Combined phase screen aberration correction and minimum variance beamforming in medical ultrasound

Ziksari, Mahsa Sotoodeh; Asl, Babak Mohammadzadeh

doi:10.1016/j.ultras.2016.11.015

Cited by 14 publications

(3 citation statements)

References 25 publications

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“…Various phase aberration correction methods have previously been proposed to compensate for the phase distortions [6][7][8][9][10][11][12][13]. Representatively, cross-correlation-based [6][7][8][9] and speckle-brightness-based [10] phase aberration correction methods have been proposed.…”

Section: Introductionmentioning

confidence: 99%

“…Representatively, cross-correlation-based [6][7][8][9] and speckle-brightness-based [10] phase aberration correction methods have been proposed. Recently, methods combined with adaptive beamforming methods have been proposed [11][12][13] that can improve the quality of whole images, but the implementation of these methods is still challenging due to their high computational complexity [13,14]. In the nearest-neighbor cross-correlation (NNCC) method [6,12,13], the number of multiplications is expressed approximately as N mult = (N − 1) × K image × L image × M, where N, L image and K image are the number of channels, scanlines and samples per a scanline in the whole image, respectively, and M is the total number of samples that contribute to the cross-correlation function.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, methods combined with adaptive beamforming methods have been proposed [11][12][13] that can improve the quality of whole images, but the implementation of these methods is still challenging due to their high computational complexity [13,14]. In the nearest-neighbor cross-correlation (NNCC) method [6,12,13], the number of multiplications is expressed approximately as N mult = (N − 1) × K image × L image × M, where N, L image and K image are the number of channels, scanlines and samples per a scanline in the whole image, respectively, and M is the total number of samples that contribute to the cross-correlation function. For an abdominal image with depth of 160 mm, the number of multiplications is approximately 1.3 billion × M when N =128, L image = 256 and K image = 4k; thus, the implementation of these methods in real time would be challenging.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Computationally Efficient Mean Sound Speed Estimation Method Based on an Evaluation of Focusing Quality for Medical Ultrasound Imaging

et al. 2019

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Generally, ultrasound receive beamformers calculate the focusing time delays of fixed sound speeds in human tissue (e.g., 1540 m/s). However, phase distortions occur due to variations of sound speeds in soft tissues, resulting in degradation of image quality. Thus, an optimal estimation of sound speed is required in order to improve image quality. Implementation of real-time sound speed estimation is challenging due to high computational and hardware complexities. In this paper, an optimal sound speed estimation method with a low-cost hardware resource is presented. In the proposed method, the optimal mean sound speed is determined by measuring the amplitude variance of pre-beamformed radio-frequency (RF) data. The proposed method was evaluated with phantom and in vivo experiments, and implemented on Virtex-4 with Xilinx ISE 12.4 using VHDL. Experiment results indicate that the proposed method could estimate the mean optimal sound speed and enhance spatial resolution with a negligible increase in the hardware resource usage.

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