Higher-order regression three-dimensional motion-compensation method for real-time optical coherence tomography...

Optical coherence tomography (OCT) allows high-resolution volumetric three-dimensional (3D) imaging of biological tissues in vivo. However, 3D-image acquisition can be time-consuming and often suffers from motion artifacts due to involuntary and physiological movements of the tissue, limiting the reproducibility of quantitative measurements.

To achieve real-time 3D motion compensation for corneal tissue with high accuracy.

We propose an OCT system for volumetric imaging of the cornea, capable of compensating both axial and lateral motion with micron-scale accuracy and millisecond-scale time consumption based on higher-order regression. Specifically, the system first scans three reference B-mode images along the C-axis before acquiring a standard C-mode image. The difference between the reference and volumetric images is compared using a surface-detection algorithm and higher-order polynomials to deduce 3D motion and remove motion-related artifacts.

System parameters are optimized, and performance is evaluated using both phantom and corneal (ex vivo) samples. An overall motion-artifact error of <4.61 microns and processing time of about 3.40 ms for each B-scan was achieved.

Higher-order regression achieved effective and real-time compensation of 3D motion artifacts during corneal imaging. The approach can be expanded to 3D imaging of other ocular tissues. Implementing such motion-compensation strategies has the potential to improve the reliability of objective and quantitative information that can be extracted from volumetric OCT measurements.