Optical Coherence Tomography Harmonization via Dual Diffusion Implicit Bridges

Image harmonization (IH) makes images from different domains consistent, enabling multi-domain comparison with reliable quantitative measurements. Optical coherence tomography (OCT) could benefit from IH, as variations in OCT systems cause significant discrepancies in image quality due to speckle and noise. However, IH approaches typically rely on paired images during the training phase, and such images are not available in OCT data due to sparse image acquisition. A Schrödinger bridge (SB) finds an optimal coupling between arbitrary probability spaces with respect to a reference path measure and can be applied to unpaired IH. SBs are the core of many generative diffusion models, when one of the probability spaces is easy to sample from (i.e., Gaussian). SBs have been applied to natural images for image-to-image translation; however, they have not been applied to OCT IH because a SB does not guarantee anatomical consistency. In this paper, we use a dual diffusion implicit bridge (DDIB) for OCT IH, which finds independent SBs between arbitrary domains and a common Gaussian probability space. We generalize the assumption of DDIB and use segmentation masks as a condition to improve anatomical consistency. We conduct DDIB experiments with and without segmentation masks as a condition and analyze performance in terms of anatomical consistency and harmonization quality. The method does not require any paired training data and can in principle be quickly adapted to new domains.