Adaptive diagonal loaded minimum variance beamforming applied to medical ultrasound imaging

Hao-lin Liu , Zhi-hong Zhang , Dong-quan Liu

Journal of Central South University ›› 2015, Vol. 22 ›› Issue (5) : 1826 -1832.

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Journal of Central South University ›› 2015, Vol. 22 ›› Issue (5) : 1826 -1832. DOI: 10.1007/s11771-015-2701-9
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Adaptive diagonal loaded minimum variance beamforming applied to medical ultrasound imaging

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Abstract

In order to enhance the robustness and contrast in the minimum variance (MV) beamformer, adaptive diagonal loading method was proposed. The conventional diagonal loading technique has already been used in the MV beamformer, but has the drawback that its level is specified by predefined parameter and without consideration of input-data. To alleviate this problem, the level of diagonal loading was computed appropriately and automatically from the given data by shrinkage method in the proposed adaptive diagonal loaded beamformer. The performance of the proposed beamformer was tested on the simulated point target and cyst phantom was obtained using Field II. In the point target simulation, it is shown that the proposed method has higher lateral resolution than the conventional delay-and-sum beamformer and could be more robust in estimating the amplitude peak than the MV beamformer when acoustic velocity error exists. In the cyst phantom simulation, the proposed beamformer has shown that it achieves an improvement in contrast ratio and without distorting the edges of cyst.

Keywords

medical ultrasound imaging / minimum variance beamforming / diagonal loading / delay-and-sum beamforming / contrast / robustness

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Hao-lin Liu, Zhi-hong Zhang, Dong-quan Liu. Adaptive diagonal loaded minimum variance beamforming applied to medical ultrasound imaging. Journal of Central South University, 2015, 22(5): 1826-1832 DOI:10.1007/s11771-015-2701-9

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References

[1]

SzaboT LDiagnostic ultrasound imaging: Inside out [M], 2004, Salt Lake City, Academic Press

[2]

SynnevagJ F, AustengA, HolmS. Adaptive beamforming applied to medical ultrasound imaging [J]. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, 2007, 54(8): 1606-1613

[3]

NilsenC C, HafizovicI. Beamspace adaptive beamforming for ultrasound imaging [J]. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, 2009, 56(10): 2187-2197

[4]

SynnevagJ F, AustengA, HolmS. Benefits of minimum-variance beamforming in medical ultrasound imaging [J]. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, 2009, 56(9): 1868-1879

[5]

ZhangZ-h, LiuH-l, HeY-n, LiuD-quan. Optimization of ultrasonic elastography by coded excitation and transmit-side multi-frequency compounding [J]. Journal of Central South University, 2014, 21(3): 1003-1010

[6]

AslB M, MahloojifarA. Minimum variance beamforming combined with adaptive coherence weighting applied to medical ultrasound imaging [J]. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, 2009, 56(9): 1923-1931

[7]

AslB M, MahloojifarA. A low-complexity adaptive beamformer for ultrasound imaging using structured covariance matrix [J]. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, 2012, 59(4): 660-667

[8]

AslB M, MahloojifarA. Eigenspace-based minimum variance beamforming applied to medical ultrasound imaging [J]. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, 2010, 57(11): 2381-2390

[9]

ZengX, WangY, YuJ. Correspondence-Beam-domain eigenspace-based minimum variance beamformer for medical ultrasound imaging [J]. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, 2013, 60(12): 2670-2676

[10]

CaponJ. High-resolution frequency-wavenumber spectrum analysis [J]. Proceedings of the IEEE, 1969, 57(8): 1408-1418

[11]

ShanT J, KailathT. Adaptive beamforming for coherent signals and interference [J]. IEEE Transactions onAcoustics, Speech and Signal Processing, 1985, 33(3): 527-536

[12]

YangJ, MaX-c, HouC-huan. Shrinkage-based capon and APES for spectral estimation [J]. IEEE Signal Processing Letters, 2009, 16(10): 869-872

[13]

DuL, LiJ, StoicaP. User parameter free approaches to multistatic adaptive ultrasound imaging [C]. 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro. ISBI 2008, 2008, Paris, IEEE Press: 1287-1290

[14]

AroraJ SIntroduction to optimum design [M], 1989, New York, McGraw-Hill

[15]

ShanT J, WaxM, KailathT. On spatial smoothing for direction-of-arrival estimation of coherent signals [J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1985, 33(4): 806-811

[16]

LiJ, StoicaP. An adaptive filtering approach to spectral estimation and SAR imaging [J]. IEEE Transactions on Signal Processing, 1996, 44(6): 1469-1484

[17]

LedoitO, WolfM. A well-conditioned estimator for large-dimensional covariance matrices [J]. Journal of Multivariate Analysis, 2004, 88(2): 365-411

[18]

StoicaP, LiJ, ZhuX. On using a priori knowledge in space-time adaptive processing [J]. IEEE Transactions on Signal Processing, 2008, 56(6): 2598-2602

[19]

JensenJ A. Field: A program for simulating ultrasound systems [J]. Medical & Biological Engineering & Computing, 1996, 34(1): 351-353

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