Low complex subspace minimum variance beamformer for medical ultrasound imaging

Ultrafast Plane Wave Imaging Using Tensor Completion-Based Minimum Variance Algorithm

OBJECTIVE

Coherent plane wave compounding (CPWC) imaging is an efficient technique in high-frame-rate ultrasound imaging. To improve the image quality obtained from the CPWC, the adaptive minimum variance (MV) algorithm can be used. However, the high computational complexity of this algorithm negatively affects the frame rate. In other words, achieving a high frame rate and high-quality features simultaneously remains a challenge in medical ultrasound imaging. The aim of the work described here was to develop an algorithm to tackle this challenge and improve the frame rate while preserving the good quality of the resulting image.

METHODS

A tensor completion (TC)-based MV algorithm is proposed to simultaneously improve the frame rate and image quality in CPWC. In the proposed method, the MV algorithm is applied to a limited number of pixels in the beamforming grid. Then, the appropriate values are assigned to the remaining unprocessed pixels by using the TC algorithm. The proposed algorithm speeds up the beamforming process, and consequently, improves the frame rate.

RESULTS

The computational complexity of the proposed TC-based MV algorithm is reduced compared with that of the conventional MV algorithm while the good quality of this algorithm is preserved. The results indicate that, in particular, by processing 40% of the beamforming grid using the MV beamformer followed by the TC algorithm, a reconstructed image comparable to that in the case in which the MV algorithm is performed on the full beamforming grid is obtained; the difference between the contrast-to-noise ratio evaluation metric between these two cases is about 0.16 dB for the experimental-resolution phantom. Also, the resulting images obtained from the MV algorithm and the TC-based MV method have the same resolution, indicating that the TC-based MV algorithm can successfully achieve the quality of the MV algorithm with a lower computational complexity.

CONCLUSION

The TC-based MV algorithm is proposed in CPWC with the goal of improving frame rate and image quality. Qualitative and quantitative results reveal that by use of the proposed algorithm, the quality of the reconstructed image will be comparable to that of the conventional MV algorithm, and the frame rate will be improved.

Correlation-based modified delay-multiply-and-sum beamforming applied to medical ultrasound imaging

BACKGROUND AND OBJECTIVE

Recently, the Filtered Delay-Multiply-and-Sum (F-DMAS) beamformer was successfully applied to Ultrasound Imaging (UI), improving the image quality compared to the conventional data-independent Delay-and-Sum (DAS) beamformer. However, its reconstructed images lead to restricted resolution, contrast, and dark regions in the speckle. Various beamformers based on F-DMAS were proposed to mitigate these issues; some improved resolution and contrast at the expense of more dark regions; others reduced the dark points with lower contrast than the F-DMAS beamformer. This study aims to propose a novel beamformer, improving resolution and contrast while reducing dark points in the speckle.

METHODS

This study proposes a modified version of the F-DMAS beamformer, using two modifications to compensate for the aforesaid trade-off. Firstly, coupled signals' Correlation Coefficient (CC) was calculated and compared to a threshold value. The multiplications were applied only to the high-correlated (those whose CC is higher than the threshold value) signals. Secondly, a new Modified Coherence Factor (MCF) was applied to the high-correlated signals. Then, these two new beamformers were combined to reach a novel beamformer entitled "Modified DMAS (MDMAS)."

RESULTS

The performance of MDMAS was evaluated using simulating Point-Spread-Function, Cyst phantom, the experimental geabr dataset, and an in vivo dataset. Moreover, we evaluated the performance of the MDMAS beamformer quantitatively. Full-width-half-maximum (FWHM), contrast-ratio (CR), contrast-to-noise-ratio (CNR), speckle signal-to-noise-ratio (sSNR), and generalized-CNR (gCNR) were assessed.

CONCLUSIONS

This paper modified the conventional F-DMAS beamformer by adaptively multiplying signals. Then, CF was implemented on high correlated signals (MCF) and combined with the adaptive beamformer to compensate for the poor contrast. Results highlight that the MDMAS beamformer outperforms F-DMAS in terms of resolution and contrast without compromising the speckle from the dark region artifact.

