3D-Unet deep learning algorithm for denoising OCTA volumes acquired at MHz A-scan rates

Increasing the FOV of OCTA images while keeping the acquisition time moderate requires high A-scan rates. Therefore, OCTA images appear to be noisier. Deep learning methods can be used for noise reduction. In OCTA volumes small vessels with an orientation perpendicular to the image plane are often removed by deep learning denoising algorithms, due to their small appearance. To overcome this a 3-dimensional Unet was developed to utilize volumetric information. With the knowledge of also the third dimension, the algorithm is able to distinguish between noise and vessel contrast and is therefore less likely to remove vessels.