  Deep learning (DL) methods typically require large datasets to effectively
learn data distributions. However, in the medical field, data is often limited
in quantity, and acquiring labeled data can be costly. To mitigate this data
scarcity, data augmentation techniques are commonly employed. Among these
techniques, generative models play a pivotal role in expanding datasets.
However, when it comes to ultrasound (US) imaging, the authenticity of
generated data often diminishes due to the oversight of ultrasound physics.
  We propose a novel approach to improve the quality of generated US images by
introducing a physics-based diffusion model that is specifically designed for
this image modality. The proposed model incorporates an US-specific scheduler
scheme that mimics the natural behavior of sound wave propagation in ultrasound
imaging. Our analysis demonstrates how the proposed method aids in modeling the
attenuation dynamics in US imaging. We present both qualitative and
quantitative results based on standard generative model metrics, showing that
our proposed method results in overall more plausible images. Our code is
available at https://github.com/marinadominguez/diffusion-for-us-images
