arXiv subjects

Moein Mozaffarzadeh

Publications and source records attributed to Moein Mozaffarzadeh.

At least 19 recordsLinked to original sources

arXiv2024

Simultaneous Coherent and Displacement Compounding for 2D Noninvasive Carotid Strain Imaging: a Proof of Principle Study

Arteriosclerosis results from lipid buildup in artery walls, leading to plaque formation, and is a leading cause of death. Plaque rupture can cause blood clots that might lead to a stroke. Distinguishing plaque types is a challenge, but ultrasound elastography can help by assessing plaque composition based on strain values. Since the artery has a circular structure, an accurate axial and lateral displacement strategy is needed to derive the radial and circumferential strains. A high precision lateral displacement is challenging due to the lack of phase information in the lateral direction of the beamformed RF data. Previously, our group has developed a compounding technique in which the lateral displacement is estimated using tri-angulation of the axial displacement estimated from transmitting and beamforming ultrasound beams at +-20 degree. However, its applicability to in vivo is challenging due to the imaging noise and the low contrast of the arterial wall, caused by a single plane wave transmission. In this paper, we combine our displacement compounding with coherent compounding. Instead of transmitting a single plane wave, multiple plane waves are transmitted at certain angles with respect to the angle of the beamforming grids, and then the backscattered wavefronts are beamformed and coherently compounded on the center of the transmit beams (-20, +20 and 0 degree). ...

eess.SP↗

arXiv2020

Technical Considerations when using Verasonics Research Ultrasound Platform for Developing a Photoacoustic Imaging System

Photoacoustic imaging (PAI) is an emerging functional and molecular imaging technology that has attracted much attention in the past decade. Recently, many researchers have used the Vantage Verasonics research system for simultaneous ultrasound (US) and photoacoustic (PA) imaging. This was the motivation to write on the details of US/PA imaging system implementation and characterization using Verasonics platform. We describe the subtle details of US/PA imaging system setup, study the performance parameters of the system, and explain sequencing of the US/PA signal generation and signal amplification as well as the details required for efficient use of the hardware of the system and data processing protocols. We focused on linear-array based PAI due to its popularity and simple setup, as well as its high potential for clinical translatability. We have shown the sequencing of the US/PA signal generation, signal amplification, and related data processing protocols. A step-by-step guideline to develop and characterize PAI system using Vantage 128 has been presented. Some of the limitations of the vantage system are also listed. Photoacoustic imaging is a complement to the already established US imaging technique and may significantly increase its scope of application in diagnostic imaging and therapeutic monitoring. Combining with commercial medical US systems, the development of PAI can be accelerated by taking advantage of US image reconstruction and processing. With the information we presented in the body of this review and the four appendices, we described most of the experimental considerations one should know when working with the Vantage system for PAI tests.

physics.app-ph↗

arXiv2018

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

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 dB and 50 dB 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.

eess.SP↗

arXiv2018

An Efficient Nonlinear Beamformer Based on P^{th} Root of Detected Signals for Linear-Array Photoacoustic Tomography: Application to Sentinel Lymph Node Imaging

In linear-array transducer based photoacoustic (PA) imaging, B-scan PA images are formed using the raw channel PA signals. Delay-and-Sum (DAS) is the most prevalent algorithm due to its simple implementation, but it leads to low quality images. Delay-Multiply-and-Sum (DMAS) provides a higher image quality in comparison with DAS while it imposes a computational burden of O(M^2). In this work, we introduce a nonlinear (NL) beamformer for linear-array PA imaging, which uses the p^{th} root of the detected signals and imposes the complexity of DAS (O(M)). The proposed algorithm is evaluated numerically and experimentally (wire-target and in vivo sentinel lymph node (SLN) imaging), and the effects of the parameter p are investigated. The results show that the NL algorithm, using a root of p (NL_p), leads to lower sidelobes and higher signal-to-noise ratio (SNR) compared to DAS and DMAS, for (p > 2). The sidelobes level (for the wire-target phantom), at the depth of 11.4 mm, are about -31 dB, -52 dB, -52 dB, -67 dB, -88 dB and -109 dB, for DAS, DMAS, NL_2, NL_3, NL_4 and NL_5, respectively, indicating the superiority of the NL_p algorithm. In addition, the best value of p for SLN imaging is reported to be 12.