A Low-complexity Minimum-variance Beamformer Based on Orthogonal Decomposition of the Compounded Subspace

Minimum-variance (MV) beamforming, as a typical adaptive beamforming method, has been widely studied in medical ultrasound imaging. This method achieves higher spatial resolution than traditional delay-and-sum (DAS) beamforming by minimizing the total output power while maintaining the desired signals. However, it suffers from high computational complexity due to the heavy calculation load when determining the inverse of the high-dimensional matrix. Low-complexity MV algorithms have been studied recently. In this study, we propose a novel MV beamformer based on orthogonal decomposition of the compounded subspace (CS) of the covariance matrix in synthetic aperture (SA) imaging, which aims to reduce the dimensions of the covariance matrix and therefore reduce the computational complexity. Multiwave spatial smoothing is applied to the echo signals for the accurate estimation of the covariance matrix, and adaptive weight vectors are calculated from the low-dimensional subspace of the original covariance matrix. We conducted simulation, experimental and in vivo studies to verify the performance of the proposed method. The results indicate that the proposed method performs well in maintaining the advantage of high spatial resolution and effectively reduces the computational complexity compared with the standard MV beamformer. In addition, the proposed method shows good robustness against sound velocity errors.

Eigenspace-based minimum variance beamformer combined with sign coherence factor: Application to linear-array photoacoustic imaging

Photoacoustic (PA) imaging combining the advantages of high resolution of ultrasound imaging and high contrast of optical imaging provides images with good quality. PA imaging often suffers from disadvantages such as clutter noises and decreased signal-to-noise-ratio at higher depths. One studied method to reduce clutter noises is to use weighting factors such as coherence factor (CF) and its modified versions that improve resolution and contrast of images. In this study, we combined the Eigen-space based minimum variance (EIBMV) beamformer with the sign coherence factor (SCF) and show the ability of these methods for noise reduction when they are used in combination with each other. In addition, we compared the proposed method with delay-and-sum (DAS) and minimum variance (MV) beamformers in simulated and experimental studies. The simulation results show that the proposed EIBMV-SCF method improves the SNR about 94 dB, 87.65 dB, and 62.29 dB compared to the DAS, MV, and EIBMV, respectively, and the corresponding improvements were 79.37/34.43 dB, 77.25/26.96 dB, and 33.19/25.56 dB in the ex vivo/in vivo experiments.

Subspace-Based Blood Power Spectral Capon Combined with Wiener Postfilter to Provide a High-Quality Velocity Waveform with Low Mathematical Complexity

In Doppler analysis, the power spectral density (PSD), which accounts for the axial velocity distribution of the blood scatterers, is estimated. The conventional spectral estimator is Welch's method, which suffers from frequency leakage at small observation window length. The performance of adaptive techniques such as blood power Capon (BPC) has been promising at the cost of higher computation complexity. Reducing the computational complexity while retaining the benefits of BPC would be necessary for real-time implementation. The purpose of the work described here was to investigate whether it is possible to decrease the computation load in BPC and still obtain acceptable results. The computation complexity in BPC is owing primarily to the matrix inversion required for computing the PSD estimate. We here propose the subspace blood power Capon technique, which employs a data covariance matrix with reduced number of rows in estimation of the weight vector. In maximum velocity estimation in the spectra, the signal noise slope intersection envelop estimator that makes use of the integrated power spectrum is employed. The evaluations are made based on both simulated and in vivo data. The results indicate that it is possible to reduce the order of complexity to almost 12.25% at the cost of 2.31% and 2.24% increases in the relative standard deviation and relative bias of the estimates. Moreover, the Wiener post-filter as a post-weighting factor, which will be multiplied by the final weight vector of the spectral estimator, estimates the power of the desired signal and the power of the interference plus noise to improve the contrast. The proposed estimator has exhibited a promising performance at beam-to-flow angles of 45°, 60° and 75°. Furthermore, the robust performance of the proposed estimator against variation in the flow rate is also documented.

A low-complexity and robust minimum variance beamformer for ultrasound imaging systems using beamspace dominant mode rejection

