Motion correction in optical coherence tomography volumes on a per A-scan basis using orthogonal scan patterns

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Abstract
High speed Optical Coherence Tomography (OCT) has made it possible to rapidly capture densely sampled 3D volume data. One key application is the acquisition of high quality in vivo volumetric data sets of the human retina. Since the volume is acquired in a few seconds, eye movement during the scan process leads to distortion, which limits the accuracy of quantitative measurements using 3D OCT data. In this paper, we present a novel software based method to correct motion artifacts in OCT raster scans. Motion compensation is performed retrospectively using image registration algorithms on the OCT data sets themselves. Multiple, successively acquired volume scans with orthogonal fast scan directions are registered retrospectively in order to estimate and correct eye motion. Registration is performed by optimizing a large scale numerical problem as given by a global objective function using one dense displacement field for each input volume and special regularization based on the time structure of the acquisition process. After optimization, each volume is undistorted and a single merged volume is constructed that has superior signal quality compared to the input volumes. Experiments were performed using 3D OCT data from the macula and optic nerve head acquired with a high-speed ultra-high resolution 850 nm spectral OCT as well as wide field data acquired with a 1050 nm swept source OCT instrument. Evaluation of registration performance and result stability as well as visual inspection shows that the algorithm can correct for motion in all three dimensions and on a per A-scan basis. Corrected volumes do not show visible motion artifacts. In addition, merging multiple motion corrected and registered volumes leads to improved signal quality. These results demonstrate that motion correction and merging improves image quality and should also improve morphometric measurement accuracy from volumetric OCT data.

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