eess.SP↗

arXiv2018

Model-Based Photoacoustic Image Reconstruction using Compressed Sensing and Smoothed L0 Norm

Photoacoustic imaging (PAI) is a novel medical imaging modality that uses the advantages of the spatial resolution of ultrasound imaging and the high contrast of pure optical imaging. Analytical algorithms are usually employed to reconstruct the photoacoustic (PA) images as a result of their simple implementation. However, they provide a low accurate image. Model-based (MB) algorithms are used to improve the image quality and accuracy while a large number of transducers and data acquisition are needed. In this paper, we have combined the theory of compressed sensing (CS) with MB algorithms to reduce the number of transducer. Smoothed version of L0-norm (SL0) was proposed as the reconstruction method, and it was compared with simple iterative reconstruction (IR) and basis pursuit. The results show that S$\ell_0$ provides a higher image quality in comparison with other methods while a low number of transducers were. Quantitative comparison demonstrates that, at the same condition, the SL0 leads to a peak-signal-to-noise ratio for about two times of the basis pursuit.

eess.SP↗

arXiv2018

Three-Dimensional Photoacoustic Tomography using Delay Multiply and Sum Beamforming Algorithm

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-and-sum (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 (SNR) and full-width-half-maximum (FWHM) for about 25 dB and 0.2 mm, respectively, compared to DAS.

eess.SP↗

arXiv2018

Effects of Important Parameters Variations on Computing Eigenspace-Based Minimum Variance Weights for Ultrasound Tissue Harmonic Imaging

In recent years, the minimum variance (MV) beamforming has been widely studied due to its high resolution and contrast in B-mode Ultrasound imaging (USI). However, the performance of the MV beamformer is degraded at the presence of noise, as a result of the inaccurate covariance matrix estimation which leads to a low quality image. Second harmonic imaging (SHI) provides many advantages over the conventional pulse-echo USI, such as enhanced axial and lateral resolutions. However, the low signal-to-noise ratio (SNR) is a major problem in SHI. In this paper, Eigenspace-based minimum variance (EIBMV) beamformer has been employed for second harmonic USI. The Tissue Harmonic Imaging (THI) is achieved by Pulse Inversion (PI) technique. Using the EIBMV weights, instead of the MV ones, would lead to reduced sidelobes and improved contrast, without compromising the high resolution of the MV beamformer (even at the presence of a strong noise). In addition, we have investigated the effects of variations of the important parameters in computing EIBMV weights, i.e., K, L, and δ, on the resolution and contrast obtained in SHI. The results are evaluated using numerical data (using point target and cyst phantoms), and the proper parameters of EIBMV are indicated for THI.

eess.SP↗

arXiv2018

An Image Registration Based Technique for Noninvasive Vascular Elastography

Non-invasive vascular elastography is an emerging technique in vascular tissue imaging. During the past decades, several techniques have been suggested to estimate the tissue elasticity by measuring the displacement of the Carotid vessel wall. Cross correlation-based methods are the most prevalent approaches to measure the strain exerted in the wall vessel by the blood pressure. In the case of a low pressure, the displacement is too small to be apparent in ultrasound imaging, especially in the regions far from the center of the vessel, causing a high error of displacement measurement. On the other hand, increasing the compression leads to a relatively large displacement in the regions near the center, which reduces the performance of the cross correlation-based methods. In this study, a non-rigid image registration-based technique is proposed to measure the tissue displacement for a relatively large compression. The results show that the error of the displacement measurement obtained by the proposed method is reduced by increasing the amount of compression while the error of the cross correlationbased method rises for a relatively large compression. We also used the synthetic aperture imaging method, benefiting the directivity diagram, to improve the image quality, especially in the superficial regions. The best relative root-mean-square error (RMSE) of the proposed method and the adaptive cross correlation method were 4.5% and 6%, respectively. Consequently, the proposed algorithm outperforms the conventional method and reduces the relative RMSE by 25%.

