We present a method, based on a single scattering model, to calculate the attenuation coefficient of each pixel in optical coherence tomography (OCT) depth profiles. Numerical simulations were used to determine the model's response to different depths and attenuation coefficients. Experiments were performed on uniform and layered phantoms with varying attenuation coefficients. They were measured by a 1300 nm OCT system and their attenuation coefficients were evaluated by our proposed method and by fitting the OCT slope as the gold standard. Both methods showed largely consistent results for the uniform phantoms. On the layered phantom, only our proposed method accurately estimated the attenuation coefficients. For all phantoms, the proposed method largely reduced the variability of the estimated attenuation coefficients. The method was illustrated on an in-vivo retinal OCT scan, effectively removing common imaging artifacts such as shadowing. By providing localized, per-pixel attenuation coefficients, this method enables tissue characterization based on attenuation coefficient estimates from OCT data.

Abstract:In conventional phase-resolved OCT blood flow is detected from phase changes between successive A-scans. Especially in high-speed OCT systems this results in a short evaluation time interval. This method is therefore often unable to visualize complete vascular networks since low flow velocities cause insufficient phase changes. This problem was solved by comparing B-scans instead of successive A-scans to enlarge the time interval. In this paper a detailed phase-noise analysis of our OCT system is presented in order to calculate the optimal time intervals for visualization of the vasculature of the human retina and choroid. High-resolution images of the vasculature of a healthy volunteer taken with various time intervals are presented to confirm this analysis. The imaging was performed with a backstitched B-scan in which pairs of small repeated B-scans are stitched together to independently control the time interval and the imaged lateral field size. A time interval of ≥2.5 ms was found effective to image the retinal vasculature down to the capillary level. The higher flow velocities of the choroid allowed a time interval of 0.64 ms to reveal its dense vasculature. Finally we analyzed depth-resolved histograms of volumetric phase-difference data to assess changes in amount of blood flow with depth. This analysis indicated different flow regimes in the retina and the choroid.

In optical frequency domain imaging (OFDI) the measurement of interference fringes is not exactly reproducible due to small instabilities in the swept-source laser, the interferometer and the data-acquisition hardware. The resulting variation in wavenumber sampling makes phase-resolved detection and the removal of fixed-pattern noise challenging in OFDI. In this paper this problem is solved by a new post-processing method in which interference fringes are resampled to the exact same wavenumber space using a simultaneously recorded calibration signal. This method is implemented in a high-speed (100 kHz) high-resolution (6.5 µm) OFDI system at 1-µm and is used for the removal of fixed-pattern noise artifacts and for phase-resolved blood flow measurements in the human choroid. The system performed close to the shot-noise limit (<1dB) with a sensitivity of 99.1 dB for a 1.7 mW sample arm power. Suppression of fixed-pattern noise artifacts is shown up to 39.0 dB which effectively removes all artifacts from the OFDI-images. The clinical potential of the system is shown by the detection of choroidal blood flow in a healthy volunteer and the detection of tissue reperfusion in a patient after a retinal pigment epithelium and choroid transplantation.

Current OCT devices provide three-dimensional (3D) in-vivo images of the human retina. The resulting very large data sets are difficult to manually assess. Automated segmentation is required to automatically process the data and produce images that are clinically useful and easy to interpret. In this paper, we present a method to segment the retinal layers in these images. Instead of using complex heuristics to define each layer, simple features are defined and machine learning classifiers are trained based on manually labeled examples. When applied to new data, these classifiers produce labels for every pixel. After regularization of the 3D labeled volume to produce a surface, this results in consistent, three-dimensionally segmented layers that match known retinal morphology. Six labels were defined, corresponding to the following layers: Vitreous, retinal nerve fiber layer (RNFL), ganglion cell layer & inner plexiform layer, inner nuclear layer & outer plexiform layer, photoreceptors & retinal pigment epithelium and choroid. For both normal and glaucomatous eyes that were imaged with a Spectralis (Heidelberg Engineering) OCT system, the five resulting interfaces were compared between automatic and manual segmentation. RMS errors for the top and bottom of the retina were between 4 and 6 μm, while the errors for intra-retinal interfaces were between 6 and 15 μm. The resulting total retinal thickness maps corresponded with known retinal morphology. RNFL thickness maps were compared to GDx (Carl Zeiss Meditec) thickness maps. Both maps were mostly consistent but local defects were better visualized in OCT-derived thickness maps.

The measurements clearly demonstrated that the μ(att) of the RNFL decreased with increasing disease severity. The RNFL attenuation coefficient may serve as a new method to quantify glaucoma in SD-OCT images.