Optimized number of the primary singular values for image reconstruction in reflection matrix based optical coherence...

Area of Science:

• Biomedical Optics
• Medical Imaging Technology
• Signal Processing

Background:

• Reflection matrix based optical coherence tomography (OCT) offers potential for doubled imaging depth.
• Image quality and imaging depth in OCT are critically dependent on the number of singular values used for reconstruction.
• Current methods lack a robust approach for optimizing singular value selection.

Purpose of the Study:

• To develop and validate a novel method for optimizing the number of primary singular values in reflection matrix OCT image reconstruction.
• To improve imaging depth and image quality in OCT by accurately determining the optimal number of singular values.
• To establish a comprehensive approach for singular value optimization in reflection matrix OCT.

Main Methods:

• Proposed a method utilizing correlation analysis between image pairs reconstructed with varying singular values and their remainders.
• Developed a feature extraction process from correlation and other curves.
• Employed a long short-term memory (LSTM) network classifier to identify the optimal number of singular values.
• Validated the method using simulated targets with varying filling fractions and signal-to-noise ratios (SNR).

Main Results:

• The developed method successfully identified the optimized number of primary singular values for OCT image reconstruction.
• Reconstructed images showed robust similarity to reference images, indicating high fidelity.
• The proposed method outperformed two current adopted methods in image reconstruction quality.
• Demonstrated robustness across different simulated target parameters (filling fraction and SNR).

Conclusions:

• This study presents the first comprehensive investigation into optimizing primary singular values for reflection matrix OCT.
• The proposed correlation-based LSTM method effectively enhances OCT imaging depth and image quality.
• The findings pave the way for more accurate and deeper imaging in reflection matrix OCT applications.