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Related Concept Videos

Standard Deviation01:10

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The most commonly used measure of variation is the standard deviation. It is a numerical value measuring how far data values are from their mean. The standard deviation value is small when the data are concentrated close to the mean, exhibiting slight variation or spread. The standard deviation value is never negative, it is either positive or zero. The standard deviation is larger when the data values are more spread out from the mean, which means the data values are exhibiting more variation.
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The standard deviation is the most common measure of variation. It is a value that tells us how far a data value is from the mean value in a dataset. Further, the standard deviation is always a positive value or zero.
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Dynamic coherence factor based on the standard deviation for coherent plane-wave compounding.

Yadan Wang1, Chichao Zheng1, Hu Peng1

  • 1Department of Biomedical Engineering, Hefei University of Technology, Hefei, 230009, China.

Computers in Biology and Medicine
|April 22, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces advanced ultrasound imaging techniques, including dynamic coherence factor (DCF) and its variations (ADCF, SN-ADCF), to enhance image quality in plane wave transmission imaging. SN-ADCF offers a superior balance of resolution, contrast, and speckle quality.

Keywords:
Coherence factorCoherent plane-wave compoundingDynamic coherence factorSquare neighborhoodStandard deviationUltrasound imaging

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Area of Science:

  • Medical Ultrasound Imaging
  • Medical Physics
  • Biomedical Engineering

Background:

  • Ultrafast imaging using plane wave transmission is a key technique in medical ultrasound.
  • Coherent plane-wave compounding (CPWC) improves image quality by combining multiple plane wave images.
  • Coherence factor (CF) weighting enhances contrast but can introduce artifacts and degrade speckle quality.

Purpose of the Study:

  • To develop novel methods to overcome limitations of existing coherence factor weighting algorithms in plane wave imaging.
  • To improve image contrast, resolution, and speckle quality in ultrasound imaging.
  • To introduce dynamic coherence factor (DCF), adjusted DCF (ADCF), and square neighborhood ADCF (SN-ADCF) for enhanced ultrasound image reconstruction.

Main Methods:

  • Proposed dynamic coherence factor (DCF) based on angular differences in CPWC to adaptively determine plane wave numbers.
  • Introduced adjusted DCF (ADCF) with a standard deviation parameter to improve resolution and contrast-to-noise ratio (CNR).
  • Developed square neighborhood ADCF (SN-ADCF) using a 2D average filter for enhanced contrast and speckle quality, evaluated on simulated, experimental, and in-vivo data.

Main Results:

  • DCF demonstrated improved contrast ratio (CR), CNR, and speckle signal-to-noise ratio (sSNR) compared to CF.
  • ADCF achieved up to 40% improvement in lateral full width at half maximum (FWHM) and superior speckle quality over CF.
  • SN-ADCF showed approximately 20% improvement in CNR and sSNR over CF, with comparable resolution, and outperformed GCF and scCF in lateral resolution.

Conclusions:

  • SN-ADCF offers a balanced performance, achieving a satisfactory trade-off between resolution, contrast, and speckle quality in plane wave ultrasound imaging.
  • The proposed DCF, ADCF, and SN-ADCF methods represent significant advancements in ultrasound image processing.
  • SN-ADCF provides superior lateral resolution and comparable contrast/speckle quality compared to other advanced methods like GCF and scCF.