• English
  • Deutsch
  • Log In
    Password Login
    Research Outputs
    Fundings & Projects
    Researchers
    Institutes
    Statistics
Repository logo
Fraunhofer-Gesellschaft
  1. Home
  2. Fraunhofer-Gesellschaft
  3. Artikel
  4. Learning optimal spatial subsampling for single-channel ultrasound imaging
 
  • Details
  • Full
Options
2024
Journal Article
Title

Learning optimal spatial subsampling for single-channel ultrasound imaging

Abstract
Traditional ultrasound synthetic aperture imaging relies on closely spaced measurement positions, where the pitch size is smaller than half the ultrasound wavelength. While this approach achieves high-quality images, it necessitates the storage of large data sets and an extended measurement time. To address these issues, there is a burgeoning interest in exploring effective subsampling techniques. Recently, Deep Probabilistic Subsampling (DPS) has emerged as a feasible approach for designing selection matrices for multi-channel systems. In this paper, we address spatial subsampling in single-channel ultrasound imaging for Nondestructive Testing (NDT) applications. To accomplish a modelbased data-driven spatial subsampling approach within the DPS framework that allows for the optimal selection of sensing positions on a discretized grid, it is crucial to build an adequate signal model and design an adapted network architecture with a reasonable cost function. The reconstructed image quality is then evaluated through simulations, showing that the presented subsampling pattern approaches the performance of fully sampling and substantially outperforms uniformly spatial subsampling in terms of signal recovery quality.
Author(s)
Wang, Han
Fraunhofer-Institut für Zerstörungsfreie Prüfverfahren IZFP  
Perez Mejia, Eduardo Jose
Fraunhofer-Institut für Zerstörungsfreie Prüfverfahren IZFP  
Römer, Florian  
Fraunhofer-Institut für Zerstörungsfreie Prüfverfahren IZFP  
Journal
Science talks  
Open Access
File(s)
Download (199.19 KB)
Rights
CC BY 4.0: Creative Commons Attribution
DOI
10.1016/j.sctalk.2024.100340
10.24406/publica-2956
Language
English
Fraunhofer-Institut für Zerstörungsfreie Prüfverfahren IZFP  
Keyword(s)
  • Machine learning for signal processing

  • Signal subsampling

  • Sparse signal recovery

  • Ultrasound nondestructive testing

  • Cookie settings
  • Imprint
  • Privacy policy
  • Api
  • Contact
© 2024