Convolutional Neural Networks Enable Direct Strain Estimation in Quasistatic Optical Coherence Elastography

Assessing the biomechanical properties of tissues can provide important information for disease diagnosis and therapeutic monitoring. Optical coherence elastography (OCE) is an emerging technology for measuring the biomechanical properties of tissues. Clinical translation of this technology is underway, and steps are being implemented to streamline data collection and processing. OCE data can be noisy, data processing can require significant manual tuning, and a single acquisition may contain gigabytes of data. In this work, we introduce a convolutional neural network-based method to translate raw OCE phase data to strain for quasistatic OCE that is ~40X faster than the conventional least squares approach by bypassing many intermediate data processing steps. The results suggest that a machine learning approach may be a valuable tool for fast, efficient, and accurate extraction of biomechanical information from raw OCE data.