Intrinsic Tradeoffs in Multi-Covariate Imaging of Sub-Resolution Targets

Abstract

Multi-covariate Imaging of Sub-resolution Targets (MIST) is an estimation-based method of imaging the statistics of diffuse scattering targets. MIST estimates the contributions of a set of covariance models to the echo data covariance matrix. Models are defined based on a spatial decomposition of the theoretical transmit intensity distribution into on-axis and off-axis contributions, delineated by a user-specified spatial cutoff. We define this cutoff as the region of interest width (ROI width). In our previous work, we selected the ROI width as the first zero crossing separating the mainlobe from the sidelobe regions. This paper explores the effects of varying two key parameters on MIST image quality: (1) ROI width and (2) the degree of spatial averaging of the measured echo data covariance matrix. These results demonstrate a fundamental tradeoff between resolution and speckle texture. We characterize MIST imaging performance across these tunable parameters in a number of simulated, phantom, and in vivo liver applications.We consider performance in noise, fidelity to native contrast, resolution, and speckle texture. MIST is also compared to varying levels of spatial and frequency compounding, demonstrating quantitative improvements in image quality at comparable levels of speckle reduction. In an in vivo example, optimized MIST images demonstrated 20.2% and 13.4% improvements in contrastto-noise ratio over optimized spatial and frequency compounding images, respectively. These results present a framework for selecting MIST parameters to maximize speckle signal-to-noise ratio without an appreciable loss in resolution.

Decorrelated Compounding in Ultrasound Images

“…Filtering compounding [3,22,23] and incoherent compounding [24,25,26,27,28,29,30] methods are two main approaches for speckle reduction. Novel approaches such as covariance based estimation method [31,32] and neural networks [33,34] were recently proposed for despeckling.…”

“…Another novel despeckling method is the multi-covariate Imaging of Sub-resolution Targets (MIST) [31] an estimation based beamforming method. MIST demonstrated improved image quality in terms of CNR at comparable resolution levels, [32]. However, none of these researches focused on detecting low-contrast targets (<3 dB), where speckles have a serious impact on the detectability of the targets in the Delay-And-Sum (DAS) method.…”

Decorrelated Compounding in Ultrasound Images

“…Filtering compounding [3,22,23] and incoherent compounding [24,25,26,27,28,29,30] methods are two main approaches for speckle reduction. Novel approaches such as covariance based estimation method [31,32] and neural networks [33,34] were recently proposed for despeckling.…”

Advances in ultrasonography: image formation and quality assessment

“…Morgan et al also decomposed received echo signals into components from main lobe, side lobe, and incoherent noise using models of their covariance model, namely, constituent covariance models [ 55 , 56 ]. The model of the covariance matrix of the received echo signal is expressed as where is the number of constituent components, is the constituent covariance model of the -th component, is the scalar variance, which corresponds to the power of the -th component, and N is a noise matrix.…”