Self-Supervised Bulk Motion Artifact Removal in Optical Coherence Tomography Angiography

Abstract

Optical coherence tomography angiography (OCTA) is an important imaging modality in many bioengineering tasks. The image quality of OCTA, however, is often hurt by Bulk Motion Artifacts (BMA), which are due to micromotion of subjects and typically appear as bright stripes surrounded by blurred areas. State-of-the-art BMA handling solutions usually treat the problem as an image inpainting one with deep neural network algorithms. These solutions, however, require numerous training samples with nontrivial annotation. Nevertheless, this context-based inpainting model has limited correction capability because it discards the rich structural and appearance information carried in the BMA stripe region. To address these issues, in this paper we propose a self-supervised content-aware BMA recover model. First, the gradient-based structural information and appearance feature are extracted from the BMA area and injected into the model to capture more connectivity. Second, with easily collected defective masks, the model is trained in a self-supervised manner that only the clear areas are for training while the BMA areas for inference. With structural information and appearance feature from noisy image as references, our model could correct larger BMA and produce better visualizing result. Only 2D images with defective masks are involved so our method is more efficient. Experiments on OCTA of mouse cortex demonstrate that our model could correct most BMA with extremely large sizes and inconsistent intensities while existing methods fail. Figure 1. Illustration of OCTA affected by Bulk Motion Artifacts (BMA). Our work focuses on the BMA removal task in vessel mask (blue rectangle in dashed line). The enhanced OCTA after BMA removal has better visualization quality.