We apply three optical coherence tomography (OCT) image analysis techniques to extract morphometric information from OCT images obtained on peripheral nerves of rat. The accuracy of each technique is evaluated against histological measurements accurate to + / − 1 μm. The three OCT techniques are: (1) average depth-resolved profile (ADRP), (2) autoregressive spectral estimation (AR-SE), and (3) correlation of the derivative spectral estimation (CoD-SE). We introduce a scanning window to the ADRP technique, which provides transverse resolution and improves epineurium thickness estimates—with the number of analyzed images showing agreement with histology increasing from 2 / 10 to 5 / 10 (Kruskal–Wallis test, α = 0.05). A method of estimating epineurium thickness, using the AR-SE technique, showed agreement with histology in 6 / 10 analyzed images (Kruskal–Wallis test, α = 0.05). Using a tissue sample in which histology identified two fascicles with an estimated difference in mean fiber diameter of 4 μm, the AR-SE and CoD-SE techniques both correctly identified the fascicle with larger fiber diameter distribution but incorrectly estimated the magnitude of this difference as 0.5 μm. The ability of the OCT signal analysis techniques to extract accurate morphometric details from peripheral nerves is promising but restricted in depth by scattering in adipose and neural tissues.
We propose a simple, numerical procedure for dispersion mapping of an object, which can be easily used for every OCT data set acquired from a Fourier domain system employing light source of sufficiently broad spectral bandwidth. The mapped value is Group Velocity Dispersion and its determination is based on the estimation of the difference in material’s thickness calculated from two images obtained for different spectral ranges. Instead of using two separate light sources to achieve that, we propose to apply a filter-based, numerical procedure that synthesizes two spectra centered around different wavelengths from one sufficiently broadband spectrum.
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