Toward an automated method for optical coherence tomography characterization

With the increasing use of optical coherence tomography (OCT) in biomedical applications, robust yet simple methods for calibrating and benchmarking a system are needed. We present here a procedure based on a calibration object complemented with an algorithm that analyzes three-dimensional OCT datasets to retrieve key characteristics of an OCT system. The calibration object combines state-of-the-art tissue phantom material with a diamond-turned aluminum multisegment mirror. This method is capable of determining rapidly volumetric field-of-view, axial resolution, and image curvature. Moreover, as the phantom material mimics biological tissue, the system’s signal and noise levels can be evaluated in conditions close to biological experiments. We believe this method could improve OCT quantitative data analysis and help OCT data comparison for longitudinal or multicenter studies.