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

Coherent multi-transducer ultrasound (CoMTUS) imaging enables the use of multiple arrays as one large effective aperture. This yields images with enlarged field-of-view, improved resolution, and higher SNR. However, creating a large but discontinuous effective aperture increases the grating and side lobe levels and generates cross-talk artifacts from the direct transmissions between arrays. These additional challenges can degrade the contrast of the images obtained through the classic Delay and Sum (DAS) beamforming algorithm. This study investigates the use of an alternative nonlinear beamforming algorithm, the Delay Multiply and Sum (DMAS), to improve contrast and reduce cross-talk artifacts in CoMTUS. We adapted the original DMAS beamforming algorithm to the specific effective aperture of CoMTUS and tested its performance in a coherent dual-array system for both 2-D and 3-D imaging. The comparison between images obtained through CoMTUS DMAS and DAS was investigated by both 2-D and 3-D simulations and experiments. DMAS was shown effective to lower the side lobes and the noise floor, thus improving the detectability of cystic lesions.

In this research work, a novel framework is proposed as an efficient successor to traditional imaging methods for breast cancer detection in order to decrease the computational complexity. In this framework, the breast is divided into segments in an iterative process and in each iteration, the one having the most probability of containing tumor with lowest possible resolution is selected by using suitable decision metrics. After finding the smallest tumor-containing segment, the resolution is increased in the detected tumor-containing segment, leaving the other parts of the breast image with low resolution. Our framework is applied on the most common used beamforming techniques, such as delay and sum (DAS) and delay multiply and sum (DMAS) and according to simulation results, our framework can decrease the computational complexity significantly for both DAS and DMAS without imposing any degradation on accuracy of basic algorithms. The amount of complexity reduction can be determined manually or automatically based on two proposed methods that are described in this framework.

Although conventional delay-and-sum (DAS) beam-forming is the most common beamforming method to reconstruct ultrasound images, alternative approaches might provide better image quality. As such, delay multiply and sum (DMAS) beam-forming has recently been proposed. In this paper, we investigate the use of a more efficient approach for the DMAS method together with high frame rate (HFR) imaging and coherent compounding. The proposed beamformer is evaluated on datasets acquired by computer simulation and experimentally in vivo. Imaging results demonstrate that it provides better clutter and noise rejection, and enhances the image quality in terms of resolution and contrast as compared with DAS and DMAS methods, without compromising the computational complexity.

In the recent years, various studies have been performed on adaptive beamforming especially for medical ultrasound imaging. In this paper, at first we applied the adaptive Minimum Variance (MV) beamforming to the simulated data derived from Field II. In contrast to conventional beamforming (DAS), MV adaptive beamforming can significantly improve the imaging resolution while retaining the contrast of the image. Then we combined MV with Coherence Factor (CF), by which the resolution and contrast of the medical ultrasound images are simultaneously increased. We implemented these beamformers in the MATLAB and survey the authenticity of them, then fulfil the beamformers with the Visual Studio codes. Those had the same results but execution times of Visual Studio codes are about 13 times lower than other. The major parameters which affect on the image quality are number of elements, height of elements, and sampling frequency. We investigated the effect of these parameters on the image quality by some simulated examples. The results illustrated that the reduction of the number of elements from M=96 to 24 and sampling frequency from 100MHz to 20MHz will degrade quality of the images. Nevertheless, the results of MV beamformer with M=24 and Fs=20MHz are comparable to DAS with M=96 and Fs=100MHz. We illustrated that if height of elements is equal or greater than 5mm, the off-plane targets have a negligible effect on the image quality but if the height of elements is in the same order of the width the off-plane targets have a significant role on the image quality. It should be taken into consideration in using of planar probs.

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.

Photoacoustic imaging (PAI), is a promising medical imaging technique that\nprovides the high contrast of the optical imaging and the resolution of\nultrasound (US) imaging. Among all the methods, Three-dimensional (3D) PAI\nprovides a high resolution and accuracy. One of the most common algorithms for\n3D PA image reconstruction is delay-and-sum (DAS). However, the quality of the\nreconstructed image obtained from this algorithm is not satisfying, having high\nlevel of sidelobes and a wide mainlobe. In this paper, delay-multiply-and-sum\n(DMAS) algorithm is suggested to overcome these limitations in 3D PAI. It is\nshown that DMAS algorithm is an appropriate reconstruction technique for 3D PAI\nand the reconstructed images using this algorithm are improved in the terms of\nthe width of mainlobe and sidelobes, compared to DAS. Also, the quantitative\nresults show that DMAS improves signal-to-noise ratio (SNR) and\nfull-width-half-maximum (FWHM) for about 25 dB and 0.2 mm, respectively,\ncompared to DAS.\n

