A high-accuracy and high-efficiency digital volume correlation method to characterize in-vivo optic nerve head biomechanics from optical coherence tomography

In-vivo optic nerve head (ONH) biomechanics characterization is emerging as a promising way to study eye physiology and pathology. We propose a high-accuracy and high-efficiency digital volume correlation (DVC) method for the purpose of characterizing the in-vivo ONH deformation from volumes acquired by optical coherence tomography (OCT). Using a combination of synthetic tests and analysis of OCTs from monkey ONHs subjected to acute and chronically elevated intraocular pressure, we demonstrate that our proposed methodology overcomes several challenges for conventional DVC methods. First, it accounts for large ONH rigid body motion in the OCT volumes which could otherwise lead to analysis failure; second, sub-voxel accuracy displacement can be guaranteed despite high noise and low image contrast of some OCT volumes; third, computational efficiency is greatly improved, such that the memory consumption of our method is substantially lower than with conventional methods; fourth, we introduce a parameter measuring displacements confidence. Test of image noise effects showed that the proposed DVC method had displacement errors smaller than 0.028 voxels with speckle noise and smaller than 0.037 voxels with Gaussian noise; The absolute (relative) strain errors in the three directions were lower than 0.0018 (4%) with speckle noise and than 0.0045 (8%) with Gaussian noise. Compared with conventional DVC methods, the proposed DVC method had substantially improved overall displacement and strain errors under large body motions (lower by up to 70%), with 75% lower computation times, while saving about 30% memory. The study thus demonstrates the potential of the proposed technique to investigate ONH biomechanics.