TSAR: A two-stage approach to motion artifact reduction in OCTA images

Optical Coherence Tomography Angiography (OCTA) is an innovative and non-invasive imaging technique that leverages motion contrast imaging to generate angiographic images from high-resolution volumetric blood flow data rapidly. However, OCTA imaging is vulnerable to various artifacts induced by eye movements, including displacement artifacts, duplicated scanning artifacts, and white line artifacts. Previous methods that attempted to mitigate eye motion artifacts necessitated costly hardware upgrades. However, despite the availability of advanced eye-tracking hardware and software correction in commercial machines, motion artifacts persist in real-world usage. Recently developed cost-effective learning-based methods only focus on the removal of white line artifacts while neglecting the displacement artifacts and duplicated scanning artifacts. To address this challenge, we propose a comprehensive framework, TSAR, to remove three types of eye motion artifacts in OCTA images. In the first stage, we leverage the intrinsic axial and directional attributes of these artifacts in the first phase to develop an innovative hierarchical transformer network. This network is designed to capture global-wise, local-wise, and vertical-wise features effectively while also removing displacement and duplicate scanning artifacts. Afterward, we leverage the contextual information and develop a residual conditional diffusion model (RCDM) to remove the white line artifacts. By applying our TSAR to the degraded OCTA images, we aim to eliminate all three types of motion artifacts. We evaluate the superior performance of our proposed methodology in artifact removal and image quality enhancement compared to other methods by conducting experiments on both synthetic and real-world OCTA images. The code is available at https://github.com/btma48/TSAR