Characteristics of Minimum Variance Beamformer for Frequency and Plane-wave Compounding

Recently, coherent plane-wave compounding (CPWC) that achieves high spatiotemporal resolution has been studied actively as a spatial compounding beamformer. Further, various frequency compounding methods have been proposed for reducing speckle noise. We already proposed the method called frequency and plane-wave compounding minimum variance distortionless response (FPWC-MVDR), which achieves high spatial resolution imaging by simultaneously optimizing frequency and spatial compounding based on minimum variance scheme. In the algorithm of this method, the data-compounded-on-receive MVDR (DCR-MVDR) principle developed for CPWC is extended and applied. In this study, we analyze the features and characteristics of FPWC-MVDR and the weaknesses to be solved in the future through experiments.

[1]  Jing Zhu,et al.  Super-Resolution Ultrasound Imaging Based on the Phase of the Carrier Wave Without Deterioration by Grating Lobes , 2018, 2018 24th International Conference on Pattern Recognition (ICPR).

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

[3]  Guan Gui,et al.  Deep Learning for Super-Resolution Channel Estimation and DOA Estimation Based Massive MIMO System , 2018, IEEE Transactions on Vehicular Technology.

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

[5]  Pai-Chi Li,et al.  Adaptive imaging using the generalized coherence factor , 2003, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[6]  M. Fink,et al.  Coherent plane-wave compounding for very high frame rate ultrasonography and transient elastography , 2009, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[7]  Francois Vignon,et al.  Capon beamforming in medical ultrasound imaging with focused beams , 2008, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[8]  Ryoya Kozai,et al.  Optimization of Frequency and Plane- Wave Compounding by Minimum Variance Beamforming , 2020, 2020 IEEE International Ultrasonics Symposium (IUS).

[9]  Amina Barhdadi,et al.  Contribution of Tissue Harmonic Imaging and Frequency Compound Imaging in Interventional Breast Sonography , 2006, Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine.

[10]  A. Mahloojifar,et al.  A low-complexity adaptive beamformer for ultrasound imaging using structured covariance matrix , 2012, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[11]  Jing Zhu,et al.  Improvement of Performance Degradation in Synthetic Aperture Extension of Enhanced Axial Resolution Ultrasound Imaging Based on Frequency Sweep , 2019, Sensors.

[12]  Wei Wang,et al.  Deep Learning for Single Image Super-Resolution: A Brief Review , 2018, IEEE Transactions on Multimedia.

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

[14]  O. V. von Ramm,et al.  Frequency compounding for speckle contrast reduction in phased array images. , 1982, Ultrasonic imaging.

[15]  K. Thomenius,et al.  Evolution of ultrasound beamformers , 1996, 1996 IEEE Ultrasonics Symposium. Proceedings.

[16]  Shaoguo Cui,et al.  Noise reduction for ultrasonic elastography using transmit-side frequency compounding: a preliminary study , 2011, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[17]  N. Tagawa,et al.  High resolution ultrasonic imaging based on frequency sweep in both of transducer element domain and imaging line domain , 2019, Japanese Journal of Applied Physics.

[18]  Richard W. Prager,et al.  A Spatial Coherence Approach to Minimum Variance Beamforming for Plane-Wave Compounding , 2018, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.

[19]  Jing Zhu,et al.  Performance improvement of ultrasonic range super-resolution based on phase rotation by dealing with echo distortion , 2019 .

[20]  Hamid Reza Shahdoosti,et al.  Edge-preserving image denoising using a deep convolutional neural network , 2019, Signal Process..

[21]  Yonina C. Eldar,et al.  Deep Learning for Fast Adaptive Beamforming , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[22]  Wonseok Jeon,et al.  Speckle noise reduction for digital holographic images using multi-scale convolutional neural networks. , 2018, Optics letters.

[23]  Pasquale Memmolo,et al.  Multilevel bidimensional empirical mode decomposition: a new speckle reduction method in digital holography , 2014 .

[24]  Sverre Holm,et al.  Eigenspace Based Minimum Variance Beamforming Applied to Ultrasound Imaging of Acoustically Hard Tissues , 2012, IEEE Transactions on Medical Imaging.

[25]  Christophe Zimmer,et al.  Deep learning massively accelerates super-resolution localization microscopy , 2018, Nature Biotechnology.

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