Delay-and-sum (DAS) beamformers, when applied to photoacoustic (PA) image reconstruction, produce strong sidelobes due to the absence of transmit focusing. Consequently, DAS PA images are often severely degraded by strong off-axis clutter. For preclinical in vivo cardiac PA imaging, the presence of these noise artifacts hampers the detectability and interpretation of PA signals from the myocardial wall, crucial for studying blood-dominated cardiac pathological information and to complement functional information derived from ultrasound imaging. In this article, we present PA subaperture processing (PSAP), an adaptive beamforming method, to mitigate these image degrading effects. In PSAP, a pair of DAS reconstructed images is formed by splitting the received channel data into two complementary nonoverlapping subapertures. Then, a weighting matrix is derived by analyzing the correlation between subaperture beamformed images and multiplied with the full-aperture DAS PA image to reduce sidelobes and incoherent clutter. We validated PSAP using numerical simulation studies using point target, diffuse inclusion and microvasculature imaging, and in vivo feasibility studies on five healthy murine models. Qualitative and quantitative analysis demonstrate improvements in PAI image quality with PSAP compared to DAS and coherence factor weighted DAS (DAS CF ). PSAP demonstrated improved target detectability with a higher generalized contrast-to-noise (gCNR) ratio in vasculature simulations where PSAP produces 19.61% and 19.53% higher gCNRs than DAS and DAS CF , respectively. Furthermore, PSAP provided higher image contrast quantified using contrast ratio (CR) (e.g., PSAP produces 89.26% and 11.90% higher CR than DAS and DAS CF in vasculature simulations) and improved clutter suppression.

Recently, adaptive beamforming methods have been successfully applied to medical ultrasound imaging, resulting in significant improvement in image quality compared to non-adaptive delay-and-sum (DAS) beamformer. Most of the adaptive beamformers presented in the ultrasound imaging literature are based on the minimum variance (MV) beamformer which can improve the imaging resolution while retaining the contrast. The main objective of this research is to present a beamformer which could improve the imaging resolution and contrast, at the same time. In this paper, we have applied the eigenspace-based MV (EIBMV) beamformer to medical ultrasound imaging and shown the simultaneous improvement in imaging resolution and contrast. EIBMV beamformer utilizes the eigenstructure of the covariance matrix to enhance the performance of the MV beamformer. The weight vector of the EIBMV is found by projecting the obtained MV weight vector onto a vector subspace constructed from the eigenstructure of the covariance matrix. EIBMV beamformer shows a significant reduction in the sidelobe levels while representing the same mainlobe width as the MV. Specifically, in the point targets phantom, the proposed method decreases the sidelobes about 22 dB and 29 dB compared to MV and DAS, respectively. Furthermore, the proposed method increases the contrast of simulated cyst phantom about 8.6 dB and 14.1 dB and also improves the contrast-to-noise ratio about 31% and 61%, compared to MV and DAS, respectively.

Ultrasound-induced cavitation is a fundamental physical phenomenon for histotripsy, lithotripsy, and local drug delivery. Magnetic nanoparticles (MNPs) enable magnetically assisted cancer therapy (Magnetic Drug Targeting, MDT) and can be used as cavitation nuclei. While MDT has shown promising results in preclinical studies, there remains a need for a real-time monitoring system. Here, we introduce a GPU-accelerated passive cavitation mapping (PCM) method that pairs a third-degree delay-multiply-and-sum beamformer (DMAS3) with coherence-factor weighting (CFwDMAS3) to enhance spatial localization and suppress off-axis incoherent emissions. We evaluated the approach in a flow-channel phantom perfused with lauric-acid–coated MNPs under pulsed focused ultrasound, and complemented the experiments with simulations based on Vokurka’s bubble-dynamics model. Reconstructions using Delay and Sum (DAS), Delay Multiply and Sum (DMAS), and DMAS3, each with and without CF weighting, were assessed using ROC metrics (AUROC, sensitivity, specificity) and precision measures (PPV, F1-score). CF weighting consistently produced sharper maps and higher specificity; CFwDMAS3 achieved the best overall balance and supported real-time visualization (approximately 0.1 s per frame) on a GPU. These results indicate that CFwDMAS3 PCM is a practical solution for monitoring MNP-mediated cavitation during MDT and related therapeutic ultrasound procedures.

In photoacoustic imaging (PAI), a delay-and-sum (DAS) beamforming reconstruction algorithm is widely used due to its ease of implementation and fast execution. However, it is plagued by issues such as high sidelobe artifacts and low contrast, that significantly hinder the ability to differentiate various structures in the reconstructed images. In this study, we propose an adaptive weighting factor called spatial coherence mean-to-standard deviation factor (scMSF) in DAS, which is extended into the spatial frequency domain. By combining scMSF with a minimum variance (MV) algorithm, the clutter level is reduced, thereby enhancing the image contrast. Quantitative results obtained from the phantom experiment demonstrate that our proposed method improves contrast ratio (CR) by 30.15 dB and signal-to-noise ratio (SNR) by 8.62 dB compared to DAS while also improving full-width at half maxima (FWHM) by 56%. From the in-vivo experiments, the scMSF-based reconstruction image exhibits a higher generalized contrast-to-noise ratio (gCNR), indicating improved target detectability with a 25.6% enhancement over DAS and a 22.5% improvement over MV.

