Reflection-mode transmission matrix reconstruction using neural networks in optical fiber imaging systems

Abstract

Needle-thin optical fibre imaging systems using multimode fibre show considerable potential for facilitating advanced medical endoscopes that can capture high-resolution images in challenging regions of the body, such as the brain or blood vessels. However, these systems experience significant optical distortion whenever the fibre is disturbed. To address this, it is crucial to calibrate the fibre transmission matrix (TM) in vivo immediately before conducting the imaging process since TM is highly sensitive to temperature variations and bending. We therefore present a reflection-mode TM reconstruction model using U-net based convolutional neural networks with a custom loss function used for arbitrary global phase compensation, which reduced computational time to ~1s. We demonstrated this model by reconstructing 64 × 64 complex-valued fibre TMs through a reflection-mode optical fibre system and tested by reconstructing widefield images with ≤ 9% image error. We anticipate this neural network-based TM reconstruction model with the custom loss function designed will lead to new AI models that deal with phase information, for example in imaging through optical fibre, holographic imaging and projection, where both phase control and speed are required.