A physics-based noise formation model for optical coherence tomography system denoising

Optical coherence tomography (OCT) as an interferometric imaging technique, suffers from massive noise. Denoising methods are applied essentially to improve image quality in OCT community. The conventional methods rely on post image processing algorithms such as non-local mean filtering, block-matching and 3D filtering algorithm. However, these conventional noise reduction methods could inevitably cause the destruction of image details, reduce the contrast at the edge of OCT images, and result in a degeneration of image quality. Current deep learning methods often ignore the specificity of system, therefore haven't taken advantages of the unique characteristics of different systems. In this work, we present a deep learning noise reduction method using the network architecture trained from synthetic OCT signals with random noise that are generated from the noise formation model characterized by our custom-built specific SDOCT (Spectrum-Domain optical coherence tomography) system. We analyze the signal formation process and the noise generation pathway of our system, thereby enabling the construction of a noise formation model. DN-Unet (Denoising Unity Network) is applied to train the datasets generated by our proposed noise formation model and the multi-to-single strategy is developed to enhance the network capability. Preliminary empirical results collectively show that the network can reach an average of 25 dB signal to noise ratio (SNR) improvement while preserving detail structures, which demonstrates the effectiveness of our noise reduction method. This method has the potential to be adopted by other systems without the need for large number of golden-standard image generation.