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Towards ultrafast quantitative phase imaging via differentiable microscopy [Invited].

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Biomedical Optics Express
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Summary

Quantitative phase microscopy (QPM) can now achieve higher throughput using a novel optical compression-decompression framework. This method reduces data acquisition bottlenecks, enabling faster label-free imaging for various applications.

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Area of Science:

  • Biomedical Optics
  • Microscopy Techniques
  • Computational Imaging

Background:

  • Quantitative phase microscopy (QPM) is a label-free imaging technique with broad applications in metabolomics and histopathology.
  • Current QPM throughput is limited by image sensor pixel rates, hindering real-time analysis and high-volume studies.
  • Advancements in sensors and deep learning have improved QPM but not overcome the fundamental hardware bottleneck.

Purpose of the Study:

  • To develop a novel framework for compressed image acquisition in QPM to overcome hardware limitations.
  • To introduce a learnable optical compression-decompression system for enhanced QPM throughput.
  • To demonstrate significant throughput improvements in QPM through content-specific feature learning.

Main Methods:

  • Numerical simulation of a differentiable quantitative phase microscopy (∂-QPM) system.
  • Implementation of learnable optical processors for image compression at the hardware level.
  • Utilizing a reconstruction neural network for post-acquisition image decompression.

Main Results:

  • Achieved a compression factor of ×64 in numerical experiments.
  • Maintained high image fidelity with a Structural Similarity Index (SSIM) of ∼0.90.
  • Preserved image quality with a Peak Signal-to-Noise Ratio (PSNR) of ∼30 dB for cellular imaging.

Conclusions:

  • The proposed ∂-QPM framework offers a new pathway to significantly enhance QPM system throughput.
  • Learned optical compression effectively bypasses sensor pixel-rate limitations.
  • This approach promises unprecedented improvements for label-free, high-throughput microscopy.