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Predictive coding compressive sensing optical coherence tomography hardware implementation.

Diego M Song Cho1, Haiqiu Yang2, Zizheng Jia2

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This study demonstrates the first hardware-based sub-Nyquist sampling using compressed sensing (CS) in optical coherence tomography (OCT). This approach significantly reduces imaging time while maintaining high-accuracy image reconstruction for various samples.

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Area of Science:

  • Biomedical Imaging
  • Optical Coherence Tomography (OCT)
  • Signal Processing

Background:

  • Compressed sensing (CS) theory allows for high-accuracy image recovery from undersampled data, significantly below the Nyquist rate.
  • Previous CS applications primarily focused on synthetic undersampling and reconstruction simulations.
  • Hardware implementation of sub-Nyquist sampling for OCT has remained a challenge.

Purpose of the Study:

  • To demonstrate the first physical, hardware-based sub-Nyquist sampling using a galvanometer-based OCT system.
  • To validate the efficacy of compressed sensing for reconstructing images acquired with significantly reduced data density.
  • To assess the imaging time reduction and reconstruction accuracy achieved by the proposed method.

Main Methods:

  • Implementation of a galvanometer-based OCT system capable of sub-Nyquist sampling.
  • Acquisition of OCT data from various samples using the developed hardware.
  • Application of compressed sensing algorithms for image reconstruction from undersampled data.
  • Quantitative evaluation of reconstruction accuracy using relative error (RE) and mean square error (MSE).

Main Results:

  • Successful hardware-based sub-Nyquist sampling was achieved in an OCT system.
  • Volume scanning time was reduced by 89% (12.5% compression rate).
  • Reconstructed images showed high accuracy with relative error < 20% and MSE ≈ 1%.

Conclusions:

  • The study presents the first physical demonstration of sub-Nyquist sampling with CS in OCT.
  • This hardware-based approach significantly accelerates imaging acquisition while preserving image quality.
  • The findings pave the way for faster and more efficient OCT imaging in various applications.