bibtext: @InProceedings{ Li_SelfsupervisedDenoising_MICCAI2024,
                   author = { Li, Zhenghong and Ren, Jiaxiang and Zou, Zhilin and Garigapati, Kalyan and Du, Congwu and Pan, Yingtian and Ling, Haibin },
                   title = { { Self-supervised Denoising and Bulk Motion Artifact Removal of 3D Optical Coherence Tomography Angiography of Awake Brain } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15011 },
                   month = {October},
                   pages = { pending },
                }
abstract: 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 slice-wise imaging procedure makes the noises mostly independent across B-scans, while preserves the continuity of vessel (including capillaries) signals across B-scans. Thus inspired, we propose a novel blind-slice self-supervised 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.
title: Self-supervised Denoising and Bulk Motion Artifact Removal of 3D Optical Coherence Tomography Angiography of Awake Brain
authors: Li, Zhenghong Ren, Jiaxiang Zou, Zhilin Garigapati, Kalyan Du, Congwu Pan, Yingtian Ling, Haibin