Minimum variance beamformer (MVB) has a high computational complexity that is mainly due to the inversion of an L×L covariance matrix involved during weight vector estimation, where L is the length of the subarray. In this work an attempt is made to reduce the computational complexity as well as increase the robustness against signal mismatch. The computational complexity is reduced by projecting the element-space data on to beamspace domain and then using dominant mode rejection on the beamspace covariance matrix (BCM). This reduces the dimension of covariance matrix and also eliminates the matrix inversion thereby reducing the computational complexity. Further, a closeness factor is introduced to determine the interference components that have to be suppressed, leading to increased robustness against signal mismatch. Performance of the proposed method has been evaluated on both simulated and experimental datasets. Results indicate that the proposed beamformer has a lateral resolution of 0.07 mm and a contrast resolution of 0.80, which are comparable to that of MVB which has a lateral and contrast resolution of 0.10 mm and 0.78, that too with a 12-fold reduction in computational complexity. The robustness of the peak magnitude estimate and spatial resolution of the beamformer with respect to error in estimation of sound velocity in the medium have also been evaluated. The variation in lateral resolution of proposed beamformer and MVB is approximately 1.21 mm and 1 mm. Further, the proposed beamformer has a maximum deviation in peak magnitude estimate of 0.7 dB whereas that of MVB is 2.5 dB, thus indicating the increased robustness of proposed method in peak magnitude estimate. Overall, the proposed beamformer has a 12-fold lower computational complexity compared to MVB with additional flexibility to increase the robustness against error in sound velocity.

High Resolution Minimum Variance Beamformer With Low Complexity in Medical Ultrasound Imaging

Although the minimum variance beamformer (MVB) shows a significant improvement in resolution and contrast in medical ultrasound imaging, its high computational complexity is a major problem in a real-time imaging system. Therefore, it seems necessary to propose a new method with a lower computational complexity that preserves the advantages of the MVB. In this paper, the MVB was implemented with a partial generalized sidelobe canceler (GSC) with a blocking matrix based on our previous study, which projected the incoming signals to a lower dimensional space. The partial GSC separated the weight vector into one fixed and one adaptive weight, whereby the optimization could be performed with lower complexity on the adaptive part. In addition, this dimension reduction allowed us to increase the length of the subarray when using a spatial smoothing method, which was used to decorrelate the incoming signals. The subarray length was limited to half the length of the full array size in the ordinary MVB, while the proposed beamformer could cross over this limitation. The results demonstrated that the point spread function of the proposed beamformer was about 6.3 times narrower than the classic MVB, while the contrast was almost saved. These results were achieved with linear computational complexity by the proposed method, while it was cubic for the MVB. For a sample scenario, the proposed method needed only 1.8% of the required ops of the MVB.

Minimum variance based fusion of fundamental and second harmonic ultrasound imaging: Simulation and experimental study

Harmonic imaging is widely used in clinical ultrasound due to its higher resolution in comparison with fundamental mode. However, the low amplitude of harmonic components in this imaging method is a crucial problem, resulting in a high sensitivity to noise, while the fundamental imaging is more robust against noise. To exploit the benefits of both the fundamental and harmonic imaging, we propose a minimum variance (MV)-based adaptive combination of fundamental and harmonic images. The performance of the proposed mixing-together MV (MTMV) beamformer is evaluated on simulated and experimental RF data. The results of the simulated point targets show that in the regions near of point targets, where the desire signals exist, the proposed MTMV beamformer mostly follows the MV-beamformed harmonic image to retain a better resolution. In the regions far from the point targets, where there is just noise, it follows the MV-beamformed fundamental image to benefit from more robustness. Also, the results of the simulated and experimental cyst phantoms indicate that MTMV reduces the background noise level and improves the contrast without compromising the high resolution of the MV-beamformed harmonic image. In the simulated cyst phantom, in comparison to DAS (fundamental), DAS (harmonic), MV (fundamental), MV (harmonic), and wavelet fusion, the image contrast ratio (CR) is increased, in average, about 5.2 dB, 3.5 dB, 1.5 dB, 3.6 dB, and 2.8 dB, respectively. The contrast-to-noise ratio (CNR) is significantly improved; about 59%, 53%, 41%, 37%, and 24%, respectively. In the experimental cyst phantom, these relative improvements are about 6.6 dB, 3.5 dB, 4.2 dB, 2.1 dB, and 3.8 dB for CR, and about 64%, 52%, 25%, 33%, and 33%, for CNR, respectively.

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

Background

In Photoacoustic imaging (PAI), the most prevalent beamforming algorithm is delay-and-sum (DAS) due to its simple implementation. However, it results in a low quality image affected by the high level of sidelobes. Coherence factor (CF) can be used to address the sidelobes in the reconstructed images by DAS, but the resolution improvement is not good enough, compared to the high resolution beamformers such as minimum variance (MV). In this paper, it is proposed to use high-resolution-CF (HRCF) weighting technique in which MV is used instead of the existing DAS in the formula of the conventional CF.

