Alperen Degirmenci1, Robert D Howe1, Douglas P Perrin2,3. 1. Harvard University, John A. Paulson School of Engineering and Applied Sciences, Cambridge, Massachusetts, United States. 2. Boston Children's Hospital, Boston, Massachusetts, United States. 3. Harvard Medical School, Boston, Massachusetts, United States.
Abstract
Purpose: In ultrasound imaging, interpolation is a key step in converting scanline data to brightness-mode (B-mode) images. Conventional methods, such as bilinear interpolation, do not fully capture the spatial dependence between data points, which leads to deviations from the underlying probability distribution at the interpolation points. Approach: We propose Gaussian process ( GP ) regression as an improved method for ultrasound scanline interpolation. Using ultrasound scanlines acquired from two different ultrasound scanners during in vivo trials, we compare the scanline conversion accuracy of three standard interpolation methods with that of GP regression, measuring the peak signal-to-noise ratio (PSNR) and mean absolute error (MAE) for each method. Results: The PSNR and MAE scores show that GP regression leads to more accurate scanline conversion compared to the nearest neighbor, bilinear, and cubic spline interpolation methods, for both datasets. Furthermore, limiting the interpolation window size of GP regression to 15 reduces computation time with minimal to no reduction in PSNR. Conclusions: GP regression quantitatively leads to more accurate scanline conversion and provides uncertainty estimates at each of the interpolation points. Our windowing method reduces the computational cost of using GP regression for scanline conversion.
Purpose: In ultrasound imaging, interpolation is a key step in converting scanline data to brightness-mode (B-mode) images. Conventional methods, such as bilinear interpolation, do not fully capture the spatial dependence between data points, which leads to deviations from the underlying probability distribution at the interpolation points. Approach: We propose Gaussian process ( GP ) regression as an improved method for ultrasound scanline interpolation. Using ultrasound scanlines acquired from two different ultrasound scanners during in vivo trials, we compare the scanline conversion accuracy of three standard interpolation methods with that of GP regression, measuring the peak signal-to-noise ratio (PSNR) and mean absolute error (MAE) for each method. Results: The PSNR and MAE scores show that GP regression leads to more accurate scanline conversion compared to the nearest neighbor, bilinear, and cubic spline interpolation methods, for both datasets. Furthermore, limiting the interpolation window size of GP regression to 15 reduces computation time with minimal to no reduction in PSNR. Conclusions: GP regression quantitatively leads to more accurate scanline conversion and provides uncertainty estimates at each of the interpolation points. Our windowing method reduces the computational cost of using GP regression for scanline conversion.
Authors: Ole Vegard Solberg; Frank Lindseth; Hans Torp; Richard E Blake; Toril A Nagelhus Hernes Journal: Ultrasound Med Biol Date: 2007-05-18 Impact factor: 2.998
Authors: Ben Luijten; Regev Cohen; Frederik J de Bruijn; Harold A W Schmeitz; Massimo Mischi; Yonina C Eldar; Ruud J G van Sloun Journal: IEEE Trans Med Imaging Date: 2020-11-30 Impact factor: 10.048