Visualization of Breast Cancer Using Contrast-Enhanced Optical Coherence Elastography Based on Tissue Heterogeneity

By mapping the mechanical properties of tissue, elastography can improve identification of breast cancer. On the macro-scale, ultrasound elastography and magnetic resonance elastography have emerged as effective clinical methods for the diagnosis of tumors. On the micro-scale, optical coherence elastography (OCE) shows promise for intraoperative tumor margin assessment during breast-conserving surgery. Whilst several OCE studies have demonstrated strong potential, the mechanical models used require the assumption of uniaxial stress throughout the sample. However, breast tissue is heterogeneous and contains compressible features (e.g., ducts and blood vessels) and collagen-rich fibrotic features (e.g., stroma). This heterogeneity can invalidate the assumption of uniaxial stress and reduce the accuracy of OCE, often making it challenging to interpret images. Here, we demonstrate a new variant of OCE based on mapping the Euler angle, i.e., the angle between the principal compression and the loading axis induced by tissue heterogeneity, which removes the assumption of uniaxial deformation. This is enabled by a hybrid three-dimensional (3-D) displacement estimation method that combines phase-sensitive detection and complex cross-correlation, providing access to the 3-D displacement and 3-D strain tensor on the micro-scale. Through experiments on phantoms, we demonstrate that an accuracy of 98.6%, a sensitivity of 0.95° (i.e., 16.58 mrad), and a spatial resolution as high as $36~\mu $ m can be achieved in Euler angle imaging. We demonstrate the potential of Euler angle imaging for visualization of breast cancer. Through close correspondence with histology, our results show that mapping the Euler angle provides additional contrast to both optical coherence tomography and a compression OCE technique in identifying cancer. Mapping the Euler angle in breast tissue may provide a new biomarker for intraoperative tumor margin assessment.