Results

The higher performance of HRCF is proved numerically and experimentally. The quantitative results obtained with the simulations show that at the depth of 40 mm, in comparison with DAS+CF and MV+CF, HRCF improves the full-width-half-maximum of about 91% and 15% and the signal-to-noise ratio about 40% and 14%, respectively.

Conclusion

Proposed method provides a high resolution along with a low level of sidelobes for PAI.

Iterative Minimum Variance Beamformer with Low Complexity for Medical Ultrasound Imaging

Minimum variance beamformer (MVB) improves the resolution and contrast of medical ultrasound images compared with delay and sum (DAS) beamformer. The weight vector of this beamformer should be calculated for each imaging point independently, with a cost of increasing computational complexity. The large number of necessary calculations limits this beamformer to application in real-time systems. A beamformer is proposed based on the MVB with lower computational complexity while preserving its advantages. This beamformer avoids matrix inversion, which is the most complex part of the MVB, by solving the optimization problem iteratively. The received signals from two imaging points close together do not vary much in medical ultrasound imaging. Therefore, using the previously optimized weight vector for one point as initial weight vector for the new neighboring point can improve the convergence speed and decrease the computational complexity. The proposed method was applied on several data sets, and it has been shown that the method can regenerate the results obtained by the MVB while the order of complexity is decreased from O(L³) to O(L²).

Singular Value Decomposition-Based Generalized Side Lobe Canceller Beamforming Method for Ultrasound Imaging

In the previous studies, eigenspace-based minimum variance (ESBMV) algorithms were proposed, however, the quality of the algorithm will degrade in low signal to noise occasions. In this study, a singular value decomposition generalized side lobe canceller (SVD-GSC) beamforming method based on the GSC is proposed. The sample covariance matrix is eigendecomposed, and a kind of further SVD is introduced to establish the noise space and the signal space, respectively. After that, the weighting vectors acquired by GSC are projected into the left singular space of the desired signal space. The performance of the proposed method is investigated by both of the simulation and experimental data. And the sound velocity error is also investigated in this paper. The imaging quality of point targets are measured by the [Formula: see text][Formula: see text]dB main lobe width and the peak side lobe (PSL). The contrast ratio (CR) is introduced to describe the quality of cyst phantom. Both the point targets and cyst phantom simulation show that the proposed SVD-GSC performs better in terms of spatial resolution, PSL and CR. Furthermore, the proposed method has a stronger robustness than the traditional GSC.

An Adaptive Synthetic Aperture Method Applied to Ultrasound Tissue Harmonic Imaging

In recent years, the minimum variance (MV) beamformer has been highly regarded since it provides high resolution and contrast in B-mode ultrasound imaging compared with nonadaptive delay-and-sum (DAS) beamformer. However, the performance of MV beamformer is degraded in the presence of the noise due to inaccurate estimation of the covariance matrix resulting in low-quality images. The conventional tissue harmonic imaging (THI) offers multiple advantages over conventional pulse-echo ultrasound imaging, including enhanced contrast resolution and improved axial and lateral resolutions, but low signal-to-noise ratio (SNR) is a major problem facing this imaging method, which uses a fixed transmit focus and dynamic receive focusing (DRF). In this paper, a synthetic aperture method based on the virtual source, namely, bidirectional pixel-based focusing (BiPBF), has been combined with the MV beamformer and then applied to second-harmonic ultrasound imaging. The main objective is suppressing the noise level to enhance the performance of the MV beamformer in the harmonic imaging, especially in lower and deeper depths where the SNR is low. In addition, combining the BiPBF and MV weighting results in simultaneous improvement in imaging resolution and contrast, in comparison with the conventional methods: DRF (DAS), BiPBF (DAS), and DRF (MV). The performance of the proposed method is evaluated on simulated and experimental RF data. The THI is achieved using the pulse-inversion technique. The results of the simulated wire phantom demonstrate that the proposed beamformer can achieve the best lateral resolution, along different depths, compared with DRF (DAS), BiPBF (DAS), and DRF (MV) methods. The results of the simulated and experimental cyst phantoms show that the new beamformer improves the contrast ratio (CR) and contrast-to-noise ratio (CNR) of the resulting images. In results of simulated cyst phantom, in average, the new beamformer improves the CR and CNR of the cyst about (7.4 dB, 49%), (3.2 dB, 16%), and (5 dB, 26%) compared with DRF (DAS), BiPBF (DAS), and DRF (MV), respectively. In results of experimental cyst phantom, these relative improvements are about (4.2 dB, 22%), (1.7 dB, 7%), and (2.6 dB, 15%). In addition, BiPBF (MV) method offers improved edge definition of cysts in comparison with the other methods.

