Deep learning-based rotation frequency estimation and NURD correction for IVOCT image data

Abstract

Atherosclerotic plaque in coronary arteries can lead to myocardial infarction and is one of the leading causes of death. Intravascular optical coherence tomography (IVOCT) can be used to image the affected blood vessels for assessment and treatment. However, catheter bending often causes changes in the rotation frequency of the optical probe during acquisition. The resulting non-uniform rotation distortion (NURD) artefacts complicate the image interpretation and may affect the diagnosis. Deep learning methods have been proposed to analyze IVOCT image data, including plaque detection [1] and feature extraction [2]. We present a novel approach to directly estimate the rotation frequency of the optical probe from a sequence of IVOCT images. We illustrate that this allows a proper correction of NURD artefacts