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Diffusion as sound propagation: physics-inspired model for ultrasound image generation

Domínguez, M ; Sharif University of Technology | 2024

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  1. Type of Document: Article
  2. DOI: 10.1007/978-3-031-72083-3_57
  3. Publisher: Springer , 2024
  4. Abstract:
  5. Deep learning (DL) methods typically require large datasets to effectively learn data distributions. However, in the medical field, data is often limited in quantity, and acquiring labeled data can be costly. To mitigate this data scarcity, data augmentation techniques are commonly employed. Among these techniques, generative models play a pivotal role in expanding datasets. However, when it comes to ultrasound (US) imaging, the authenticity of generated data often diminishes due to the oversight of ultrasound physics. We propose a novel approach to improve the quality of generated US images by introducing a physics-based diffusion model that is specifically designed for this image modality. The proposed model incorporates an US-specific scheduler scheme that mimics the natural behavior of sound wave propagation in ultrasound imaging. Our analysis demonstrates how the proposed method aids in modeling the attenuation dynamics in US imaging. We present both qualitative and quantitative results based on standard generative model metrics, showing that our proposed method results in overall more plausible images. Our code is available at github.com/marinadominguez/diffusion-for-us-images. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024
  6. Keywords:
  7. Diffusion Models ; Synthetic Image Generation ; Ultrasound ; Deep learning ; Image coding ; Image enhancement ; Large datasets ; Network security ; Ultrasonic propagation ; Diffusion model ; Generative model ; Image generations ; Large datasets
  8. Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; Volume 15004 LNCS , 2024 , Pages 613-623 ; 03029743 (ISSN); 978-303172082-6 (ISBN)
  9. URL: https://link.springer.com/chapter/10.1007/978-3-031-72083-3_57