Deep OCT Angiography Image Generation for Motion Artifact Suppression
This addresses motion artifact suppression in medical imaging for ophthalmology, but it is incremental as it builds on existing U-Net and generative methods.
The researchers tackled motion artifacts in OCT angiography images by developing a deep generative model that uses a single intact OCT scan to fill gaps, showing that generative models can augment missing scans for improved diagnostic use.
Eye movements, blinking and other motion during the acquisition of optical coherence tomography (OCT) can lead to artifacts, when processed to OCT angiography (OCTA) images. Affected scans emerge as high intensity (white) or missing (black) regions, resulting in lost information. The aim of this research is to fill these gaps using a deep generative model for OCT to OCTA image translation relying on a single intact OCT scan. Therefore, a U-Net is trained to extract the angiographic information from OCT patches. At inference, a detection algorithm finds outlier OCTA scans based on their surroundings, which are then replaced by the trained network. We show that generative models can augment the missing scans. The augmented volumes could then be used for 3-D segmentation or increase the diagnostic value.