Spatially Smoothed Adaptive Null Subtraction Imaging Applied to Coherent Plane Wave Compounding

The coherent plane-wave compounding (CPWC) method is known as an ultrafast imaging technique in the medical field that provides high-speed imaging scenarios. To achieve a reconstructed image with improved quality in CPWC, different techniques have been developed. The nonlinear null subtraction imaging (NSI) method is one of the recently developed algorithms that results in resolution improvement with lower computational complexity compared to the adaptive minimum variance (MV) algorithm. However, this algorithm cannot preserve the speckle statistics well enough. Also, the resolution improvement of NSI depends on an adjustable parameter (bias). To tackle this limitation, a modified version of the NSI algorithm, known as spatially smoothed adaptive NSI (SSANSI), is proposed in this article in which the bias parameter is determined adaptively for each imaging point. Also, the spatial smoothing process over some overlapping subarrays is performed to further improve the background speckle in speckle-generating datasets. Quantitative and qualitative evaluations on PICMUS datasets demonstrate that the SSANSI algorithm outperforms NSI in terms of retrieving the speckle information, while the image resolution is also improved. In particular, for the experimental-resolution dataset, the proposed SSANSI algorithm improves the resolution metric [full-width at half-maximum (FWHM)] for about 0.31, 0.21, and 0.02 (in mm) than that of delay-and-sum (DAS), MV, and NSI (bias = 0.5), respectively. Also, the contrast metric [generalized contrast-to-noise ratio (gCNR)] of SSANSI is increased by about 0.05, 0.01, and 0.46, respectively, compared to DAS, MV, and NSI, for in vivo dataset.