Variability in illumination, signal quality, tilt and the amount of motion pose challenges for post-processing based 3D-OCT motion correction algorithms. We present an advanced 3D-OCT motion correction algorithm using image registration and orthogonal raster scan patterns aimed at addressing these challenges. An intensity similarity measure using the pseudo Huber norm and a regularization scheme based on a pseudo L0.5 norm are introduced. A two-stage registration approach was developed. In the first stage, only axial motion and axial tilt are coarsely corrected. This result is then used as the starting point for a second stage full optimization. In preprocessing, a bias field estimation based approach to correct illumination differences in the input volumes is employed. Quantitative evaluation was performed using a large set of data acquired from 73 healthy and glaucomatous eyes using SD-OCT systems. OCT volumes of both the optic nerve head and the macula region acquired with three independent orthogonal volume pairs for each location were used to assess reproducibility. The advanced motion correction algorithm using the techniques presented in this paper was compared to a basic algorithm corresponding to an earlier version and to performing no motion correction. Errors in segmentation-based measures such as layer positions, retinal and nerve fiber thickness, as well as the blood vessel pattern were evaluated. The quantitative results consistently show that reproducibility is improved considerably by using the advanced algorithm, which also significantly outperforms the basic algorithm. The mean of the mean absolute retinal thickness difference over all data was 9.9 um without motion correction, 7.1 um using the basic algorithm and 5.0 um using the advanced algorithm. Similarly, the blood vessel likelihood map error is reduced to 69% of the uncorrected error for the basic and to 47% of the uncorrected error for the advanced algorithm. These results demonstrate that our advanced motion correction algorithm has the potential to improve the reliability of quantitative measurements derived from 3D-OCT data substantially.

Structured Abstract PURPOSE To develop a robust, sensitive, and fully automatic algorithm to quantify diabetes related capillary dropout using optical coherence tomography (OCT) angiography (OCTA). METHODS A 1050 nm wavelength, 400 kHz A-scan rate swept-source OCT prototype was used to perform volumetric OCTA imaging over 3 mm × 3 mm fields in normal controls (n = 5), patients with diabetes without diabetic retinopathy (DR) (n = 7), patients with non-proliferative diabetic retinopathy (NPDR) (n = 9), and patients with proliferative diabetic retinopathy (PDR) (n = 5); for each patient, one eye was imaged. A fully automatic algorithm to quantify intercapillary areas was developed. RESULTS Of the 26 evaluated eyes, the segmentation was successful in 22 eyes (85%). The mean values of the 10 and 20 largest intercapillary areas, either including or excluding the foveal avascular zone, showed a consistent trend of increasing size from normal control eyes, to eyes with diabetic retinopathy but without DR, to NPDR eyes, and finally, to PDR eyes. CONCLUSIONS OCTA based screening and monitoring of DR patients is critically dependent on automated vessel analysis. The presented algorithm was able to automatically extract an intercapillary area based metric in patients having various stages of DR. Intercapillary area based approaches are likely more sensitive to early stage capillary dropout than vascular density based methods.