A Physics-Informed Diffusion Model for Super-Resolved Reconstruction of Optical Coherence Tomography Data.

Abstract

This study introduces a physics-informed diffusion model (PIDM) for super-resolution (SR) reconstruction of optical coherence tomography (OCT) data. An optimization framework was developed for maximizing the likelihood of observing an OCT image in the dataset, given the super-resolved reconstruction from a physics-informed diffusion model (PIDM) that reverses the degradations in OCT images. The image degradations were modeled as a serialization of three processes accounting for the effects of defocus, speckle noise, and digital sampling in OCT images. An analytical model for light-propagation model and a statistical model for speckle noise were derived based on the physical properties of the OCT setup. These models were then integrated with a diffusion model to reverse the degradations caused by defocus blur and digital sampling, minimizing susceptibility to noise and defocus-induced artifacts. The proposed method was employed for reconstructing images of a standard resolution target, plant tissue, and in vivo human cornea, using the complex OCT data acquired with a line-scan OCT (LS-OCT) system. The results from the PIDM exhibit improved sharpness and contrast compared to the images resulting from a few baseline methods such as standalone super-resolution using DM. Complementing DM with the physics of OCT could be a viable solution for obtaining high-fidelity SR reconstruction of OCT images. This work harnesses the power of diffusion models for super-resolution in OCT images. Such development could potentially enhance cellular-resolution OCT imaging of ophthalmic tissues, where high-fidelity images are crucial for accurate diagnosis.