Three‐dimensional static optical coherence elastography based on inverse compositional Gauss‐Newton digital volume correlation

Abstract

The three‐dimensional (3D) mechanical properties characterization of tissue is essential for physiological and pathological studies, as biological tissue is mostly heterogeneous and anisotropic. A digital volume correlation (DVC)‐based 3D optical coherence elastography (OCE) method is developed to measure the 3D displacement and strain tensors. The DVC algorithm includes a zero‐mean normalized cross‐correlation criterion‐based coarse search regime, an inverse compositional Gauss‐Newton fine search algorithm and a local ternary quadratic polynomial fitting strain calculation method. A 3D optical coherence tomography (OCT) scanning protocol is proposed through theoretical analysis and experimental verification. Measurement errors of the DVC‐based 3D OCE method are evaluated to be less than 2.0 μm for displacements and 0.30% for strains by rigid body motion experiments. The 3D displacements and strains of a phantom and a specimen of chicken breast tissue under compression are measured. Results of the phantom show a good agreement with theoretical analysis and tensile testing. The strains of the chicken breast tissue indicate anisotropic biomechanical properties. This study provides an effective method for 3D biomechanical property studies of soft tissue and improves the development of 3D OCE techniques.

High‐accuracy optical coherence elastography digital volume correlation methods to measure depth regions with low correlation

“…In biological tissue imaging, the probability of multiple scattering events increases with depth. The zero-mean normalized cross-correlation (ZNCC) criterion [19] is used to calculate the correlation coefficient distribution of a 3D OCT image. The correlation coefficient decreases along the axial direction.…”

“…Based on the above scheme, the memory required for parallel computing is optimized by keeping track of the amount of data required for each pass layer computation. The axial size of the transfer layer data used by the DVC method is shown in Equation (19).…”

“…In 2013, Fu et al proposed DVC based OCE method for the first time [9] and measured the 3D full-field deformation of a cornea specimen [17]. In the following decade, the OCE method based on the DVC technique has been applied to the measurement of internal 3D deformation of biological tissues such as ophthalmology and blood vessels [18][19][20][21]. Some scholars started to focus on algorithm optimization work when combining DVC with OCE.…”

“…Some scholars started to focus on algorithm optimization work when combining DVC with OCE. Meng et al [19] introduced the efficient and high-precision inverse Gaussian Newton DVC algorithm into the OCE method, which enabled the accuracy of displacement measurement and strain calculation to reach 2 μm and 0.003, respectively. Wu et al [22] used the second-order shape function to match the complex deformation and extended the tracking strain range to 0.095 with a relative error reduction of 30%-50%.…”

Digital Volume Correlation for large deformations of soft tissues: Pipeline and proof of concept for the application to breast ex vivo deformations

“…Several works used DVC to map strain fields for arteries [29], tendons [30], intervertebral disks [31,32], tissue interfaces with prosthetics [33] or even the mitral valve [34]. Optical coherence elastography/tomography techniques (e.g., for the identification of elastic properties) based on DVC with 3D infinitesimal strain measurements were performed ex vivo on mesoscopic (i.e., millimetric regions of interest) human [35] and chicken [36] breast tissues. As DVC requires high quality images to properly converge, the regions of interest are usually of the order of centimeters (due to technical limitations of setups).…”