eess.SP↗

arXiv2018

Double Minimum Variance Beamforming Method to Enhance Photoacoustic Imaging

One of the common algorithms used to reconstruct photoacoustic (PA) images is the non-adaptive Delay-and-Sum (DAS) beamformer. However, the quality of the reconstructed PA images obtained by DAS is not satisfying due to its high level of sidelobes and wide mainlobe. In contrast, adaptive beamformers, such as minimum variance (MV), result in an improved image compared to DAS. In this paper, a novel beamforming method, called Double MV (D-MV) is proposed to enhance the image quality compared to the MV. It is shown that there is a summation procedure between the weighted subarrays in the output of the MV beamformer. This summation can be interpreted as the non-adaptive DAS beamformer. It is proposed to replace the existing DAS with the MV algorithm to reduce the contribution of the off-axis signals caused by the DAS beamformer between the weighted subarrays. The numerical results show that the proposed technique improves the full-width-half-maximum (FWHM) and signal-to-noise ratio (SNR) for about 28.83 μm and 4.8 dB in average, respectively, compared to MV beamformer. Also, quantitative evaluation of the experimental results indicates that the proposed D-MV leads to 0.15 mm and 1.96 dB improvement in FWHM and SNR, in comparison with MV beamformer.

eess.SP↗

arXiv2018

Photoacoustic Image Formation Based on Sparse Regularization of Minimum Variance Beamformer

Delay-and-Sum (DAS) is the most common algorithm used in photoacoustic (PA) image formation. However, this algorithm results in a reconstructed image with a wide mainlobe and high level of sidelobes. Minimum variance (MV), as an adaptive beamformer, overcomes these limitations and improves the image resolution and contrast. In this paper, a novel algorithm, named modified-sparse-MV (MS-MV) is proposed in which a L1-norm constraint is added to the MV minimization problem after some modifications, in order to suppress the sidelobes more efficiently, compared to MV. The added constraint can be interpreted as the sparsity of the output of the MV beamformed signals. Since the final minimization problem is convex, it can be solved efficiently using a simple iterative algorithm. The numerical results show that the proposed method, MS-MV beamformer, improves the signal-to-noise (SNR) about 19.48 dB, in average, compared to MV. Also, the experimental results, using a wire-target phantom, show that MS-MV leads to SNR improvement of about 2.64 dB in comparison with the MV.

eess.SP↗

arXiv2018

Linear-Array Photoacoustic Imaging Using Minimum Variance-Based Delay Multiply and Sum Adaptive Beamforming Algorithm

In Photoacoustic imaging (PA), 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 novel 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 dB, 18 dB 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.

eess.SP↗

arXiv2018

Enhanced Linear-array Photoacoustic Beamforming using Modified Coherence Factor

Photoacoustic imaging (PAI) is a promising medical imaging modality providing the spatial resolution of ultrasound (US) imaging and the contrast of pure optical imaging. For linear-array PAI, a beamformer has to 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 along with significant effects of the 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 improve the contrast. In this paper, it has been shown that the numerator of the formula of CF contains a DAS algebra, and it was proposed to use the delay-multiply-and-sum (DMAS) 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, and 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.

physics.med-ph↗

arXiv2018

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.

eess.SP↗

arXiv2018

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 level of sidelobes and low noise suppression. In this paper, a novel beamforming algorithm is introduced based on the expansion of DMAS formula. It is shown that there is a DAS algebra inside the expansion, and it is proposed to use 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 about 25% lower level of sidelobes compared to DMAS. Moreover, the introduced method leads to 23%, 22% and 43% improvement in Signal-to-Noise Ratio, Full-Width-Half-Maximum and Contrast Ratio, respectively, in comparison with DMAS beamformer.

eess.SP↗

arXiv2018

Image Enhancement and Noise Reduction Using Modified Delay-Multiply-and-Sum Beamformer: Application to Medical Photoacoustic Imaging

