Using Sparse Array for 3D Passive Cavitation Imaging

Sivadon, Audrey; Varray, François; Nicolas, Barbara; Béra, Jean‐Christophe; Gilles, B.

doi:10.1109/ius46767.2020.9251562

Cited by 2 publications

(3 citation statements)

References 17 publications

(10 reference statements)

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“…where the expression of PF D M W (r, f ) can be deduced from the r ̸ = 0 case of equation (20) in [32] by taking the first order Taylor expansion of M r at r = 0. In 2D passive imaging, the FD-MW beamformer has been proven to give well-balanced performances between DAS and RCB, thanks to a computation complexity equivalent to the one of FD-DAS, for metrics approaching performances of FD-RCB [34]. This allows to propose FD-MW as a good compromise between previously presented beamformers, in the context of 3D-PAM.…”

Section: Adaptive Fd-mw Beamformermentioning

confidence: 98%

“…Nevertheless, such custom-built hemispherical configurations are very specific to transcranial therapy and imaging, and the case of more versatile and generic systems like the above-mentioned commercial array used in [13] and [22] have to be considered. In a preliminary study [25], we compared 3D-PAM of a simulated harmonic point-source computed with a full matrix array similar to this commercial probe, to the one obtained with a random sparse array using only 256 elements among the 1024 available. It confirmed the pertinence of using a spatial apodization which reduces the number of active elements from 1024 to 256 with a random spatial pattern with negligible loss in the image quality and a drastic reduction of the computational cost.…”

Section: Introductionmentioning

confidence: 99%

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3-D Passive Cavitation Imaging Using Adaptive Beamforming and Matrix Array Transducer With Random Apodization

Sivadon,

Varray,

Béra

et al. 2024

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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With the development of promising cavitation-based treatments, the interest in cavitation monitoring with passive acoustic mapping (PAM) is significantly increasing. While most of studies regarding PAM are performed in 2D, 3D imaging modalities are getting more attention relying on either custommade or commercial matrix probes. Unless specific phasedarrays are used for a specific application, limitations due to probe apertures often results in poor performances of the 3D mapping, due to the use of a Delay-And-Sum (DAS) classic beamformer, which results in strong artifacts and large main lobe sizes. In this paper, 3D-PAM is achieved by performing adaptive beamforming in the frequency domain in 3D, and using a random sparse apodization of a commercial matrix array driving only 256 elements among the 1024 available. It reduces the computation time and makes use of only one 256-channel research platform. Three beamformers have been implemented in 3D and in the Frequency Domain: The DAS beamformer, which corresponds to the beamformer used in previous 3D-PAM studies, the Robust Capon Beamformer (RCB), an adaptive algorithm widely used in 2D-PAM for its high performances, and the MidWay (MW) beamformer, an adaptive algorithm with a computation complexity equivalent to the one of DAS. These algorithms are evaluated both in simulations and experiments with a harmonic source at different positions, and are also applied to real cavitation signals. The results show that, in the case of matrix-arrays of small aperture such as generic commercial matrix probes, the DAS beamformer leads to large main lobe sizes, while adaptive beamformers largely improve the performances of the mapping. The low computation time and its parameter-free character make MW beamformer a good compromise for 3D-PAM applications. It thus appears that a random sparse apodization combined with adaptive beamforming is a good solution to achieve high performance 3D-PAM with manageable devices.

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Section: Adaptive Fd-mw Beamformermentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

3-D Passive Cavitation Imaging Using Adaptive Beamforming and Matrix Array Transducer With Random Apodization

Sivadon,

Varray,

Béra

et al. 2024

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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“…Recently, Schoen et al were able to draw similar assumptions for ASM reconstructions with simulated matrix arrays [ 22 ]. Furthermore, Sivadon et al numerically investigated random and regular sparse arrays for use in PAM, finding that a reduction from 1024 to 256 elements resulted in nearly identical images [ 23 ]. This is consistent with a simulation study from Acconcia et al, who suggested the modest number of 128 receiver elements to be sufficient for PAM [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

Optimization of 3D Passive Acoustic Mapping Image Metrics: Impact of Sensor Geometry and Beamforming Approach

Therre,

Fournelle,

Tretbar

2024

Sensors

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Three-dimensional passive acoustic mapping (PAM) with matrix arrays typically suffers from high demands on the receiving electronics and high computational load. In our study, we investigated, both numerically and experimentally, the influence of matrix array aperture size, element count, and beamforming approaches on defined image metrics. With a numerical Vokurka model, matrix array acquisitions of cavitation signals were simulated. In the experimental part, two 32 × 32 matrix arrays with different pitches and aperture sizes were used. After being reconstructed into 3D cavitation maps, defined metrics were calculated for a quantitative comparison of experimental and numerical data. The numerical results showed that the enlargement of the aperture from 5 to 40 mm resulted in an improvement of the full width at half maximum (FWHM) by factors of 6 and 13 (in lateral and axial dimension, respectively). A larger array sparsity influenced the point spread function (PSF) only slightly, while the grating lobe level (GLL) remained more than 12 dB below the main lobe. These results were successfully experimentally confirmed. To further reduce the GLL caused by array sparsity, we adapted a non-linear filter from optoacoustics for use in PAM. In combination with the delay, multiply, sum, and integrate (DMSAI) algorithm, the GLL was decreased by 20 dB for 64-element reconstructions, resulting in levels that were identical to the fully populated matrix reconstruction levels.

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Using Sparse Array for 3D Passive Cavitation Imaging

Cited by 2 publications

References 17 publications

3-D Passive Cavitation Imaging Using Adaptive Beamforming and Matrix Array Transducer With Random Apodization

3-D Passive Cavitation Imaging Using Adaptive Beamforming and Matrix Array Transducer With Random Apodization

Optimization of 3D Passive Acoustic Mapping Image Metrics: Impact of Sensor Geometry and Beamforming Approach

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