A dynamic generalized coherence factor for side lobe suppression in ultrasound imaging

Coherence-based weighting techniques have been widely studied to weight beamsummed data to improve image quality in ultrasound imaging. Although generalized coherence factor (GCF) enhances the robustness of coherence factor (CF) with preserved speckle pattern by including some incoherent components, the side lobe suppression performance is insufficient due to constant cut-off frequency M0. To address this problem, we introduced in this paper a dynamic GCF method, referred to as DGCF-C, based on the amplitude standard deviation and the convolution output of aperture data. The cut-off frequency is adaptively selected for GCF at each imaging point using the amplitude standard deviation of aperture data. Moreover, the convolution output of aperture data is used to calculate the dynamic GCF. The proposed method is evaluated in simulation and tissue-mimicking phantom studies. The image quality was analyzed in terms of resolution, contrast ratio (CR), generalized contrast-to-noise ratio (GCNR), speckle signal-to-noise ratio (sSNR), and signal-to-noise ratio (SNR). The results demonstrate that DGCF-C (Mmax=2) achieves mean resolution improvements of 35.1% in simulation, and 32.6% in experiment, compared with GCF (M0=1). Moreover, DGCF-C (Mmax=4) outperforms GCF (M0=2) with an average GCNR improvement of 13.5% and an average sSNR improvement of 15.2%, which indicates the better-preservation of speckle.

[1]  J. Jensen,et al.  Calculation of pressure fields from arbitrarily shaped, apodized, and excited ultrasound transducers , 1992, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[2]  G. E. Trahey,et al.  Short-lag spatial coherence of backscattered echoes: imaging characteristics , 2011, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[3]  M. O'Donnell,et al.  Phase-aberration correction using signals from point reflectors and diffuse scatterers: measurements , 1988, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[4]  A. Austeng,et al.  Benefits of minimum-variance beamforming in medical ultrasound imaging , 2009, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[5]  Yi Guo,et al.  2-D Minimum Variance Based Plane Wave Compounding with Generalized Coherence Factor in Ultrafast Ultrasound Imaging , 2018, Sensors.

[6]  Billy Y S Yiu,et al.  GPU-based minimum variance beamformer for synthetic aperture imaging of the eye. , 2015, Ultrasound in medicine & biology.

[7]  Yonina C. Eldar,et al.  Sparse Convolutional Beamforming for Ultrasound Imaging , 2018, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.

[8]  Moein Mozaffarzadeh,et al.  Enhanced linear-array photoacoustic beamforming using modified coherence factor , 2017, Journal of biomedical optics.

[9]  Sverre Holm,et al.  Implementing capon beamforming on a GPU for real-time cardiac ultrasound imaging , 2014, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.

[10]  Moein Mozaffarzadeh,et al.  Image improvement in linear-array photoacoustic imaging using high resolution coherence factor weighting technique , 2019, BMC biomedical engineering.

[11]  Vassilis Sboros,et al.  Experimental performance assessment of the sub‐band minimum variance beamformer for ultrasound imaging , 2017, Ultrasonics.

[12]  A. Mahloojifar,et al.  Minimum variance beamforming combined with adaptive coherence weighting applied to medical ultrasound imaging , 2009, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[13]  Giovanni Magenes,et al.  The Delay Multiply and Sum Beamforming Algorithm in Ultrasound B-Mode Medical Imaging , 2015, IEEE Transactions on Medical Imaging.

[14]  Yong-Ping Zheng,et al.  Subarray coherence based postfilter for eigenspace based minimum variance beamformer in ultrasound plane-wave imaging. , 2016, Ultrasonics.

[15]  Ali Mahloojifar,et al.  Double-Stage Delay Multiply and Sum Beamforming Algorithm: Application to Linear-Array Photoacoustic Imaging , 2018, IEEE Transactions on Biomedical Engineering.

[16]  You Leo Li,et al.  Coherent flow power doppler (CFPD): flow detection using spatial coherence beamforming , 2015, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.

[17]  Ali Mahloojifar,et al.  Double-Stage Delay Multiply and Sum Beamforming Algorithm Applied to Ultrasound Medical Imaging. , 2017, Ultrasound in medicine & biology.

[18]  Eigil Samset,et al.  Delay and Standard Deviation Beamforming to Enhance Specular Reflections in Ultrasound Imaging , 2016, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.

[19]  M. O'Donnell,et al.  Synthetic aperture imaging for small scale systems , 1995, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[20]  Che-Chou Shen,et al.  Autocorrelation-based generalized coherence factor for low-complexity adaptive beamforming. , 2016, Ultrasonics.

[21]  Piero Tortoli,et al.  High Frame-Rate, High Resolution Ultrasound Imaging With Multi-Line Transmission and Filtered-Delay Multiply And Sum Beamforming , 2017, IEEE Trans. Medical Imaging.

[22]  Piero Tortoli,et al.  Experimental evaluation of ultrasound higher‐order harmonic imaging with Filtered‐Delay Multiply And Sum (F‐DMAS) non‐linear beamforming , 2018, Ultrasonics.

[23]  Sverre Holm,et al.  Wiener beamforming and the coherence factor in ultrasound imaging , 2010, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[24]  Shi Zhang,et al.  An efficient subarray average delay multiply and sum beamformer algorithm in ultrasound imaging , 2018, Ultrasonics.

[25]  Christian Cachard,et al.  A Nonlinear Beamformer Based on p-th Root Compression—Application to Plane Wave Ultrasound Imaging , 2018 .

[26]  M. O’Donnell,et al.  Phase-aberration correction using signals from point reflectors and diffuse scatterers: basic principles , 1988, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[27]  Pai-Chi Li,et al.  MVDR-based coherence weighting for high-frame-rate adaptive imaging , 2009, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[28]  Dongwoon Hyun,et al.  Lesion Detectability in Diagnostic Ultrasound with Short-Lag Spatial Coherence Imaging , 2011, Ultrasonic imaging.

[29]  Pai-Chi Li,et al.  Adaptive imaging using the generalized coherence factor. , 2003, IEEE transactions on ultrasonics, ferroelectrics, and frequency control.

[30]  G. Trahey,et al.  Sources of image degradation in fundamental and harmonic ultrasound imaging using nonlinear, full-wave simulations , 2011, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[31]  B M Asl,et al.  Eigenspace-based minimum variance beamforming applied to medical ultrasound imaging , 2010, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[32]  Jørgen Arendt Jensen,et al.  Synthetic aperture ultrasound imaging. , 2006, Ultrasonics.

[33]  Gregg E Trahey,et al.  In vivo application of short-lag spatial coherence imaging in human liver. , 2013, Ultrasound in medicine & biology.

[34]  G. Trahey,et al.  Short-lag spatial coherence imaging of cardiac ultrasound data: initial clinical results. , 2013, Ultrasound in medicine & biology.

[35]  Liang Zhou,et al.  Zero-Phase Filtered Delay Multiply and Sum in Ultrasound Computed Tomography , 2017 .

[36]  J. Capon.  High-resolution frequency-wavenumber spectrum analysis , 1969 .

[37]  A. Austeng,et al.  Adaptive Beamforming Applied to Medical Ultrasound Imaging , 2007, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[38]  J. Camacho,et al.  Phase Coherence Imaging , 2009, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[39]  Jan D'hooge,et al.  The Generalized Contrast-to-Noise Ratio , 2018, 2018 IEEE International Ultrasonics Symposium (IUS).