Self-supervised Denoising and Bulk Motion Artifact Removal of 3D Optical Coherence Tomography Angiography of Awake Brain

Denoising of 3D Optical Coherence Tomography Angiography (OCTA) for awake brain microvasculature is challenging. An OCTA volume is scanned slice by slice, with each slice (named B-scan) derived from dynamic changes in successively acquired OCT images. A B-scan of an awake brain often suffers from complex noise and Bulk Motion Artifacts (BMA), severely degrading image quality. Also, acquiring clean B-scans for training is difficult. Fortunately, we observe that, the slicewise imaging procedure makes the noises mostly independent across Bscans, while preserving the continuity of vessel (including capillaries) signals across B-scans. Thus inspired, we propose a novel blind-slice selfsupervised learning method to denoise 3D brain OCTA volumes slice by slice. For each B-scan slice, named center B-scan, we mask it entirely black and train the network to recover the original center B-scan using its neighboring B-scans. To enhance the BMA removal performance, we adaptively select only BMA-free center B-scans for model training. We further propose two novel refinement methods: (1) a non-local block to enhance vessel continuity and (2) a weighted loss to improve vascular contrast. To the best of our knowledge, this is the first self-supervised 3D OCTA denoising method that effectively reduces both complex noise and BMA while preserving capillary signals in brain OCTA volumes. Code is available at https://github.com/ZhenghLi/SOAD.