Summary of Implicit Neural Representations For Speed-of-sound Estimation in Ultrasound, by Michal Byra et al.
Implicit Neural Representations for Speed-of-Sound Estimation in Ultrasound
by Michal Byra, Piotr Jarosik, Piotr Karwat, Ziemowit Klimonda, Marcin Lewandowski
First submitted to arxiv on: 21 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Medical Physics (physics.med-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to estimating the speed-of-sound in ultrasound image reconstruction techniques is proposed, utilizing implicit neural representations (INRs). These INRs encode continuous functions through network weights, potentially overcoming limitations of existing methods. Unlike convolutional networks that often fail when applied to real tissues due to out-of-distribution and data-shift issues, INRs are optimized for individual data cases, making them suitable for processing US data collected from varied tissues. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Speed-of-sound estimation in ultrasound is important for image reconstruction and tissue characterization. This paper introduces a new way to do this using “implicit neural representations”. These special networks can learn to estimate speed-of-sound by looking at individual pieces of data, which makes them good at handling different types of tissues. |
