Correction of motion artifacts in OCT-AFI data collected in airways (Conference Presentation)

Abstract

Abstract: Optical coherence tomography (OCT) provides in vivo imaging with near-histologic resolution of tissue morphology. OCT has been successfully employed in clinical practice in non-pulmonary fields of medicine such as ophthalmology and cardiology. Studies suggest that OCT has the potential to be a powerful tool for the detection and localization of malignant and non-malignant pulmonary diseases. The combination of OCT with autofluorescence imaging (AFI) provides valuable information about the structural and metabolic state of tissues. Successful application of OCT or OCT-AFI to the field of pulmonary medicine requires overcoming several challenges. This work address those associated with motion: cardiac cycle, breathing and non-uniform rotation distortion (NURD) artifacts. Mechanically rotated endoscopic probes often suffer from image degradation due to NURD. In addition cardiac and breathing motion artifacts may be present in-vivo that are not seen ex-vivo. These motion artifacts can be problematic in OCT-AFI systems with slower acquisition rates and have been observed to generate identifiable prominent artifacts which make confident interpretation of observed structures (blood vessels, etc) difficult. Understanding and correcting motion artifact could improve the image quality and interpretation. In this work, the motion artifacts in pulmonary OCT-AFI data sets are estimated in both AFI and OCT images using a locally adaptive registration algorithm that can be used to correct/reduce such artifacts. Performance of the algorithm is evaluated on images of a NURD phantom and on in-vivo OCT-AFI datasets of peripheral lung airways.