Optimization of array encoding for ultrasound imaging.

Item request has been placed! ×
Item request cannot be made. ×
loading   Processing Request
Read More Add to Saved list
  • Additional Information
    • Source:
      Publisher: IOP Publishing Country of Publication: England NLM ID: 0401220 Publication Model: Electronic Cited Medium: Internet ISSN: 1361-6560 (Electronic) Linking ISSN: 00319155 NLM ISO Abbreviation: Phys Med Biol Subsets: MEDLINE
    • Publication Information:
      Original Publication: Bristol : IOP Publishing
    • Subject Terms:
      Ultrasonography*/instrumentation ; Ultrasonography*/methods ; Phantoms, Imaging* ; Image Processing, Computer-Assisted*/methods; Machine Learning
    • Abstract:
      Objective . The transmit encoding model for synthetic aperture imaging is a robust and flexible framework for understanding the effects of acoustic transmission on ultrasound image reconstruction. Our objective is to use machine learning (ML) to construct scanning sequences, parameterized by time delays and apodization weights, that produce high-quality B-mode images. Approach . We use a custom ML model in PyTorch with simulated RF data from Field II to probe the space of possible encoding sequences for those that minimize a loss function that describes image quality. This approach is made computationally feasible by a novel formulation of the derivative for delay-and-sum beamforming. Main results . When trained for a specified experimental setting (imaging domain, hardware restrictions, etc), our ML model produces optimized encoding sequences that, when deployed in the REFoCUS imaging framework, improve a number of standard quality metrics over conventional sequences including resolution, field of view, and contrast. We demonstrate these results experimentally on both wire targets and a tissue-mimicking phantom. Significance . This work demonstrates that the set of commonly used encoding schemes represent only a narrow subset of those available. Additionally, it demonstrates the value for ML tasks in synthetic transmit aperture imaging to consider the beamformer within the model, instead of purely as a post-processing step.
      (© 2024 Institute of Physics and Engineering in Medicine.)
    • Contributed Indexing:
      Keywords: image quality; machine learning; numerical optimization; spatial encoding; synthetic aperture
    • Publication Date:
      Date Created: 20240530 Date Completed: 20240620 Latest Revision: 20240820
    • Publication Date:
      20240820
    • Accession Number:
      10.1088/1361-6560/ad5249
    • Accession Number:
      38815603