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In-silico clearing approach for deep refractive index tomography by partial reconstruction and wave-backpropagation
Osamu Yasuhiko1, Kozo Takeuchi2
1Central Research Laboratory, Hamamatsu Photonics K.K, 5000 Hirakuchi, Hamakita-ku, Hamamatsu, 434-8601, Shizuoka, Japan. osamu.yasuhiko@crl.hpk.co.jp.
Light, Science & Applications
|April 27, 2023
Summary
This study introduces a deep refractive index (RI) tomography method to visualize thick biological samples. The novel approach effectively overcomes scattering and aberrations for enhanced, label-free imaging of cellular structures.
Area of Science:
- Biophysics
- Optical Imaging
- Computational Biology
Background:
- Refractive index (RI) is crucial for understanding light-matter interactions and cellular properties in biological imaging.
- Conventional RI tomography struggles with thick samples due to multiple scattering (MS) and sample-induced aberration (SIA).
- Accurate visualization of RI distribution is essential for biologically relevant sample analysis.
Purpose of the Study:
- To develop a deep RI tomographic approach overcoming MS and SIA for enhanced thick sample reconstruction.
- To enable label-free, noninvasive deep visualization of multicellular specimens.
- To retrieve biologically relevant information from RI distributions.
Main Methods:
- Utilizes partial RI reconstruction from multiple holograms with angular diversity.
- Employs iterative backpropagation using reconstructed partial RI maps to build the full tomogram.
- Suppresses MS and SIA through repeated reconstruction and backpropagation steps.
Main Results:
- Successfully visualized a 140 µm multicellular spheroid within minutes.
- Demonstrated enhanced deep visualization capability and computational efficiency over conventional methods.
- Quantified high-RI structures and morphological changes within multicellular spheroids.
Conclusions:
- The proposed deep RI tomography method effectively overcomes MS and SIA for superior thick sample visualization.
- The technique offers label-free, noninvasive deep imaging, facilitating understanding of specimen architecture.
- It enables retrieval of biologically relevant information from RI distributions, aiding in the study of morphological changes.
