Abstract
Proximal rotary scanning is predominantly used in the clinical practice of endoscopic and intravascular OCT, mainly because of the much lower manufacturing cost of the probe compared to distal scanning. However, proximal scanning causes severe beam stability issues (also known as non-uniform rotational distortion, NURD), which hinders the extension of its applications to functional imaging, such as OCT elastography (OCE). In this work, we demonstrate the abilities of learning-based NURD correction methods to enable the imaging stability required for intensity-based OCE. Compared with the previous learning-based NURD correction methods that use pseudo distortion vectors for model training, we propose a method to extract real distortion vectors from a specific endoscopic OCT system, and validate its superiority in accuracy under both convolutional-neural-network- and transformer-based learning architectures. We further verify its effectiveness in elastography calculations (digital image correlation and optical flow) and the advantages of our method over other NURD correction methods. Using the air pressure of a balloon catheter as a mechanical stimulus, our proximal-scanning endoscopic OCE could effectively differentiate between areas of varying stiffness of atherosclerotic vascular phantoms. Compared with the existing endoscopic OCE methods that measure only in the radial direction, our method could achieve 2D displacement/strain distribution in both radial and circumferential directions.