Undoubtedly, photoacoustic tomography (PAT) is a promising technique unveiling physiological information in the biomedical imaging field. However, targeting optically/acoustically non-uniform biological tissues and receiving signals with finite aperture/band-limited ultrasonic transducers have been practical limitations that hinder the clinical application of PAT. This study closely analyzes the effects of four main limiting factors named limited view (LV), limited bandwidth (LB), lossy medium (LM), and heterogeneous medium (HM) and compared the adverse effects given from the four factors. First, radiofrequency (RF) data was generated assuming a realistic situation using k-Wave, a MATLAB-based wave propagation-simulating tool. A single 4 mm-sized circular target was used, and physical parameters of the detector array were designed referring to a commercial linear probe (GE9LD, General Electric). Supposing double-aperture, full-band signal reception from the lossless, homogeneous medium as a baseline, RF data were acquired under a successive accumulation of the limiting factors in order of LV, LB, LM, and HM. Second, the obtained RF datasets were reconstructed with 7 representative beamforming algorithms; delay and sum (DAS), delay multiply and sum, pth root DAS, minimum variance, filtered back-projection, frequency-domain reconstruction, and time reversal. Among obtained images, common graphical features for each limiting factor were specified. Lastly, we quantitatively assessed the similarity between the image and the actual target with peak signal-to-noise ratio and structural similarity as performance indices. As a result, the characteristic features from each limiting factor were commonly identified on the reconstructed image, and the performance indices sequentially degraded following the sequence of practical conditions.

In this paper, the adaptive, minimum variance (MV) beam-former is applied to medical ultrasound imaging. The significant resolution and contrast gain provided by the adaptive, minimum variance (MV) beamformer, introduces the possibility of plane wave (PW) ultrasound imaging. Data is obtained using Field II and a 7 MHz, 128-elements, linear array transducer with lambda/2-spacing. MV is compared to the conventional delay-and-sum (DS) beamformer with Boxcar and Hanning weights. Furthermore, the PW images are compared to the a conventional ultrasound image, obtained from a linear scan sequence. The four approaches, {Linear Scan, DS Boxcar, DS Hanning, MV}, have full width at half maximum of {0.82, 0.71, 1.28, 0.12} mm and peak side-lobe levels of {-40.1,-16.8,-34.4,-57.0} dB.

A novel sophisticated form of DMAS beamformer: Application to breast cancer detection

In contrast with the common delay-and-sum (DAS) beamformer, adaptive beamformers can be used to increase the resolution of acoustic measurement systems without sacrificing sidelobe levels. The non-linear nature of adaptive beamforming methods may cause changes to measured scattered amplitude distributions. For example, large differences in speckle statistics have been observed in some cases when using the adaptive minimum variance distortionless response (MVDR) beamformer in medical ultrasound, unless preventative measures were implemented. Low complexity adaptive (LCA) beamforming techniques have shown similar, but smaller, differences. Recently, experiments have been conducted in the Oslofjord, Norway, to determine whether low-complexity adaptive (LCA) beamforming techniques can improve the performance of bathymetric echosounders. When using LCA with these types of sonar systems, it is important to understand any changes to the backscatter statistics, as these changes may affect, e.g., seafloor classification methods. In this talk we will present comparisons between backscatter statistics estimated using a weighted delay-and-sum beamformer and those estimated using a low complexity adaptive beamformer. We will discuss any differences seen, possible mechanisms causing these differences and potential remedies. [Research supported by The Norwegian Research Council and Kongsberg Maritime. The first author is employed in Kongsberg Maritime.]

The minimum variance (MV) adaptive beamformer has been widely proposed for matched‐field processing because it provides a means of suppressing ambiguous beampattern ‘‘sidelobes.’’ In this paper, the performance of three robust MV methods is evaluated using vertical array data from the Mediterranean Sea collected by the NATO SACLANT Centre north of Elba [D. F. Gingras and P. Gerstoft, 3234 (1994)]. The three MV methods considered are (1) the reduced MV method (RMV) [C. L. Byrne et al., 2493–2502 (1990)], (2) the MV method with neighborhood location constraints (MV‐NLC) [H. Schmidt et al., 1851–62 (1990)], and (3) the MV method with environmental perturbation constraints (MV‐EPC) [J. Krolik, 1408–19 (1992)]. Real data results indicate that each approach has different merits. Projecting the data onto the reduced space defined by the modal eigenfunctions as in the RMV is necessary to avoid instabilities caused by highly correlated noise. Using neighboring location constraints as in the MV‐NLC reduces the need to finely sample the ambiguity surface. And using constraints derived from the second‐order statistics of the signal wavefront averaged over an ensemble of perturbed environmental parameters as in the MV‐EPC increases robustness to environmental mismatch. [Work supported by ONR.]