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<p>Intravital fluorescence microscopy is hampered by optical aberrations arising from heterogeneous distributions of the refractive index. Adaptive optics (AO) methods are either costly and slow, requiring additional hardware, or inaccurate due to lack of wavefront information in multiple angular directions. Here we present a latent-space-enhanced digital AO (LEAO) method that uses wave-optics priors embedded in high-dimensional spatial–angular data and semantically disentangles their representations in the latent space. LEAO achieves more than sixfold higher aberration estimation accuracy than the existing approach (coordinate-based neural representations for computational AO). It also exhibits strong robustness across different system configurations and imaging conditions, achieving almost an order of magnitude higher accuracy than iterative digital AO under extreme conditions such as a low signal-to-noise ratio of 3.4 dB. We experimentally demonstrate that LEAO improves diverse biological observations in vivo, such as large-scale tracking of T cells across an entire lymph node, multiregional neural recording in mouse cortex and long-term monitoring of neutrophil activation, extravasation and clearing through mouse intact skull after traumatic brain injury.</p>

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High-fidelity intravital imaging of biological dynamics with latent-space-enhanced digital adaptive optics

  • 期刊论文

  • 发表日期：

    2026年4月23日

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  • Yunmin Zeng,
  • Qi Zhang,
  • Yihong Xiao,
  • Shidong Wu,
  • Seonghoon Kim,
  • Yunhao Zhang,
  • Mingrui Wang,
  • Yuanlong Zhang,
  • Xinyang Li,
  • Zhi Lu,
  • Jiamin Wu,
  • Qionghai Dai

摘要

Intravital fluorescence microscopy is hampered by optical aberrations arising from heterogeneous distributions of the refractive index. Adaptive optics (AO) methods are either costly and slow, requiring additional hardware, or inaccurate due to lack of wavefront information in multiple angular directions. Here we present a latent-space-enhanced digital AO (LEAO) method that uses wave-optics priors embedded in high-dimensional spatial–angular data and semantically disentangles their representations in the latent space. LEAO achieves more than sixfold higher aberration estimation accuracy than the existing approach (coordinate-based neural representations for computational AO). It also exhibits strong robustness across different system configurations and imaging conditions, achieving almost an order of magnitude higher accuracy than iterative digital AO under extreme conditions such as a low signal-to-noise ratio of 3.4 dB. We experimentally demonstrate that LEAO improves diverse biological observations in vivo, such as large-scale tracking of T cells across an entire lymph node, multiregional neural recording in mouse cortex and long-term monitoring of neutrophil activation, extravasation and clearing through mouse intact skull after traumatic brain injury.