High Fidelity Ultrasound Video Synthesis

High-fidelity ultrasound video synthesis aims to generate realistic ultrasound videos for applications like training sonographers and augmenting datasets for machine learning. Current research heavily utilizes diffusion models, often incorporating physics-based constraints to improve image realism and anatomical accuracy, alongside other architectures like neural radiance fields (NeRFs) and generative adversarial networks (GANs). This work is significant because it addresses the limitations of scarce and expensive-to-acquire real ultrasound data, potentially improving diagnostic accuracy and training efficiency in medical imaging.