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Extracting Group Velocity Dispersion values using quantum-mimic Optical Coherence Tomography and Machine Learning.

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Area of Science:

  • Optical Physics
  • Biomedical Imaging
  • Machine Learning

Background:

  • Quantum-mimic Optical Coherence Tomography (Qm-OCT) images contain artifacts that obscure data.
  • These artifacts are uniquely related to the Group Velocity Dispersion (GVD) of materials.
  • Analyzing artifacts for GVD is challenging in multi-layered samples due to artifact overload.

Purpose of the Study:

  • To develop an automated method for inferring Group Velocity Dispersion (GVD) from Qm-OCT artifacts.
  • To overcome limitations of manual artifact analysis in complex, multi-layered structures.
  • To provide accurate GVD depth distribution profiles from Qm-OCT data.

Main Methods:

  • A neural network was trained using Qm-OCT data as input and dispersion profiles (GVD depth distribution) as output.
  • Noise was accounted for during the neural network training process.
  • The model was tested on BK7, sapphire, grape, and cucumber samples.

Main Results:

  • Accurate GVD values were estimated for BK7 and sapphire.
  • Qualitative GVD distribution was determined for biological samples (grape and cucumber).
  • The method demonstrated scalability and automated layer-specific GVD analysis.

Conclusions:

  • Machine learning offers an automated, scalable solution for GVD measurement using Qm-OCT artifacts.
  • The developed method surpasses traditional GVD retrieval techniques by eliminating user input and providing comprehensive layer analysis.
  • Further research can optimize accuracy by addressing noise and physical detection limitations.