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Related Experiment Video

Updated: Jun 25, 2025

A Coregistered Ultrasound and Photoacoustic Imaging Protocol for the Transvaginal Imaging of Ovarian Lesions
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Optimization of array encoding for ultrasound imaging.

Jacob Spainhour1, Korben Smart2, Stephen Becker1

  • 1Department of Applied Mathematics, University of Colorado Boulder, Boulder, CO, United States of America.

Physics in Medicine and Biology
|May 30, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning optimizes ultrasound imaging sequences for better resolution and contrast. This approach explores novel scanning patterns beyond conventional methods, enhancing B-mode image quality.

Keywords:
image qualitymachine learningnumerical optimizationspatial encodingsynthetic aperture

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Area of Science:

  • Ultrasound imaging
  • Medical imaging physics
  • Machine learning applications

Background:

  • The transmit encoding model is crucial for understanding acoustic transmission effects in synthetic aperture imaging.
  • Current scanning sequences represent a limited subset of possibilities for ultrasound image reconstruction.

Purpose of the Study:

  • To utilize machine learning (ML) to develop optimized scanning sequences for high-quality B-mode ultrasound images.
  • To explore novel encoding sequences beyond conventional methods in synthetic aperture imaging.

Main Methods:

  • A custom ML model in PyTorch was developed using simulated radiofrequency (RF) data from Field II.
  • The model probes encoding sequences (time delays, apodization weights) to minimize an image quality loss function.
  • A novel derivative formulation for delay-and-sum beamforming enabled computational feasibility.

Main Results:

  • ML-optimized encoding sequences, when used with the REFoCUS imaging framework, significantly improved resolution, field of view, and contrast.
  • Experimental validation on wire targets and a tissue-mimicking phantom confirmed the enhanced image quality metrics.
  • The ML approach demonstrated superior performance compared to conventional scanning sequences.

Conclusions:

  • Machine learning can discover and optimize novel scanning sequences for synthetic transmit aperture imaging.
  • Integrating beamforming within the ML model is valuable for synthetic transmit aperture imaging tasks.
  • This work expands the understanding of available encoding schemes, moving beyond narrow, commonly used subsets.