Structure-Aware Diffusion Image Outpainting for Echocardiographic Field-of-View Extension

Transthoracic Echocardiography (TTE) often suffers from a limited field of view (FoV), which may obscure peripheral cardiac structures and hinder comprehensive visual assessment. Although FoV extension can be formulated as outpainting, public studies on echocardiographic FoV outpainting remain scarce and are mainly represented by cGAN-based reconstruction methods such as echoGAN. However, in noisy ultrasound images with weak boundaries, the challenge is not only realistic texture synthesis, but also structural continuity across the observed–generated boundary. To address this issue, we propose a structure-aware diffusion framework for echocardiographic FoV outpainting. To the best of our knowledge, this is the first work to introduce diffusion models into this task, moving beyond the existing cGAN-based formulation. Our framework combines a diffusion baseline, a Structural Cue Encoder (SCE) for structure-sensitive conditioning, and Structure-aware Diffusion Learning (SDL) for structural regularization during denoising. Experiments show clear and consistent improvements over cGAN-based reconstruction, producing more coherent structures, smoother transitions, and better FoV extension quality.