Automated, Depth-Resolved Estimation of the Attenuation Coefficient From Optical Coherence Tomography Data

Area of Science:

• Biomedical Optics
• Medical Imaging
• Quantitative Imaging

Background:

• Optical Coherence Tomography (OCT) is a valuable imaging modality.
• Accurate measurement of the attenuation coefficient is crucial for OCT-based tissue characterization.
• Existing automated methods often neglect critical factors like confocal effects and sensitivity fall-off.

Purpose of the Study:

• To present a novel automated method for depth-resolved extraction of the attenuation coefficient from OCT data.
• To incorporate the confocal function and sensitivity fall-off for improved accuracy.
• To enhance the method further using convex optimization for image denoising.

Main Methods:

• Developed the Depth-Resolved Confocal (DRC) technique to create an invertible mapping between OCT intensity and attenuation coefficient.
• Integrated Intensity Weighted Horizontal Total Variation (iwhTV) as a convex optimization problem for image denoising.
• Validated the DRC and DRC+iwhTV methods using simulated data, optical phantoms, and ex-vivo porcine tissue.

Main Results:

• The DRC technique accurately extracts the attenuation coefficient by accounting for confocal effects and sensitivity fall-off.
• DRC+iwhTV demonstrated further improvement in attenuation coefficient estimation.
• Validated performance across diverse datasets, confirming robustness and accuracy.

Conclusions:

• The DRC technique provides an accurate, automated method for depth-resolved attenuation coefficient extraction in OCT.
• DRC+iwhTV offers enhanced quantitative imaging capabilities for OCT.
• This approach can significantly improve OCT contrast for superior tissue characterization in clinical and research settings.