Image improvement in linear-array photoacoustic imaging using high resolution coherence factor weighting technique

Mozaffarzadeh, Moein; Makkiabadi, Bahador; Basij, Maryam; Mehrmohammadi, Mohammad

doi:10.1186/s42490-019-0009-9

Cited by 17 publications

(12 citation statements)

References 59 publications

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“…This is mainly due to the fact that EIBMV-DMAS uses the eigendecomposition of covariance matrix, resulting in a higher noise suppression. In addition, weighting methods ( [22], [23]) can be applied on the proposed beamformer in order to further improve the PA image. In this paper, the EIBMV-DMAS has been evaluated numerically and experimentally.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, for linear-array PAI, MV was combined with DMAS to improve the resolution of the DMAS while the sidelobes are retained [15], [20], [21]. Two modifications of Coherence Factor (CF) have been introduced for linear-array PAI, in order to have a lower sidelobes and higher resolution compared to the conventional CF [22], [23]. In this paper, a novel beamforming algorithm, namely Eigenspace-Based Minimum Variance-DMAS (EIBMV-DMAS), is introduced.…”

Section: Introductionmentioning

confidence: 99%

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Eigenspace-Based Minimum Variance Combined With Delay Multiply and Sum Beamformer: Application to Linear-Array Photoacoustic Imaging

Mozaffarzadeh

Mahloojifar

Periyasamy

et al. 2019

IEEE J. Select. Topics Quantum Electron.

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In Photoacoustic imaging, Delay-and-Sum (DAS) algorithm is the most commonly used beamformer. However, it leads to a low resolution and high level of sidelobes. Delay-Multiply-and-Sum (DMAS) was introduced to provide lower sidelobes compared to DAS. In this paper, to improve the resolution and sidelobes of DMAS, a novel beamformer is introduced using Eigenspace-Based Minimum Variance (EIBMV) method combined with DMAS, namely EIBMV-DMAS. It is shown that expanding the DMAS algebra leads to several terms which can be interpreted as DAS. Using the EIBMV adaptive beamforming instead of the existing DAS (inside the DMAS algebra expansion) is proposed to improve the image quality. EIBMV-DMAS is evaluated numerically and experimentally. It is shown that EIBMV-DMAS outperforms DAS, DMAS and EIBMV in terms of resolution and sidelobes. In particular, at the depth of 11 mm of the experimental images, EIBMV-DMAS results in about 113 d B and 50 d B sidelobe reduction, compared to DMAS and EIBMV, respectively. At the depth of 7 mm, for the experimental images, the quantitative results indicate that EIBMV-DMAS leads to improvement in Signal-to-Noise Ratio (SNR) of about 75% and 34%, compared to DMAS and EIBMV, respectively.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Eigenspace-Based Minimum Variance Combined With Delay Multiply and Sum Beamformer: Application to Linear-Array Photoacoustic Imaging

Mozaffarzadeh

Mahloojifar

Periyasamy

et al. 2019

IEEE J. Select. Topics Quantum Electron.

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“…where y DAS (k) is the output of beamformer, k is the time index, M is the number of array elements and x i (k) and ∆ i are the detected signals and the corresponding time delay for detector i, respectively. 44 To provide a more efficient beamformer and improve the quality of the reconstructed image, coherence factor (CF) can be used combined with DAS, which leads to sidelobe levels reduction and contrast enhancement. 45 CF, as a weighting procedure, is presented by:…”

Section: Methodsmentioning

confidence: 99%

Enhanced linear-array photoacoustic beamforming using modified coherence factor

et al. 2018

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Photoacoustic imaging (PAI) is a promising medical imaging modality providing the spatial resolution of ultrasound imaging and the contrast of optical imaging. For linear-array PAI, a beamformer can be used as the reconstruction algorithm. Delay-and-sum (DAS) is the most prevalent beamforming algorithm in PAI. However, using DAS beamformer leads to low-resolution images as well as high sidelobes due to nondesired contribution of off-axis signals. Coherence factor (CF) is a weighting method in which each pixel of the reconstructed image is weighted, based on the spatial spectrum of the aperture, to mainly improve the contrast. We demonstrate that the numerator of the formula of CF contains a DAS algebra and propose the use of a delay-multiply-and-sum beamformer instead of the available DAS on the numerator. The proposed weighting technique, modified CF (MCF), has been evaluated numerically and experimentally compared to CF. It was shown that MCF leads to lower sidelobes and better detectable targets. The quantitative results of the experiment (using wire targets) show that MCF leads to for about 45% and 40% improvement, in comparison with CF, in the terms of signal-to-noise ratio and full-width-half-maximum, respectively.

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“…[29][30][31][32][33][34] As a weighting method for the PA image reconstruction, modified coherence factor (MCF) and high resolution CF (HRCF) have been introduced, which result in a higher contrast and a better resolution compared to the conventional CF, respectively. 35,36 In this paper, we propose to use DMAS beamforming method for 3D PAI while its superiority for 2D imaging has been proved in the former publications. A 2D array of US transducers is used to perform the 3D PAI.…”

Section: Introductionmentioning

confidence: 95%

Three-dimensional photoacoustic tomography using delay multiply and sum beamforming algorithm

Paridar

Mozaffarzadeh

Mahloojifar

et al. 2018

Photons Plus Ultrasound: Imaging and Sensing 2018

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Photoacoustic imaging (PAI), is a promising medical imaging technique that provides the high contrast of the optical imaging and the resolution of ultrasound (US) imaging. Among all the methods, Three-dimensional (3D) PAI provides a high resolution and accuracy. One of the most common algorithms for 3D PA image reconstruction is delay-and-sum (DAS). However, the quality of the reconstructed image obtained from this algorithm is not satisfying, having high level of sidelobes and a wide mainlobe. In this paper, delay-multiply-andsum (DMAS) algorithm is suggested to overcome these limitations in 3D PAI. It is shown that DMAS algorithm is an appropriate reconstruction technique for 3D PAI and the reconstructed images using this algorithm are improved in the terms of the width of mainlobe and sidelobes, compared to DAS. Also, the quantitative results show that DMAS improves signal-to-noise ratio (SN R) and full-width-half-maximum (F W HM ) for about 25 dB and 0.2 mm, respectively, compared to DAS.

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Image improvement in linear-array photoacoustic imaging using high resolution coherence factor weighting technique

Cited by 17 publications

References 59 publications

Eigenspace-Based Minimum Variance Combined With Delay Multiply and Sum Beamformer: Application to Linear-Array Photoacoustic Imaging

Eigenspace-Based Minimum Variance Combined With Delay Multiply and Sum Beamformer: Application to Linear-Array Photoacoustic Imaging

Enhanced linear-array photoacoustic beamforming using modified coherence factor

Three-dimensional photoacoustic tomography using delay multiply and sum beamforming algorithm

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