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Deep learning-based single-shot phase retrieval algorithm for surface plasmon resonance microscope based refractive

Kitsada Thadson¹, Sarinporn Visitsattapongse¹, Suejit Pechprasarn^2,3

¹Department of Biomedical Engineering, School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok, 10520, Thailand.

Scientific Reports

|August 12, 2021

Summary

This summary is machine-generated.

A novel deep learning algorithm enables single-shot phase retrieval using a conventional microscope. This artificial intelligence approach achieves a lower detection limit for refractive index sensing compared to traditional methods.

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Area of Science:

Optics and Photonics
Artificial Intelligence in Microscopy
Biomedical Imaging

Background:

Phase retrieval is crucial for quantitative imaging in microscopy.
Conventional methods often require complex setups or multiple measurements.
Surface plasmon resonance (SPR) imaging is a sensitive technique for detecting refractive index changes.

Purpose of the Study:

To develop and validate a deep learning algorithm for single-shot phase retrieval.
To demonstrate its application in surface plasmon resonance imaging.
To compare its refractive index sensing performance against conventional methods.

Main Methods:

A context aggregation network architecture was employed for deep learning-based pattern recognition.
The algorithm was trained using simulated data and validated experimentally.
Performance was assessed using shot noise models, Monte Carlo simulations, and refractive index sensing experiments.

Main Results:

The deep learning algorithm successfully retrieved phase profiles from single grayscale images.
It demonstrated capability in surface plasmon resonance imaging, covering phase transitions from 0 to 2π rad.
The AI-based method achieved a refractive index detection limit of 4.67 × 10^-6 RIU, outperforming conventional intensity measurements (1.73 × 10^-5 RIU).

Conclusions:

Deep learning offers a viable approach for single-shot phase retrieval in conventional microscopes.
This AI-based method provides a simplified and effective platform for refractive index sensing.
While not matching interferometer sensitivity, it offers significant advantages over intensity-based measurements without complex instrumentation.