OCTDiff: Bridged Diffusion Model for Portable OCT Super-Resolution and Enhancement
Ye Tian, Angela McCarthy, Gabriel Gomide, Nancy Liddle, Jedrzej Golebka, Royce Chen, Jeff Liebmann, Kaveri Thakoor
Medical imaging super-resolution is critical for improving diagnostic utility and reducing costs, particularly for low-cost modalities such as portable Optical Coherence Tomography (OCT). We propose OCTDiff, a Bridged Diffusion Model designed to enhance image resolution and quality from portable OCT devices. Our image-to-image diffusion framework addresses key challenges in the conditional generation process of denoising diffusion probabilistic models (DDPMs). A novel component, Adaptive Noise Aggregation (ANA), is introduced to improve denoising dynamics within the reverse diffusion. Additionally, we integrate Multi-Scale Cross-Attention (MSCA) into the U-Net backbone to capture local dependencies across spatial resolutions. To address overfitting on small clinical datasets and to preserve fine structural details essential for retinal diagnostics, we design a customized loss function guided by clinical quality scores. OCTDiff outperforms convolutional baselines and standard DDPMs, achieving state-of-the-art performance on clinical portable OCT datasets. Our model and its downstream applications can potentially be generalizable to other medical imaging modalities and revolutionize the current workflow of ophthalmic diagnostics. The code will be made available upon acceptance.