E5520. Using AI to Denoise Ultrasound: Current State of Development
Authors
Daniel Jung;
No Affiliation
Eric Errampalli;
No Affiliation
Sriram Paravastu;
No Affiliation
Zachary Gaughan;
No Affiliation
Background
Ultrasound imaging is a widely used diagnostic tool in medicine, providing real-time imaging of organs and tissues in an efficient, noninvasive manner. However, ultrasound images contain a lot of noise, which can affect the diagnostic performance. The use of artificial intelligence (AI) to denoise ultrasound images has shown potential to improve the visualization of ultrasound images. We conducted a literature review of the existing research on the use of AI for denoising ultrasound images.
Educational Goals / Teaching Points
Ultrasound images contain a lot of noise, which can affect the diagnostic performance. AI-based methods to denoise ultrasound have been tested and have shown promising results. Deep learning-based methods, such as convolutional neural networks (CNNs), demonstrate the capability to denoise ultrasound images, while retaining characteristic features-related information. Highlight the difference between deep learning-based methods and other methods of denoising ultrasound. AI-based techniques allow for effective noise removal, while preserving important image details. The use of AI to denoise ultrasound images has shown potential to improve the visualization of ultrasound images. The use of AI-based denoising techniques for ultrasound imaging is still a developing field and needs further research and testing.
Key Anatomic/Physiologic Issues and Imaging Findings/Techniques
AI-based methods to denoise ultrasound has shown promising results. Deep learning-based methods, such as CNNs tuned for feature retention, have been used to denoise ultrasound images. These techniques allow for effective noise removal while preserving important image details.
Conclusion
The use of AI-based denoising techniques for ultrasound imaging is still a developing field. Preliminary studies have demonstrated its efficacy in effectively removing noise without losing important feature details. Further studies are needed to evaluate the enhanced diagnostic capabilities of the denoising algorithm, its performance in different regions of the body, and the efficacy of AI-based denoising techniques.