Photoacoustic imaging (PAI) is an emerging biomedical imaging modality capable of providing both high contrast and high resolution of optical and UltraSound (US) imaging. When a short duration laser pulse illuminates the tissue as a target of imaging, tissue induces US waves and detected waves can be used to reconstruct optical absorption distribution. Since receiving part of PA consists of US waves, a large number of beamforming algorithms in US imaging can be applied on PA imaging. Delay-and-Sum (DAS) is the most common beamforming algorithm in US imaging. However, make use of DAS beamformer leads to low resolution images and large scale of off-axis signals contribution. To address these problems a new paradigm namely Delay-Multiply-and-Sum (DMAS), which was used as a reconstruction algorithm in confocal microwave imaging for breast cancer detection, was introduced for US imaging. Consequently, DMAS was used in PA imaging systems and it was shown this algorithm results in resolution enhancement and sidelobe degrading. However, in presence of high level of noise the reconstructed image still suffers from high contribution of noise. In this paper, a modified version of DMAS beamforming algorithm is proposed based on DAS inside DMAS formula expansion. The quantitative and qualitative results show that proposed method results in more noise reduction and resolution enhancement in expense of contrast degrading. For the simulation, two-point target, along with lateral variation in two depths of imaging are employed and it is evaluated under high level of noise in imaging medium. Proposed algorithm in compare to DMAS, results in reduction of lateral valley for about 19 dB followed by more distinguished two-point target. Moreover, levels of sidelobe are reduced for about 25 dB.

eess.SP↗

arXiv2018

Medical Photoacoustic Beamforming Using Minimum Variance-Based Delay Multiply and Sum

Delay-and-Sum (DAS) beamformer is the most common beamforming algorithm in Photoacoustic imaging (PAI) due to its simple implementation and real time imaging. However, it provides poor resolution and high levels of sidelobe. A new algorithm named Delay-Multiply-and-Sum (DMAS) was introduced. Using DMAS leads to lower levels of sidelobe compared to DAS, but resolution is not satisfying yet. In this paper, a novel beamformer is introduced based on the combination of Minimum Variance (MV) adaptive beamforming and DMAS, so-called Minimum Variance-Based DMAS (MVB-DMAS). It is shown that expanding the DMAS equation leads to some terms which contain a DAS equation. It is proposed to use MV adaptive beamformer instead of existing DAS inside the DMAS algebra expansion. MVB-DMAS is evaluated numerically compared to DAS, DMAS and MV and Signal-to-noise ratio (SNR) metric is presented. It is shown that MVB-DMAS leads to higher image quality and SNR for about 13 dB, 3 dB and 2 dB in comparison with DAS, DMAS and MV, respectively.

eess.SP↗

arXiv2017

Image Improvement in Linear-Array Photoacoustic Imaging using High Resolution Coherence Factor Weighting Technique

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). As a weighting algorithm in linear-array PAI, it was 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. The higher performance of HRCF was 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. Moreover, the contrast ratio at the depth of 20 mm has been improved about 62 % and 21 % by HRCF, compared to DAS+CF and MV+CF, respectively

eess.SP↗

arXiv2017

Eigenspace-Based Minimum Variance Adaptive Beamformer Combined with Delay Multiply and Sum: Experimental Study

Delay and sum (DAS) is the most common beamforming algorithm in linear-array photoacoustic imaging (PAI) as a result of its simple implementation. However, it leads to a low resolution and high sidelobes. Delay multiply and sum (DMAS) was used to address the incapabilities of DAS, providing a higher image quality. However, the resolution improvement is not well enough compared to eigenspace-based minimum variance (EIBMV). In this paper, the EIBMV beamformer has been combined with DMAS algebra, called EIBMV-DMAS, using the expansion of DMAS algorithm. The proposed method is used as the reconstruction algorithm in linear-array PAI. EIBMV-DMAS is experimentally evaluated where the quantitative and qualitative results show that it outperforms DAS, DMAS and EIBMV. The proposed method degrades the sidelobes for about 365 %, 221 % and 40 %, compared to DAS, DMAS and EIBMV, respectively. Moreover, EIBMV-DMAS improves the SNR about 158 %, 63 % and 20 %, respectively.

eess.SP↗