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 512 × 512 × 512 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 (∼2.1 × ) and depth of focus (∼5.6 × ) 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.

中文翻译：

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自适应光学光学相干断层扫描视网膜图像中单个 A 线的多参考全局配准

意义：自适应光学光学相干断层扫描 (AO-OCT) 技术可实现视网膜的非侵入性、高分辨率三维 (3D) 成像，并有望及早发现眼部疾病。然而，AO-OCT 数据被眼动伪影破坏，必须在后处理中去除，这个过程因大量数据而变得耗时。目标：有效去除单个 A 线水平的眼动伪影，包括任何单个参考体积中存在的眼动伪影。方法：我们开发了一种配准方法，该配准方法将 (1) 3D B 扫描配准算法与 (2) 全局 A 线配准算法级联，用于校正扭转眼球运动和图像缩放并生成全局无运动坐标。第一种算法将 3D 平移眼球运动校正到单个参考体积，并使用并行计算进行加速。第二种算法使用不同的参考体积结合第一种算法的多次运行的输出，然后进行仿射变换，允许将所有图像配准到单个 A 线级别的全局坐标系。结果：3D B 扫描算法以高配准精度和鲁棒性估计和校正 3D 平移运动，即使对于包含微眼跳的体积也是如此。平均注册音量将我们的图像质量指标提高了 22 dB。在图形处理单元上的 CUDA™ 中实现仅在 10.6 秒内注册一个 512 × 512 × 512 的体积，比中央处理单元上的 MATLAB™ 快 150 倍。全局A线算法最大限度地减少图像失真，提高锥光感受器马赛克的规律性，并支持增强低对比度视网膜细胞特征的可视化。平均注册音量将我们的图像质量提高了 9.4 dB。它还允许将成像视场 (∼2.1 × ) 和焦深 (∼5.6 × ) 扩展到超出单参考配准所能达到的范围。结论：我们可以使用全局坐标系在单个 A 线级别有效地校正所有 3D 中的眼睛运动。