Abstract

Speckle variance in ultrasound images limits the detection of low-contrast targets. In conventional compounding, multiple correlated sub-images are generated and then averaged to reduce the speckles at the cost of resolution loss. In this paper, a decorrelation procedure was applied to the correlated sub-images to further reduce speckle variance. Lesion signal-to-noise-ratio (lSNR), which combines the effect of speckle reduction and resolution loss, was used as an indicator of the detectability of lesions. The lSNR of the hyperechoic lesion in the simulated and experimental images using decorrelated compounding was increased by 122% and 89%, respectively, compared to the delay-and-sum method.

Highlights

  • Speckles are inherently present in ultrasound images and appear as a grainy texture

  • Lesion signal-tonoise-ratio, which combines the effect of speckle reduction and resolution loss, was used as an indicator of the detectability of lesions

  • The envelope of the beamformed radio frequency (RF) data or sub-images were averaged in incoherent compounding, such as spatial compounding (SC) (Shattuck and Vonramm, 1982) and frequency compounding (FC) (Melton and Magnin, 1984)

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Summary

Introduction

Speckles are inherently present in ultrasound images and appear as a grainy texture. Speckles are formed by the interference of the echoes generated from a cluster of scattering particles (Burckhardt, 1978) within one resolution cell. Various compounding methods and filtering methods were proposed for speckle reduction to improve lesion detectability (Perperidis, 2016). The proposed method is similar to incoherent compounding in that the envelope of the decorrelated subimages were averaged, but the phase information of the sub-images were used in the decorrelation process. Despeckling is mainly used to improve the image quality for further image processing It is not clear if the detectability of low-contrast lesions can be significantly improved with these methods. The improvement of lesion (> 1⁄4 6 dB contrast) detectability was studied by applying filtering method (Verhoeven and Thijssen, 1993), frequency compounding (Sanchez and Oelze, 2009), and spatial compounding (Hansen and Jensen, 2012)

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