Spectroscopic optical coherence tomography with graphics processing unit based analysis of three dimensional data sets

Abstract

Spectroscopic optical coherence tomography (OCT) is an extension of the standard backscattering intensity analysis of OCT. It enables depth resolved monitoring of molecular and structural differences of tissue. One drawback of most methods to calculate the spectroscopic data is the long processing time. Also systematic and stochastic errors make the interpretation of the results challenging. Our approach combines modern signal processing tools with powerful graphics processing unit (GPU) programming. The processing speed for the spectroscopic analysis is nearly 3 mega voxel per second. This allows us to analyze multiple B-Scans in a few seconds and to display the results as a three dimensional data set. Our algorithm contains the following steps in addition to the conventional processing for frequency domain OCT: a quality map to exclude noisy parts of the data, spectral analysis by short time Fourier transform, feature reduction by Principal Component Analysis, unsupervised pattern recognition with K-means and rendering of the gray scale backscattering OCT data which is superimposed with a color map that is based on the results of the pattern recognition algorithm. Our set up provides a spectral range from 650-950nm and is optimized to suppress chromatic errors. In a proof-of-principle attempt, we already achieved additional spectroscopic contrast in phantom samples including scattering microspheres of different sizes and ex vivo biological tissue. This is an important step towards a system for real time spectral analysis of OCT data, which would be a powerful diagnosis tool for many diseases e.g. cancer detection at an early stage.

Comparison of different metrics for analysis and visualization in spectroscopic optical coherence tomography

“…Most algorithms require a short time for the processing using our computer system, and are therefore after optimization suited for real time imaging. This has already been demonstrated for the PCA and K-Means method by the use of General Purpose Graphics Processing Unit programming by Jaedicke et al [44].…”

Contrast enhancement methods in Optical Coherence Tomography using spectral features

“…when a strongly modulated spectrum produces a huge shift of the center of mass. A more sophisticated approach is the use of pattern recognition techniques to distinguish between different areas in a structural image based on spectral features [6] [7].…”

Performance Review of Multiple Reference Versus Time Domain Optical Coherence Tomography

“…Those applications would benefit from the larger detection bandwidth of the photo-diode compared to charge-coupled detectors in other OCT technologies. [14]- [17].…”