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Walsh function current patterns and data synthesis for electrical impedance tomography.

E J Woo1, P Hua, J G Webster

  • 1Dept. of Biomed. Eng., Kon Kuk Univ., Choongbuk.

IEEE Transactions on Medical Imaging
|January 1, 1992
PubMed
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This study introduces a novel data collection method using Walsh functions for injection currents. This approach simplifies hardware and enhances data quality for improved subject interior imaging.

Area of Science:

  • Electrical Impedance Tomography
  • Signal Processing

Background:

  • Current data collection methods in impedance imaging can be complex or yield suboptimal data.
  • Existing methods like sinusoidal injection require sophisticated hardware, while pulse-based methods offer limited information.

Purpose of the Study:

  • To present a new data collection method utilizing Walsh functions as injection current patterns.
  • To demonstrate the method's ability to meet optimality and single-time measurement conditions.
  • To highlight the advantages of Walsh functions over traditional injection patterns.

Main Methods:

  • Employing Walsh functions, which require only two current levels (+1 and -1), for injection current patterns.
  • Comparing Walsh function injection with diagonal/neighboring pulse patterns and sinusoidal injection.

Related Experiment Videos

  • Analyzing the spatial frequency simulation capabilities of Walsh functions.
  • Main Results:

    • Walsh function injection simplifies current source design compared to sinusoidal methods.
    • This method provides richer information about the subject's interior by simulating low and high spatial frequencies.
    • It achieves better signal-to-noise ratio (SNR) and distinguishability than pulse methods, comparable to sinusoidal methods.

    Conclusions:

    • Walsh function injection offers a simplified yet effective approach for data collection in impedance imaging.
    • The method balances the ease of pulse injection with the superior data quality of sinusoidal injection.
    • This technique enhances the distinguishability and SNR, leading to better interior imaging.