Dehazing Ultrasound using Diffusion Models

Tristan S.W. Stevens (Corresponding author), Faik C. Meral, Jason Yu, Iason Z. Apostolakis, Jean-Luc Robert, Ruud J.G. van Sloun

Research output: Contribution to journalArticleAcademicpeer-review

7 Citations (Scopus)

Abstract

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 in-vitro and in-vivo cardiac datasets show that the proposed dehazing method effectively removes haze while preserving signals from weakly reflected tissue.
Original languageEnglish
Article number10423849
Pages (from-to)3546-3558
Number of pages13
JournalIEEE Transactions on Medical Imaging
Volume43
Issue number10
Early online date7 Feb 2024
DOIs
Publication statusPublished - Oct 2024

Funding

This work was performed within the IMPULSE framework of the Eindhoven MedTech Innovation Center (e/MTIC, incorporating Eindhoven University of Technology and Philips Research), including a PPS supplement from the Dutch Ministry of Economic Affairs and Climate Policy.

Keywords

  • Ultrasonic imaging
  • Clutter
  • Biological system modeling
  • Radio frequency
  • Harmonic analysis
  • Noise reduction
  • Image quality
  • Ultrasound
  • dehazing
  • cardiovascular
  • diffusion models
  • deep generative prior
  • posterior sampling
  • Humans
  • Heart/diagnostic imaging
  • Algorithms
  • Echocardiography/methods
  • Image Processing, Computer-Assisted/methods

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