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Measurement of X-ray Beam Coherence along Multiple Directions Using 2-D Checkerboard Phase Grating
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Autocorrelation-based generalized coherence factor for low-complexity adaptive beamforming.

Che-Chou Shen1, Yong-Qi Xing1, Gency Jeng2

  • 1Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.

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|August 28, 2016
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Summary
This summary is machine-generated.

A new autocorrelation-based algorithm for generalized coherence factor (GCF) estimation significantly reduces computational complexity and improves image quality in adaptive imaging applications.

Keywords:
Adaptive imagingAutocorrelationComputational complexityGeneralized coherence factor

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Area of Science:

  • Medical Imaging
  • Signal Processing

Background:

  • Generalized coherence factor (GCF) estimation suppresses sidelobe artifacts in channel data.
  • Conventional Fast Fourier Transform (FFT) methods for GCF estimation are computationally intensive.

Purpose of the Study:

  • To introduce an autocorrelation (AR)-based algorithm for GCF estimation.
  • To reduce the computational complexity of adaptive imaging.

Main Methods:

  • Autocorrelation estimates spectral parameters (mean frequency, bandwidth) from channel data.
  • A pseudo-spectrum is analytically computed for GCF weighting, with an optional bandwidth factor Q for optimization.

Main Results:

  • GCF computation complexity is reduced from O(Nlog2N) with FFT to O(N) with AR.
  • The GCF-AR method effectively suppresses lateral sidelobes, improving image contrast and reducing speckle variation.
  • Contrast-to-Noise Ratio (CNR) improved from 6.7 (GCF-FFT) to 9.0 (GCF-AR).

Conclusions:

  • The GCF-AR method offers reduced computational complexity and superior image quality in adaptive imaging.
  • This method enhances image contrast and is more resistant to artifacts near strong reflectors.