Echocardiography has been a prominent tool for the diagnosis of cardiac
disease. However, these diagnoses can be heavily impeded by poor image quality.
Acoustic clutter emerges due to multipath reflections imposed by layers of
skin, subcutaneous fat, and intercostal muscle between the transducer and
heart. As a result, haze and other noise artifacts pose a real challenge to
cardiac ultrasound imaging. In many cases, especially with difficult-to-image
patients such as patients with obesity, a diagnosis from B-Mode ultrasound
imaging is effectively rendered unusable, forcing sonographers to resort to
contrast-enhanced ultrasound examinations or refer patients to other imaging
modalities. Tissue harmonic imaging has been a popular approach to combat haze,
but in severe cases is still heavily impacted by haze. Alternatively, denoising
algorithms are typically unable to remove highly structured and correlated
noise, such as haze. It remains a challenge to accurately describe the
statistical properties of structured haze, and develop an inference method to
subsequently remove it. Diffusion models have emerged as powerful generative
models and have shown their effectiveness in a variety of inverse problems. In
this work, we present a joint posterior sampling framework that combines two
separate diffusion models to model the distribution of both clean ultrasound
and haze in an unsupervised manner. Furthermore, we demonstrate techniques for
effectively training diffusion models on radio-frequency ultrasound data and
highlight the advantages over image data. Experiments on both \emph{in-vitro}
and \emph{in-vivo} cardiac datasets show that the proposed dehazing method
effectively removes haze while preserving signals from weakly reflected tissue.Comment: 10 pages, 11 figures, preprint IEEE submissio