The diffraction tomography theorem is adapted to one-dimensional length measurement. The resulting spectral interferometry technique is described and the first length measurements using this technique on a model eye and on a human eye in vivo are presented.

Measurement of intraocular distances by backscattering spectral interferometry

Amplitude decorrelation measurement is sensitive to transverse flow and immune to phase noise in comparison to Doppler and other phase-based approaches. However, the high axial resolution of OCT makes it very sensitive to the pulsatile bulk motion noise in the axial direction. To overcome this limitation, we developed split-spectrum amplitude-decorrelation angiography (SSADA) to improve the signal-to-noise ratio (SNR) of flow detection. The full OCT spectrum was split into several narrower bands. Inter-B-scan decorrelation was computed using the spectral bands separately and then averaged. The SSADA algorithm was tested on in vivo images of the human macula and optic nerve head. It significantly improved both SNR for flow detection and connectivity of microvascular network when compared to other amplitude-decorrelation algorithms.

Split-spectrum amplitude-decorrelation angiography with optical coherence tomography

Optical coherence tomography angiography.

Quantitative Optical Coherence Tomography Angiography of Choroidal Neovascularization in Age-Related Macular Degeneration

Optical Coherence Tomography Angiography of Optic Disc Perfusion in Glaucoma

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

Images of an object, such as OCT scans of a human eye, can include distortions and data gaps due to relative motion of the object and the image acquisition device. Methods and systems for correction of such distortions and data gaps are described herein. Motion-corrected data is arrived at by applying three- dimensional transforms to input three-dimensional data sets that represent at least partially overlapping regions of the imaged object. The three dimensional transforms are computed based on an objective function that accounts for similarity between the transformed three-dimensional data sets and the estimated motion of the object relative to an imaging instrument. Methods and systems described herein advantageously eliminate the need for postulated assumptions and reliance on landmarks and are capable of filling data gaps, thereby producing high quality, undistorted images of objects subject to movement during imaging. Multiple motion-corrected data sets can be merged or combined to produce a data set with improved image quality.

Method and apparatus for motion correction and image enhancement for optical coherence tomography

Automatic Nerve Fiber Layer Segmentation and Geometry Correction on Spectral Domain OCT Images Using Fuzzy C-Means Clustering

Motion Artifact Correction in OCT Volume Scans Using Image Registration

•Deformation field U between reference ONH R and a sample •Model healthy ONH variability by Principal Component Analysis (PCA) on deformation fields U of controls Ic •Crop deformations by the fraction represented by PCA model of controls 5.Statistical deformation model (SDM) of ONH to provide a mathematically derived and biologically interpretable description of ONH variability in glaucomatous optic atrophy. •PCA model on deformations U of –control samples Ic –glaucomatous cases Ig •Supervised attribute selection on modes → Identification of modes most discriminating between controls and glaucomatous

R. Bock

Papers

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

Method and apparatus for motion correction and image enhancement for optical coherence tomography

Automatic Nerve Fiber Layer Segmentation and Geometry Correction on Spectral Domain OCT Images Using Fuzzy C-Means Clustering

Motion Artifact Correction in OCT Volume Scans Using Image Registration

Statistical Deformation Modeling of the Optic Disk