The standard technique used by commercial medical ultrasound systems to form B-mode images is delay and sum (DAS) beamforming. However, DAS often results in limited image resolution and contrast, which are governed by the center frequency and the aperture size of the ultrasound transducer. A large number of elements leads to improved resolution but at the same time increases the data size and the system cost due to the receive electronics required for each element. Therefore, reducing the number of receiving channels while producing high quality images is of great importance. In this paper, we introduce a nonlinear beamformer called COnvolutional Beamforming Algorithm (COBA), which achieves significant improvement of lateral resolution and contrast. In addition, it can be implemented efficiently using the fast Fourier transform. Based on the COBA concept, we next present two sparse beamformers with closed form expressions for the sensor locations, which result in the same beam pattern as DAS and COBA while using far fewer array elements. Optimization of the number of elements shows that they require a minimal number of elements which is on the order of the square root of the number used by DAS. The performance of the proposed methods is tested and validated using simulated data, phantom scans and in vivo cardiac data. The results demonstrate that COBA outperforms DAS in terms of resolution and contrast and that the suggested beamformers offer a sizable element reduction while generating images with an equivalent or improved quality in comparison to DAS.
Considering a full array, several methods to improve image quality have been proposed. Adaptive beamformers improve resolution without sacrificing contrast by dynamically changing the receive aperture weights based on the received data statistics @cite_5 . The most common is Capon minimum variance (MV) beamforming @cite_20 which offers better contrast and resolution than DAS. However, it is difficult to apply in real-time due to the calculation of a covariance matrix and its inverse at each time instant. Its application to ultrasound imaging was studied extensively over the last decade and many improved versions of MV with reduced complexity have been proposed @cite_26 @cite_21 @cite_16 @cite_18 . Nilsen suggest a beamspace adaptive beamformer, BS-Capon, based on orthogonal beams formed in different directions @cite_22 . Jensen developed an adaptive beamformer called multi-beam Capon that is based on multibeam covariance matrices @cite_30 . Using similar concepts, Jensen and Austeng proposed a method called iterative adaptive approach (IAA) @cite_45 @cite_3 .
@cite_30 @cite_18 @cite_26 @cite_22 @cite_21 @cite_3 @cite_45 @cite_5 @cite_16 @cite_20
2135172025 2061156132 2312490702 2163333108 2066152866 2151246884 2330369165 2167589082 2114953841
Medical ultrasound imaging systems are often based on transmitting, and recording the backscatter from, a series of focused broadband beams with overlapping coverage areas. When applying adaptive beamforming, a separate array covariance matrix for each image sample is usually formed. The data used to estimate any one of these covariance matrices is often limited to the recorded backscatter from a single transmitted beam, or that of some adjacent beams through additional focusing at reception. We propose to form, for each radial distance, a single covariance matrix covering all of the beams. The covariance matrix is estimated by combining the array samples after a sequenced time delay and phase shift. The time delay is identical to that performed in conventional delay-and-sum beamforming. The performance of the proposed approach in conjunction with the Capon beamformer is studied on both simulated data of scenes consisting of point targets and recorded ultrasound phantom data from a specially adapted commercial scanner. The results show that the proposed approach is more capable of resolving point targets and gives better defined cyst-like structures in speckle images compared with the conventional delay-and-sum approach. Furthermore, it shows both an increased robustness to noise and an increased ability to resolve point-like targets compared with the more traditional per-beam Capon beamformer. Currently, the nonadaptive delay-and-sum (DAS) beamformer is used in medical ultrasound imaging. However, due to its data-independent nature, DAS leads to images with limited resolution and contrast. In this paper, an adaptive minimum variance (MV)-based beamformer that combines the MV and coherence factor (CF) weighting is introduced and adapted to medical ultrasound imaging. MV-adaptive beamformers can improve the image quality in terms of resolution and sidelobes by suppressing off-axis signals, while keeping onaxis ones. In addition, CF weighting can improve contrast and sidelobes by emphasizing the in-phase signals and reducing the out-of-phase ones. Combining MV and CF weighting results in simultaneous improvement of imaging resolution and contrast, outperforming both DAS and MV beamformers. In addition, because of the power of CF in reducing the focusing errors, the proposed method presents satisfactory robustness against sound velocity inhomogeneities, outperforming the regularized MV beamformer. The excellent performance of the proposed beamforming approach is demonstrated by several simulated examples. Capon Beamforming Algorithm involves calculating inverse covariance matrix. It's complex to calculate inverse matrix in engineering applications. On the basis of summary of distribution of covariance matrix elements, an improved inverse covariance matrix calculating method is proposed. Simulation shows that the improved algorithm achieves the same accuracy with original algorithm as reducing the computational complexity. (6 pages) Applying the Capon adaptive beamformer in medical ultrasound imaging results in enhanced resolution by improving the interference-suppressing capabilities of the array. This improvement comes at the expense of an increased computational complexity. We have investigated the application of a beamspace adaptive beamformer for medical ultrasound imaging, which can be used to achieve reduced computational complexity with performance comparable to that of the Capon beamformer. The idea behind beamspace beamforming is that, instead of using the spatial statistics of the elements in the array to differentiate between signals and interference, we use the spatial