Maximum negentropy beamforming using complex generalized Gaussian distribution model

Kumatani, Kenichi; Rauch, Barbara; McDonough, John; Klakow, Dietrich

doi:10.1109/acssc.2010.5757769

Cited by 6 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several beamformers based on HOS have been proposed in the optimum array processing literature. Adaptive beamforming based on HOS has been attempted in the field of far-field speech recognition using kurtosis or negentropy maximization, achieving efficient noise and reverberation suppression [36], [37]. A polyspectra approach has also been proposed for source detection and parameter estimation of wideband sources demonstrating optimal performance for non-Gaussian signals in the presence of additive Gaussian noise [38].…”

Section: Introductionmentioning

confidence: 99%

Passive Acoustic Mapping Using Data-Adaptive Beamforming Based on Higher Order Statistics

Lyka

Coviello

Paverd

et al. 2018

IEEE Trans. Med. Imaging

View full text Add to dashboard Cite

Sources of nonlinear acoustic emissions, particularly those associated with cavitation activity, play a key role in the safety and efficacy of current and emerging therapeutic ultrasound applications, such as oncological drug delivery, blood-brain barrier opening, and histotripsy. Passive acoustic mapping (PAM) is the first technique to enable real-time and non-invasive imaging of cavitation activity during therapeutic ultrasound exposure, through the recording and passive beamforming of broadband acoustic emissions using an array of ultrasound detectors. Initial limitations in PAM spatial resolution led to the adoption of optimal data-adaptive beamforming algorithms, such as the robust capon beamformer (RCB), that provide improved interference suppression and calibration error mitigation compared to non-adaptive beamformers. However, such approaches are restricted by the assumption that the recorded signals have a Gaussian distribution. To overcome this limitation and further improve the source resolvability of PAM, we propose a new beamforming approach termed robust beamforming by linear programming (RLPB). Along with the variance, this optimization-based method uses higher-order-statistics of the recorded signals, making no prior assumption on the statistical distribution of the acoustic signals. The RLPB is found via numerical simulations to improve resolvability over time exposure acoustics and RCB. In vitro experimentation yielded improved resolvability with respect to the source-to-array distance on the order of 22% axially and 13% transversely relative to RCB, whilst successfully accounting for array calibration errors. The improved resolution and decreased dependence on accurate calibration of RLPB is expected to facilitate the clinical translation of PAM for diagnostic, including super-resolution, and therapeutic ultrasound applications.

show abstract

Section: Introductionmentioning

confidence: 99%