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This study introduces an improved inverse-scattering method for optical imaging in thick biological tissues. The enhanced technique achieves high-resolution, label-free 3D imaging by optimizing reconstruction strategies for scattering samples.

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Area of Science:

  • Biophysics
  • Optical Imaging
  • Computational Microscopy

Background:

  • Multiple scattering in biological samples degrades optical imaging quality by obscuring sample-specific details.
  • Physics-based inverse-scattering methods computationally reconstruct samples but face challenges due to non-convex optimization, leading to inaccuracies in highly scattering media.

Purpose of the Study:

  • To investigate how different implementation strategies for inverse-scattering methods impact reconstruction quality.
  • To develop a robust inverse-scattering approach for high-resolution, label-free 3D imaging in thick biological samples.

Main Methods:

  • Utilized multi-slice beam propagation (MSBP), a non-convex inverse-scattering technique, to reconstruct the 3D refractive index (RI) of scattering samples.
  • Systematically evaluated MSBP performance on phantoms and biological samples.
  • Employed an amplitude-only cost function within the inverse-solver and incorporated angular and defocus diversity in scattering measurements.

Main Results:

  • Demonstrated that specific implementation strategies significantly influence inverse-scattering reconstruction quality.
  • Achieved high-quality, fully-volumetric RI imaging with subcellular resolution and label-free 3D contrast.
  • Successfully imaged diverse, multiple-scattering samples, including thick biological tissues.

Conclusions:

  • Optimized inverse-scattering strategies, particularly using amplitude-only cost functions and diverse measurements, enable robust 3D RI imaging.
  • This work establishes a foundation for applying inverse-scattering techniques to deep-tissue imaging in multicellular samples.
  • Introduces a novel paradigm for deep-tissue computational imaging with biologically interpretable results.