statistics of a set of orthogonal beams, which are formed in different directions. This represents a shift from element space to beamspace. Because the majority of interference in medical ultrasound imaging is constrained to a limited spatial interval due to the focused transmit beam, this latter space can be reduced to a dimension that is lower than that of element space. We show, using simulations and experimental data, that this dimension can be selected as low as 3 while still achieving performance comparable to its element space counterpart. Minimum variance (MV) beamforming has been studied for improving the performance of a diagnostic ultrasound imaging system. However, it is not easy for the MV beamforming to be implemented in a real-time ultrasound imaging system because of the enormous amount of computation time associated with the covariance matrix inversion. In this paper, to address this problem, we propose a new fast MV beamforming method that almost optimally approximates the MV beamforming while reducing the computational complexity greatly through dimensionality reduction using principal component analysis (PCA). The principal components are estimated offline from pre-calculated conventional MV weights. Thus, the proposed method does not directly calculate the MV weights but approximates them by a linear combination of a few selected dominant principal components. The combinational weights are calculated in almost the same way as in MV beamforming, but in the transformed domain of beamformer input signal by the PCA, where the dimension of the transformed covariance matrix is identical to the number of some selected principal component vectors. Both computer simulation and experiment were carried out to verify the effectiveness of the proposed method with echo signals from simulation as well as phantom and in vivo experiments. It is confirmed that our method can reduce the dimension of the covariance matrix down to as low as 2 × 2 while maintaining the good image quality of MV beamforming. This paper addresses the problem of source localization and waveform estimation in array processing applications. We present two nonparametric user parameter free algorithms, namely the iterative adaptive approach (IAA) and the maximum likelihood based IAA (IAA-ML). Both IAA and IAA-ML can work with arbitrary array geometries and uncorrelated as well as coherent sources. We extend IAA and IAA-ML to yield sparse results by using the Bayesian information criterion (BIC). Further improvements in performance can be achieved by employing the last step of the parametric RELAX algorithm initialized by the estimates of IAA and IAA-ML with BIC. Our simulations demonstrate that IAA and IAA-ML show better performance than the most competitive sparse signal representation approaches in the literature: M-FOCUSS, M-SBL and lscr1-SVD. Many medical ultrasound imaging systems are based on sweeping the image plane with a set of narrow beams. Usually, the returning echo from each of these beams is used to form one or a few azimuthal image samples. We model, for each radial distance, jointly the full azimuthal scanline. The model consists of the amplitudes of a set of densely placed potential reflectors (or scatterers), cf. sparse signal representation. To fit the model, we apply the iterative adaptive approach (IAA) on data formed by a sequenced time delay and phase shift. The performance of the IAA in combination with our time delayed and phase-shifted data is studied on both Field II simulated data of scenes consisting of point targets and hollow cystlike structures, and recorded ultrasound phantom data from a specially adapted commercial scanner. The results show that the proposed IAA is more capable of resolving point targets and gives better defined and more geometrically correct cyst-like structures in speckle images compared to the conventional delayand-sum (DAS) approach. Compared to a Capon beamformer, the IAA showed an improved rendering of cyst-like structures and a similar point-target resolvability. Unlike the Capon beamformer, the IAA has no user parameters and seems unaffected by signal cancellation. The disadvantage of the IAA is a high computational load. We have applied the minimum variance (MV) adaptive beamformer to medical ultrasound imaging and shown significant improvement in image quality compared to delay-and-sum (DAS). We demonstrate reduced main-lobe width and suppression of sidelobes on both simulated and experimental RF data of closely spaced wire targets, which gives potential contrast and resolution enhancement in medical images. The method is applied to experimental RF data from a heart phantom, in which we show increased resolution and improved definition of the ventricular walls. A potential weakness of adaptive beamformers is sensitivity to errors in the assumed wavefield parameters. We look at two ways to increase robustness of the proposed method; spatial smoothing and diagonal loading. We show that both are controlled by a single parameter that can move the performance from that of a MV beamformer to that of a DAS beamformer. We evaluate the sensitivity to velocity errors and show that reliable amplitude estimates are achieved while the mainlobe width and sidelobe levels are still significantly lower than for the conventional beam-former. For over thirty years adaptive beamforming (AB) algorithms have been applied in RADAR and SONAR signal processing. Higher resolution and contrast is attainable using those algorithms at the price of an increased computational load. In this paper we consider four beamformers (BFs): Frost BF, Duvall BF, SSB, and SPOC. These algorithms are well know in the RADAR SONAR literature. We have performed a series of simulations using ultrasound data to test the performance of those algorithms and compare them to the conventional, data independent, beamforming. Every algorithm was applied on single channel ultrasonic data that was generated using Field II. For a 32 element linear array operating at 5 MHz, beamplot results show that while the Duvall and SSB beamformers reduce sidelobes by roughly 20 dB, the sidelobes using the Frost algorithm rise by 23dB. The -6dB resolution is improved by 38 , 83 , and 43 in the case of Duvall, Frost, and SSB algorithms, respectively. In the case of SPOC, the beamplot shows a super-resolution peak with noise floor at -110 dB. Similar results were obtained for an array consisting of 64 elements.