Regional-Lag Signed Delay Multiply and Sum Beamforming in Ultrafast Ultrasound Imaging

Yan, Xin; Qi, Yanxing; Wang, Yinmeng; Wang, Yuanyuan

doi:10.1109/tuffc.2021.3127878

Cited by 13 publications

(4 citation statements)

References 37 publications

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“…The F-DMAS also imposes a high computational load due to the square root and multiplications operations in the image generation [16], but there are some fast implementations proposed in [17,18] to address the issue. The method has been further improved for better performance in subsequent studies, such as double-stage F-DMAS [19], subarray F-DMAS [20], signed DMAS [21,22], baseband DMAS [23], and weighted F-DMAS [24,25].…”

Section: Introductionmentioning

confidence: 99%

Pixel-based approach to delay multiply and sum beamforming in combination with Wiener filter for improving ultrasound image quality

et al. 2023

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Section: Introductionmentioning

confidence: 99%

Pixel-based approach to delay multiply and sum beamforming in combination with Wiener filter for improving ultrasound image quality

et al. 2023

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“…However, we just gave a brief introduction of the concept and did not explain in detail the potential applications and main theory behind the improvement of AR. Several researchers [ 24 , 25 , 26 , 27 , 28 ] also implemented and compared our proposed method, but the results they obtained and presented had several flaws. First, the dynamic range used to present all the B-mode images in their work was 70 dB, which was very high compared to the conventional range of 40 to 60 dB.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of Ultrasound B-Mode Image Quality Using Nonlinear Filtered-Multiply-and-Sum Compounding for Improved Carotid Artery Segmentation

et al. 2023

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In ultrasound B-mode imaging, the axial resolution (AR) is commonly determined by the duration or bandwidth of an excitation signal. A shorter-duration pulse will produce better resolution compared to a longer one but with compromised penetration depth. Instead of relying on the pulse duration or bandwidth to improve the AR, an alternative method termed filtered multiply and sum (FMAS) has been introduced in our previous work. For spatial-compounding, FMAS uses the autocorrelation technique as used in filtered-delay multiply and sum (FDMAS), instead of conventional averaging. FMAS enables a higher frame rate and less computational complexity than conventional plane-wave compound imaging beamformed with delay and sum (DAS) and FDMAS. Moreover, it can provide an improved contrast ratio and AR. In previous work, no explanation was given on how FMAS was able to improve the AR. Thus, in this work, we discuss in detail the theory behind the proposed FMAS algorithm and how it is able to improve the spatial resolution mainly in the axial direction. Simulations, experimental phantom measurements and in vivo studies were conducted to benchmark the performance of the proposed method. We also demonstrate how the suggested new algorithm may be used in a practical biomedical imaging application. The balloon snake active contour segmentation technique was applied to the ultrasound B-mode image of a common carotid artery produced with FMAS. The suggested method is capable of reducing the number of iterations for the snake to settle on the region-of-interest contour, accelerating the segmentation process.

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“…proposed a robust SLSC (R-SLSC) which enables the use of higher lag information, and demonstrated that the R-SLSC provides increased contrast, signal-to-noise ratio, and contrast-to-noise ratio over traditional SLSC imaging [ 24 ]. The double-stage DMAS (DS-DMAS) [ 25 ] and regional-lag signed DMAS (rsDMAS) [ 26 ] were successfully developed to improve the imaging quality of DMAS. In addition, the DMAS has been studied to improve active contour segmentation in ultrafast ultrasound imaging [ 27 ] and improve contrast, resolution, and signal-to-clutter ratio by expanding the two-dimensional summations to four-dimensional summations [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

Minimum variance beamforming combined with covariance matrix-based adaptive weighting for medical ultrasound imaging

Wang

Liu

et al. 2022

BioMed Eng OnLine

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Background The minimum variance (MV) beamformer can significantly improve the image resolution in ultrasound imaging, but it has limited performance in noise reduction. We recently proposed the covariance matrix-based statistical beamforming (CMSB) for medical ultrasound imaging to reduce sidelobes and incoherent clutter. Methods In this paper, we aim to improve the imaging performance of the MV beamformer by introducing a new pixel-based adaptive weighting approach based on CMSB, which is named as covariance matrix-based adaptive weighting (CMSAW). The proposed CMSAW estimates the mean-to-standard-deviation ratio (MSR) of a modified covariance matrix reconstructed by adaptive spatial smoothing, rotary averaging, and diagonal reducing. Moreover, adaptive diagonal reducing based on the aperture coherence is introduced in CMSAW to enhance the performance in speckle preservation. Results The proposed CMSAW-weighted MV (CMSAW-MV) was validated through simulation, phantom experiments, and in vivo studies. The phantom experimental results show that CMSAW-MV obtains resolution improvement of 21.3% and simultaneously achieves average improvements of 96.4% and 71.8% in average contrast and generalized contrast-to-noise ratio (gCNR) for anechoic cyst, respectively, compared with MV. in vivo studies indicate that CMSAW-MV improves the noise reduction performance of MV beamformer. Conclusion Simulation, experimental, and in vivo results all show that CMSAW-MV can improve resolution and suppress sidelobes and incoherent clutter and noise. These results demonstrate the effectiveness of CMSAW in improving the imaging performance of MV beamformer. Moreover, the proposed CMSAW with a computational complexity of $$O(N^2)$$ O ( N 2 ) has the potential to be implemented in real time using the graphics processing unit.

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Regional-Lag Signed Delay Multiply and Sum Beamforming in Ultrafast Ultrasound Imaging

Cited by 13 publications

References 37 publications

Pixel-based approach to delay multiply and sum beamforming in combination with Wiener filter for improving ultrasound image quality

Pixel-based approach to delay multiply and sum beamforming in combination with Wiener filter for improving ultrasound image quality

Enhancement of Ultrasound B-Mode Image Quality Using Nonlinear Filtered-Multiply-and-Sum Compounding for Improved Carotid Artery Segmentation

Minimum variance beamforming combined with covariance matrix-based adaptive weighting for medical ultrasound imaging

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