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<p>Tissue-induced optical aberrations fundamentally constrain intravital microscopy in deep or complex biological specimens. While adaptive optics (AO) can compensate for these aberrations, conventional AO methods are limited by either guide-star dependency or slow correction speeds. Here, we develop MeNet-AO, a multi-encoder network-based AO method that enables rapid, guide-star-free aberration correction. By integrating a noise-resilient, structure-independent feature extraction model with a physics-informed multi-encoder architecture, MeNet-AO jointly decodes multiple large-amplitude aberration modes from wavefront-modulated image pairs, achieving an effective balance between prediction accuracy and temporal efficiency. Validated in living organisms, MeNet-AO improves fluorescence imaging in zebrafish brain and eye, enhances neuronal calcium transients and direction selectivity in mouse visual cortex, and enables subcellular-resolution microglial calcium imaging through thinned-skull windows – revealing spatiotemporally heterogeneous signaling patterns previously obscured by skull aberration. The speed and robustness of MeNet-AO in low-signal and scattering conditions establish it as a versatile platform for dynamic subcellular imaging deep within native tissue environments.</p>

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Physics-informed multi-encoder adaptive optics enables rapid aberration correction for intravital microscopy of deep complex tissue

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月20日

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  • Xiangzhang Cheng,
  • Bo Wang,
  • Li Luo,
  • Zhaowei Sun,
  • Sicong He

摘要

Tissue-induced optical aberrations fundamentally constrain intravital microscopy in deep or complex biological specimens. While adaptive optics (AO) can compensate for these aberrations, conventional AO methods are limited by either guide-star dependency or slow correction speeds. Here, we develop MeNet-AO, a multi-encoder network-based AO method that enables rapid, guide-star-free aberration correction. By integrating a noise-resilient, structure-independent feature extraction model with a physics-informed multi-encoder architecture, MeNet-AO jointly decodes multiple large-amplitude aberration modes from wavefront-modulated image pairs, achieving an effective balance between prediction accuracy and temporal efficiency. Validated in living organisms, MeNet-AO improves fluorescence imaging in zebrafish brain and eye, enhances neuronal calcium transients and direction selectivity in mouse visual cortex, and enables subcellular-resolution microglial calcium imaging through thinned-skull windows – revealing spatiotemporally heterogeneous signaling patterns previously obscured by skull aberration. The speed and robustness of MeNet-AO in low-signal and scattering conditions establish it as a versatile platform for dynamic subcellular imaging deep within native tissue environments.