Processing pipeline for large optical coherence elastography datasets with quasi-static air-jet excitation: application to human brain tumor tissue

Optical coherence elastography (OCE) is a powerful imaging modality for assessing the mechanical properties of biological tissues. We employed an OCE system based on an Optores OMES 3.2 MHz OCT platform combined with an in-house developed air-jet excitation source to characterize healthy and tumorous (meningioma) human brain tissue. This paper presents a comprehensive software framework for processing large OCE datasets, enabling robust extraction of characteristic features from phase-derived displacement data and calculation of mechanical proxy parameters for detailed tissue characterization. Feature detection is achieved using a modified triangle threshold algorithm applied to the displacement curves from the OCE phase data. Extensive pre- and post-processing steps, including percentile-based filtering and adaptive histogram equalization, are applied to mitigate phase unwrapping errors and enhance visualization of the high dynamic range of OCE data. Exemplary measurements on human brain tumor samples demonstrate the framework's ability to differentiate between tissue types, highlighting its potential for future clinical and research applications.