  Optical coherence tomography angiography (OCTA) is an important imaging
modality in many bioengineering tasks. The image quality of OCTA, however, is
often degraded 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 methods usually treat BMA removal as a learning-based image
inpainting problem, but require numerous training samples with nontrivial
annotation. In addition, these methods discard 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 removal
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, in which only the clear areas are used for
training while the BMA areas for inference. With the structural information and
appearance feature from noisy image as references, our model can remove larger
BMA and produce better visualizing result. In addition, only 2D images with
defective masks are involved, hence improving the efficiency of our method.
Experiments on OCTA of mouse cortex demonstrate that our model can remove most
BMA with extremely large sizes and inconsistent intensities while previous
methods fail.
