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<p>High-resolution tissue imaging is often compromised by sample-induced optical aberrations that degrade resolution and contrast. Although wavefront sensor-based adaptive optics (AO) can measure these aberrations, such hardware solutions are typically complex, expensive to implement and slow when serially mapping spatially varying aberrations across large fields of view. Here we introduce <span>AOViFT</span> (adaptive optical vision Fourier transformer)—a machine learning-based aberration sensing framework built around a three-dimensional multistage vision transformer that operates on Fourier domain embeddings. <span>AOViFT</span> infers aberrations and restores diffraction-limited performance in puncta-labeled specimens with substantially reduced computational cost, training time and memory footprint compared to conventional architectures or real-space networks. We validated <span>AOViFT</span> on live gene-edited zebrafish embryos, demonstrating its ability to correct spatially varying aberrations using either a deformable mirror or postacquisition deconvolution. By eliminating the need for the guide star and wavefront sensing hardware and simplifying the experimental workflow, <span>AOViFT</span> lowers technical barriers for high-resolution volumetric microscopy across diverse biological samples.</p>

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Fourier-based three-dimensional multistage transformer for aberration correction in multicellular specimens

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月1日

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  • Thayer Alshaabi,
  • Daniel E. Milkie,
  • Gaoxiang Liu,
  • Cyna Shirazinejad,
  • Jason L. Hong,
  • Kemal Achour,
  • Frederik Görlitz,
  • Ana Milunovic-Jevtic,
  • Cat Simmons,
  • Ibrahim S. Abuzahriyeh,
  • Erin Hong,
  • Samara Erin Williams,
  • Nathanael Harrison,
  • Evan Huang,
  • Eun Seok Bae,
  • Alison N. Killilea,
  • Ian A. Swinburne,
  • David G. Drubin,
  • Srigokul Upadhyayula,
  • Eric Betzig

摘要

High-resolution tissue imaging is often compromised by sample-induced optical aberrations that degrade resolution and contrast. Although wavefront sensor-based adaptive optics (AO) can measure these aberrations, such hardware solutions are typically complex, expensive to implement and slow when serially mapping spatially varying aberrations across large fields of view. Here we introduce AOViFT (adaptive optical vision Fourier transformer)—a machine learning-based aberration sensing framework built around a three-dimensional multistage vision transformer that operates on Fourier domain embeddings. AOViFT infers aberrations and restores diffraction-limited performance in puncta-labeled specimens with substantially reduced computational cost, training time and memory footprint compared to conventional architectures or real-space networks. We validated AOViFT on live gene-edited zebrafish embryos, demonstrating its ability to correct spatially varying aberrations using either a deformable mirror or postacquisition deconvolution. By eliminating the need for the guide star and wavefront sensing hardware and simplifying the experimental workflow, AOViFT lowers technical barriers for high-resolution volumetric microscopy across diverse biological samples.