Abstract
.Significance: Adaptive optics optical coherence tomography (AO-OCT) technology enables non-invasive, high-resolution three-dimensional (3D) imaging of the retina and promises earlier detection of ocular disease. However, AO-OCT data are corrupted by eye-movement artifacts that must be removed in post-processing, a process rendered time-consuming by the immense quantity of data.Aim: To efficiently remove eye-movement artifacts at the level of individual A-lines, including those present in any individual reference volume.Approach: We developed a registration method that cascades (1) a 3D B-scan registration algorithm with (2) a global A-line registration algorithm for correcting torsional eye movements and image scaling and generating global motion-free coordinates. The first algorithm corrects 3D translational eye movements to a single reference volume, accelerated using parallel computing. The second algorithm combines outputs of multiple runs of the first algorithm using different reference volumes followed by an affine transformation, permitting registration of all images to a global coordinate system at the level of individual A-lines.Results: The 3D B-scan algorithm estimates and corrects 3D translational motions with high registration accuracy and robustness, even for volumes containing microsaccades. Averaging registered volumes improves our image quality metrics up to 22 dB. Implementation in CUDA™ on a graphics processing unit registers a volume in only 10.6 s, 150 times faster than MATLAB™ on a central processing unit. The global A-line algorithm minimizes image distortion, improves regularity of the cone photoreceptor mosaic, and supports enhanced visualization of low-contrast retinal cellular features. Averaging registered volumes improves our image quality up to 9.4 dB. It also permits extending the imaging field of view () and depth of focus () beyond what is attainable with single-reference registration.Conclusions: We can efficiently correct eye motion in all 3D at the level of individual A-lines using a global coordinate system.
Highlights
Analysis of Adaptive optics-optical coherence tomography (AO-OCT) data is impeded by two factors: First, the time to acquire a single volume is sufficient for it to be distorted by eye motions such as tremors, drifts, and microsaccades,[5] creating artifacts that are many times larger than the cells being imaged.[6]
Our results using relative contrast, image sharpness ratio (ISR), and mean squared error (MSE) are consistent with our visual observations and show that our 3D B-scan registration algorithm substantially enhances en face image quality by correcting motion artifacts and permitting averaging of multiple volumes
We can use the global coordinate system of the global A-line registration algorithm to align volumes focused at different depths by selecting a set of reference volumes that span the range of depths (a.k.a, focus stacking)
Summary
May support earlier detection and improved monitoring of prevalent blinding retinal diseases, such as glaucoma, diabetic retinopathy, and age-related macular degeneration.[1,2,3,4] analysis of AO-OCT data is impeded by two factors: First, the time to acquire a single volume is sufficient for it to be distorted by eye motions such as tremors, drifts, and microsaccades,[5] creating artifacts that are many times larger than the cells being imaged.[6] These motion artifacts must be measured and compensated for to permit proper analysis of the retinal tissue images We address this problem by proposing a cascade of two registration algorithms that we call 3D B-scan registration[7] and global A-line registration. The algorithms correct for XYZ translational eye movements, torsional eye movements, and variations in image magnification, and generate global motion-free coordinates
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