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Updated: Jan 15, 2026
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Evaluating Targeting Accuracy in the Focal Plane for an Ultrasound-guided High-intensity Focused Ultrasound Phased-array System
Published on: March 6, 2019
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Optimized Apodizations with Training Simulations (OATS): Learned depth-dependent apodizations via differentiable
Di Xiao1, Hassan Nahas1, Misaki Hiroshima2
1Schlegel-UW Research Institute for Aging, University of Waterloo, Waterloo, Canada.
Ultrasonics
|October 9, 2025
Summary
A new AI framework, Optimal Apodizations with Training Simulations (OATS), improves ultrasound image quality by learning optimal apodization weights. This reduces operator dependency and enhances diagnostic accuracy in medical imaging.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Ultrasound Technology
Background:
- Ultrasound is a crucial real-time, point-of-care imaging tool.
- Operator dependency in ultrasound imaging can affect image quality due to manual setting adjustments.
- Improving B-mode image quality can minimize operator dependency.
Purpose of the Study:
- To introduce a supervised learning framework (Optimal Apodizations with Training Simulations - OATS) for enhancing ultrasound image quality.
- To develop novel apodization weights that reduce operator dependency and improve image fidelity.
- To validate the effectiveness of OATS-generated apodizations on both simulated and real-world ultrasound data.
Main Methods:
- Utilized a differentiable beamformer within a supervised learning framework to optimize apodization weights.
- Trained the framework on a simulated dataset of over 200 images, comparing simulated ground truth with post-beamformed images.
- Experimentally verified the performance of OATS-derived apodization weights in focused and unfocused ultrasound imaging scenarios.
Main Results:
- OATS-apodized images showed reduced sidelobe artifacts and improved lateral resolution by 11% in focused imaging compared to Hanning apodization.
- In unfocused imaging, OATS demonstrated reduced sidelobe artifacts and enhanced tissue-to-lesion contrast by up to 13 dB.
- Learned apodization weights were physically interpretable, emulating parameters like time-gain compensation and focal adjustments.
Conclusions:
- The OATS framework successfully generates generalizable receive apodizations for significant ultrasound image quality improvement.
- This AI-driven approach effectively reduces operator dependency in ultrasound imaging.
- OATS offers a promising method for enhancing diagnostic accuracy through superior image quality in medical ultrasound.
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