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Optical recognition of constructs using hyperspectral imaging and detection (ORCHID)
Ren A Odion1,2, Tuan Vo-Dinh3,4,5
1Fitzpatrick Institute for Photonics, Duke University, Durham, NC, USA.
Scientific Reports
|December 8, 2022
Summary
A new method, Optical Recognition of Constructs Using Hyperspectral Imaging and Detection (ORCHID), enables deep 3D imaging of samples. ORCHID collects spectral and spatial data, overcoming limitations of previous techniques for enhanced sample analysis.
Area of Science:
- Optical Imaging
- Spectroscopy
- Nanotechnology
Background:
- Deep sample imaging is challenging, requiring specialized techniques like spatially offset optical spectroscopy.
- Current methods offer only one-dimensional spectral information (depth).
Purpose of the Study:
- To introduce a general and practical method, Optical Recognition of Constructs Using Hyperspectral Imaging and Detection (ORCHID).
- To enable deep 3D imaging with both spatial and spectral data collection.
Main Methods:
- ORCHID integrates spatial offset detection with hyperspectral imaging and digital image processing.
- It computationally bins 2D optical data based on radial pixel distances.
- A tunable filter is used for hyperspectral data acquisition.
Main Results:
- ORCHID collects optical signals from deep within samples across X, Y, Z, and spectral dimensions.
- Demonstrated proof of principle using surface-enhanced Raman scattering (SERS) nanostars and quantum dots.
- Generated hyperspectral data cubes that spatially locate nanoparticle volumes and provide spectral information.
Conclusions:
- ORCHID is a versatile modality applicable to various narrow-band optical techniques.
- It overcomes the depth and dimensionality limitations of prior deep imaging methods.
- Enables in-depth 3D imaging and characterization of materials.
