Microbubble Localization for Three-Dimensional Superresolution Ultrasound Imaging Using Curve Fitting and Deconvolution Methods

摘要

Superresolution algorithms in ultrasound imaging are attracting the interest of researchers recently due to the ability of these methods to enable enhanced vascular imaging. In this study, two superresolution imaging methods are compared for postprocessing images of microbubbles generated using passive acoustic mapping (PAM) methods with a potential application of three-dimensional (3-D) brain vascular imaging. The first method is based on fitting single bubble images one at a time with a 3-D Gaussian profile to localize the microbubbles and a superresolution image is then formed using the uncertainty of the localization as the standard deviation of the Gaussian profile. The second superresolution method is based on image deconvolution that processes multiframe resolution-limited images iteratively and estimates the intensity at each pixel of the superresolution image without the need for localizing each microbubble. The point spread function is approximated by a Gaussian curve which is similar to the beam response of the hemispherical transducer array used in our experimental setup. The Cramér–Rao Bounds of the two estimation techniques are derived analytically and the performance of these techniques is compared through numerical simulations based on experimental PAM images. For linear and sinusoidal traces, the localization errors between the estimated peaks by the fitting-based method and the actual source locations were 220$pm$10 $mu$m and 210$pm$5 $mu$m, respectively, as compared to 74$pm$10$mu$m and 59$pm$8$mu$m with the deconvolution-based method. However, in terms of the running time and the computational costs, the curve fitting technique outperforms the deconvolution-based approach.