Abstract
Recognizing the properties of elastic tissue can facilitate surgical navigation, e.g., when localizing lesions by palpation. However, palpation is very subjective and often unavailable in minimally invasive surgery. High-speed optical coherence elastography (OCE) adapted for intraoperative use could enable elasticity estimation by measuring the propagation of mechanically stimulated waves. However, robust estimation of wave velocity can be challenging, and reconstruction of the elastic modulus is highly dependent on the correct modeling of wave propagation. We therefore consider deep learning for the end-to-end estimation of elasticity from OCE phase data. Since optical coherence tomography inherently produces a temporal sequence of one-dimensional axial scans (A-scans), we consider transformer-based deep learning models to directly process A-scan sequences. For homogeneous tissue phantoms with known elastic properties, we obtain a mean error of 1.64 kPa, which significantly improves elasticity reconstruction compared to conventional processing and the best CNN-based approach with 7.80 kPa and 5.55 kPa, respectively. Furthermore, we demonstrate generalization to heterogeneous phantoms with inclusions and assess the elasticity of soft tissue samples, including heart, kidney, and liver. The results show that transformer architectures are well suited for reconstructing elasticity from A-scan sequences in OCE.