A low complexity minimum variance beamformer for ultrasound imaging using dominant mode rejection

In recent years, high resolution adaptive minimum variance-based beamformers have been successfully applied to medical ultrasound imaging to improve its resolution and contrast, simultaneously. However, these improvements come at the cost of much more computational complexity in comparison to the non-adaptive delay-and-sum beamformer. The computational overhead mainly results from the L×L covariance matrix inversion needed for computation of the adaptive weights, the complexity of which is cubic with the subarray size, O(L³). In medical ultrasound imaging with focusing on the imaging point, we have a limited number of dominant modes and there is no need for the full matrix inversion. Based on this idea, we have investigated the application of the dominant mode rejection (DMR) adaptive beamformer for medical ultrasound imaging, which uses only some largest dominant modes to approximate the covariance matrix in dominant subspace. We show, using simulated and experimental data, that this subspace dimension can be selected as low as two resulting in significant computational complexity reduction while still achieving performance comparable to that of the minimum variance beamformer.

An efficient subarray average delay multiply and sum beamformer algorithm in ultrasound imaging

Beamformer plays an important role in medical ultrasound imaging systems. The delay multiply and sum (DMAS) beamformer achieves better performance in contrast and resolution compared with the delay and sum (DAS) beamformer, but suffers from higher computational complexity and partial energy loss. The higher computational complexity mainly arises from the multiply and geometric average operation, which needs (N²-N)/2 computations at every point, where N denotes the number of array elements. The partial energy loss, mainly due to the autocorrelation component of the echo signals, has been neglected in the DMAS beamformer. In this paper, we propose a subarray average delay multiply and sum (SA-DMAS) beamformer which is combined with subarray average technique of covariance matrix and DMAS beamformer. This will lower the computational complexity, while keeping the side lobe suppressing property of DMAS. The main idea of the proposed method is adding autocorrelation component of the echo signals to DMAS, and converting the expression in covariance matrix form. The subarray average technique is used to estimate the covariance matrix of the echo signals. The field II simulation of point targets and cyst phantoms was used to prove the performance of the proposed method. An RF data experiment was applied to support the feasibility and validity of our method. The simulation and experimental results show that our method has a lower computational complexity as O(9/2L²) , where L denotes the sub-array size, and has equivalent performance like the MV and DMAS beamformer.

Double-Stage Delay Multiply and Sum Beamforming Algorithm Applied to Ultrasound Medical Imaging

In ultrasound (US) imaging, delay and sum (DAS) is the most common beamformer, but it leads to low-quality images. Delay multiply and sum (DMAS) was introduced to address this problem. However, the reconstructed images using DMAS still suffer from the level of side lobes and low noise suppression. Here, a novel beamforming algorithm is introduced based on expansion of the DMAS formula. We found that there is a DAS algebra inside the expansion, and we proposed use of the DMAS instead of the DAS algebra. The introduced method, namely double-stage DMAS (DS-DMAS), is evaluated numerically and experimentally. The quantitative results indicate that DS-DMAS results in an approximately 25% lower level of side lobes compared with DMAS. Moreover, the introduced method leads to 23%, 22% and 43% improvement in signal-to-noise ratio, full width at half-maximum and contrast ratio, respectively, compared with the DMAS beamformer.

Linear-array photoacoustic imaging using minimum variance-based delay multiply and sum adaptive beamforming algorithm

In photoacoustic imaging, delay-and-sum (DAS) beamformer is a common beamforming algorithm having a simple implementation. However, it results in a poor resolution and high sidelobes. To address these challenges, a new algorithm namely delay-multiply-and-sum (DMAS) was introduced having lower sidelobes compared to DAS. To improve the resolution of DMAS, a beamformer is introduced using minimum variance (MV) adaptive beamforming combined with DMAS, so-called minimum variance-based DMAS (MVB-DMAS). It is shown that expanding the DMAS equation results in multiple terms representing a DAS algebra. It is proposed to use the MV adaptive beamformer instead of the existing DAS. MVB-DMAS is evaluated numerically and experimentally. In particular, at the depth of 45 mm MVB-DMAS results in about 31, 18, and 8 dB sidelobes reduction compared to DAS, MV, and DMAS, respectively. The quantitative results of the simulations show that MVB-DMAS leads to improvement in full-width-half-maximum about 96%, 94%, and 45% and signal-to-noise ratio about 89%, 15%, and 35% compared to DAS, DMAS, MV, respectively. In particular, at the depth of 33 mm of the experimental images, MVB-DMAS results in about 20 dB sidelobes reduction in comparison with other beamformers.

