Ultrasound Imaging based on the Variance of a Diffusion Restoration
  Model
Yuxin Zhang Clément Huneau Jérôme Idier Diana Mateus
Despite today's prevalence of ultrasound imaging in medicine, ultrasound
signal-to-noise ratio is still affected by several sources of noise and
artefacts. Moreover, enhancing ultrasound image quality involves balancing
concurrent factors like contrast, resolution, and speckle preservation.
Recently, there has been progress in both model-based and learning-based
approaches addressing the problem of ultrasound image reconstruction. Bringing
the best from both worlds, we propose a hybrid reconstruction method combining
an ultrasound linear direct model with a learning-based prior coming from a
generative Denoising Diffusion model. More specifically, we rely on the
unsupervised fine-tuning of a pre-trained Denoising Diffusion Restoration Model
(DDRM). Given the nature of multiplicative noise inherent to ultrasound, this
paper proposes an empirical model to characterize the stochasticity of
diffusion reconstruction of ultrasound images, and shows the interest of its
variance as an echogenicity map estimator. We conduct experiments on synthetic,
in-vitro, and in-vivo data, demonstrating the efficacy of our variance imaging
approach in achieving high-quality image reconstructions from single plane-wave
acquisitions and in comparison to state-of-the-art methods.