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Alperen Degirmenci1, Robert D Howe1, Douglas P Perrin2,3

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Gaussian process regression improves ultrasound image quality by accurately interpolating scanline data. This method enhances brightness-mode image generation and offers uncertainty estimates, with optimized windowing reducing computational cost.

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Area of Science:

  • Medical imaging
  • Ultrasound technology
  • Signal processing

Background:

  • Scanline interpolation is crucial for converting ultrasound scanline data into brightness-mode (B-mode) images.
  • Conventional methods like bilinear interpolation have limitations in capturing spatial data dependencies, leading to inaccuracies.
  • Deviations from the underlying probability distribution occur at interpolation points with standard methods.

Purpose of the Study:

  • To introduce Gaussian process (GP) regression as an advanced technique for ultrasound scanline interpolation.
  • To compare the accuracy of GP regression against traditional interpolation methods (nearest neighbor, bilinear, cubic spline).
  • To evaluate the impact of interpolation window size on GP regression's accuracy and computational efficiency.

Main Methods:

  • Utilized ultrasound scanlines from in vivo trials using two different ultrasound scanners.
  • Applied Gaussian process regression and compared it with nearest neighbor, bilinear, and cubic spline interpolation.
  • Quantified scanline conversion accuracy using Peak Signal-to-Noise Ratio (PSNR) and Mean Absolute Error (MAE).
  • Investigated the effect of limiting the GP regression interpolation window size.

Main Results:

  • Gaussian process regression demonstrated superior scanline conversion accuracy compared to all tested conventional methods for both datasets.
  • PSNR and MAE scores confirmed the enhanced performance of GP regression.
  • Reducing the GP regression interpolation window size to 15 minimized computational time with negligible impact on PSNR.

Conclusions:

  • Gaussian process regression provides a quantitatively more accurate method for ultrasound scanline conversion.
  • GP regression offers valuable uncertainty estimates at each interpolation point.
  • The proposed windowing approach effectively reduces the computational cost associated with GP regression in scanline conversion.