A Fast and Robust Beamspace Adaptive Beamformer for Medical Ultrasound Imaging

Minimum variance beamformer (MVB) increases the resolution and contrast of medical ultrasound imaging compared with nonadaptive beamformers. These advantages come at the expense of high computational complexity that prevents this adaptive beamformer to be applied in a real-time imaging system. A new beamspace (BS) based on discrete cosine transform is proposed in which the medical ultrasound signals can be represented with less dimensions compared with the standard BS. This is because of symmetric beampattern of the beams in the proposed BS compared with the asymmetric ones in the standard BS. This lets us decrease the dimensions of data to two, so a high complex algorithm, such as the MVB, can be applied faster in this BS. The results indicated that by keeping only two beams, the MVB in the proposed BS provides very similar resolution and also better contrast compared with the standard MVB (SMVB) with only 0.44% of needed flops. Also, this beamformer is more robust against sound speed estimation errors than the SMVB.

Double-Stage Delay Multiply and Sum Beamforming Algorithm: Application to Linear-Array Photoacoustic Imaging

Photoacoustic imaging (PAI) is an emerging medical imaging modality capable of providing high spatial resolution of Ultrasound (US) imaging and high contrast of optical imaging. Delay-and-Sum (DAS) is the most common beamforming algorithm in PAI. However, using DAS beamformer leads to low resolution images and considerable contribution of off-axis signals. A new paradigm namely delay-multiply-and-sum (DMAS), which was originally used as a reconstruction algorithm in confocal microwave imaging, was introduced to overcome the challenges in DAS. DMAS was used in PAI systems and it was shown that this algorithm results in resolution improvement and sidelobe degrading. However, DMAS is still sensitive to high levels of noise, and resolution improvement is not satisfying. Here, we propose a novel algorithm based on DAS algebra inside DMAS formula expansion, double stage DMAS (DS-DMAS), which improves the image resolution and levels of sidelobe, and is much less sensitive to high level of noise compared to DMAS. The performance of DS-DMAS algorithm is evaluated numerically and experimentally. The resulted images are evaluated qualitatively and quantitatively using established quality metrics including signal-to-noise ratio (SNR), full-width-half-maximum (FWHM) and contrast ratio (CR). It is shown that DS-DMAS outperforms DAS and DMAS at the expense of higher computational load. DS-DMAS reduces the lateral valley for about 15 dB and improves the SNR and FWHM better than 13% and 30%, respectively. Moreover, the levels of sidelobe are reduced for about 10 dB in comparison with those in DMAS.

Combined phase screen aberration correction and minimum variance beamforming in medical ultrasound

In recent years, applying adaptive beamforming to ultrasound imaging improves image quality in terms of resolution and contrast. One of the best adaptive beamformers in this field is the minimum variance (MV) beamformer which presents better resolution and edge definition compared to the traditional delay-and-sum (DAS) beamformer. However, in real situations, sound-velocity inhomogeneities cause phase aberration which leads to ambiguity in targets' location and degradation in resolution. This effect is a fundamental obstacle to utilize advantages of MV beamformer, although, in aberrating medium MV beamformer results in better performance compared to DAS. In this paper, two different levels of phase screens have been applied to simulate aberrator layers located close to the transducer. Also, prior to beamforming process, a conventional correction technique based on phase screen model is used. Simulations are performed in majority resolution of MV which has the lowest robustness. The results demonstrate that applying this correction method can retrieve the efficiency of the MV beamformer. Moreover, the method improves the performance of the MV in both terms of resolution and contrast. As corrected MV achieved at least 22% improvement in sidelobe reduction and 24% increase in contrast to noise ratio (CNR) with respect to the DAS corrected data. Also, according to experimental dataset 17% enhancement in CNR is yielded by MV.