UGDT-SR: Uncertainty-Guided Diffusion Transformer for Ultrasound Image Super-Resolution

Super-resolution plays a crucial role in enhancing the diagnostic utility of ultrasound images by recovering fine anatomical structures. Due to their remarkable performance in natural image restoration and generation, diffusion probabilistic models have become attractive for ultrasound image superresolution that highly requires complex textures and structural details modelling. However, existing diffusion-based superresolution methods typically apply uniform noise perturbations and reconstruction strategies across the entire image. Such global treatment neglects the varying reconstruction difficulty and clinical importance of different regions, often resulting in blurry lesion boundaries and suboptimal recovery of diagnostically significant structures. To address this issue, we propose UGDT-SR, a structure-aware ultrasound image super-resolution framework that integrates Bayesian uncertainty modeling and Transformer-guided structural representation into the diffusion process. Specifically, we introduce a Bayesian uncertainty network to estimate pixel-wise reconstruction difficulty, generating structural uncertainty masks that adaptively modulate local noise injection during forward diffusion. Furthermore, we inject these uncertainty masks into the conditional encoder to guide the model's attention toward clinically relevant regions throughout the reconstruction. Extensive experiments on both public and clinical ultrasound datasets demonstrate that UGDT-SR consistently outperformed CNN-, GAN-, and diffusion-based baselines, achieving superior perceptual quality and structural fidelity, especially in lesion areas.