Diffusion Reconstruction of Ultrasound Images with Informative
  Uncertainty
Yuxin Zhang Clément Huneau Jérôme Idier Diana Mateus
Despite its wide use in medicine, ultrasound imaging faces several challenges
related to its poor signal-to-noise ratio and several sources of noise and
artefacts. Enhancing ultrasound image quality involves balancing concurrent
factors like contrast, resolution, and speckle preservation. In recent years,
there has been progress both in model-based and learning-based approaches to
improve ultrasound image reconstruction. Bringing the best from both worlds, we
propose a hybrid approach leveraging advances in diffusion models. To this end,
we adapt Denoising Diffusion Restoration Models (DDRM) to incorporate
ultrasound physics through a linear direct model and an unsupervised
fine-tuning of the prior diffusion model. We conduct comprehensive experiments
on simulated, in-vitro, and in-vivo data, demonstrating the efficacy of our
approach in achieving high-quality image reconstructions from a single plane
wave input and in comparison to state-of-the-art methods. Finally, given the
stochastic nature of the method, we analyse in depth the statistical properties
of single and multiple-sample reconstructions, experimentally show the
informativeness of their variance, and provide an empirical model relating this
behaviour to speckle noise. The code and data are available at: (upon
acceptance).