Deconvolution methods for mitigation of transverse blurring in optical coherence tomography.

Area of Science:

• Biomedical Optics
• Medical Imaging Technology
• Tissue Engineering

Background:

• Optical coherence tomography (OCT) is vital for non-invasive optical biopsies.
• Achieving high imaging resolution in OCT is essential for detailed tissue analysis.
• Transverse resolution in OCT is often limited by optical aberrations and scattering.

Purpose of the Study:

• To present a Gaussian beam deconvolution method for mitigating transverse blurring in OCT.
• To investigate deconvolution algorithms for improving OCT transverse resolution.
• To enhance the representation of specimens in OCT imaging.

Main Methods:

• Gaussian beam deconvolution using focal-dependent kernels.
• Investigated direct inverse problem with truncated singular value decomposition and Tikhonov regularization.
• Developed a dynamically iterative Richardson-Lucy algorithm with a beam-width-dependent scheme.

Main Results:

• The developed deconvolution method successfully reduces transverse blurring in OCT.
• Improvements in transverse point-spread-function were observed for sparse scattering samples.
• Deblurring was achieved in regions up to twice the confocal region size, validated experimentally.

Conclusions:

• Gaussian beam deconvolution significantly enhances transverse resolution in OCT.
• The method improves cellular detection and categorization in sparse scattering specimens like engineered tissues.
• This approach offers improved OCT imaging without requiring new hardware or multiple acquisitions.