Deep Ultrasound Denoising Using Diffusion Probabilistic Models
Hojat Asgariandehkordi Sobhan Goudarzi Adrian Basarab Hassan Rivaz
Ultrasound images are widespread in medical diagnosis for musculoskeletal,
cardiac, and obstetrical imaging due to the efficiency and non-invasiveness of
the acquisition methodology. However, the acquired images are degraded by
acoustic (e.g. reverberation and clutter) and electronic sources of noise. To
improve the Peak Signal to Noise Ratio (PSNR) of the images, previous denoising
methods often remove the speckles, which could be informative for radiologists
and also for quantitative ultrasound. Herein, a method based on the recent
Denoising Diffusion Probabilistic Models (DDPM) is proposed. It iteratively
enhances the image quality by eliminating the noise while preserving the
speckle texture. It is worth noting that the proposed method is trained in a
completely unsupervised manner, and no annotated data is required. The
experimental blind test results show that our method outperforms the previous
nonlocal means denoising methods in terms of PSNR and Generalized Contrast to
Noise Ratio (GCNR) while preserving speckles.