Kalman-Based Real-Time Functional Decomposition for the Spectral Calibration in Swept Source Optical Coherence...

Area of Science:

• Biomedical Imaging
• Optical Engineering
• Signal Processing

Background:

• Current Swept-Source Optical Coherence Tomography (SS-OCT) systems face challenges with input signal-dependent nonlinearities.
• Existing methods rely on interpolation and resampling, which can introduce artifacts and limit imaging speed.
• Mach-Zehnder Interferometer signal processing is crucial for accurate OCT data acquisition.

Purpose of the Study:

• To develop a real-time adaptive algorithm for optimal sampling of interferometric signals in SS-OCT.
• To eliminate nonlinearities inherent in current SS-OCT signal processing.
• To enhance imaging speed and reduce artifacts in SS-OCT systems.

Main Methods:

• A real-time functional decomposition adaptive algorithm is proposed.
• The Kalman approach is utilized to estimate the wavenumber index parameter (k) from the Mach-Zehnder Interferometer signal.
• An adaptive level crossing sampler generates a k-linearizing sampling clock in real-time.

Main Results:

• The algorithm completely eliminates input signal-dependent nonlinearities.
• It removes the need for classical interpolation and resampling, preventing associated artifacts.
• Imaging acquisition speed is increased by 10X.
• The system demonstrates robustness to noise, validated by mathematical analysis and simulations.

Conclusions:

• The developed adaptive calibration algorithm provides an artifact-free SS-OCT realization.
• This real-time linearization scheme significantly improves imaging performance.
• The method offers a substantial advancement in SS-OCT technology for faster and more accurate imaging.