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

PROJECT TITLE :

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

ABSTRACT:

We present a methodology for automated, depth-resolved extraction of the attenuation coefficient from Optical Coherence Tomography (OCT) knowledge. In distinction to previous automated, depth-resolved methods, the Depth-Resolved Confocal (DRC) technique derives an invertible mapping between the measured OCT intensity data and also the attenuation coefficient while considering the confocal perform and sensitivity fall-off, that are critical to ensure accurate measurements of the attenuation coefficient in practical settings (e.g., clinical endoscopy). We tend to conjointly show that further improvement of the estimated attenuation coefficient is doable by formulating image denoising as a convex optimization downside that we tend to term Intensity Weighted Horizontal Total Variation (iwhTV). The performance and accuracy of DRC alone and DRC+iwhTV are validated with simulated information, optical phantoms, and ex-vivo porcine tissue. Our results suggest that implementation of DRC+iwhTV represents a completely unique method to improve OCT distinction for better tissue characterization through quantitative imaging.

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