2-D Minimum Variance Based Plane Wave Compounding with Generalized Coherence Factor in Ultrafast 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.

Plane wave ultrasound imaging using compressive sensing and minimum variance beamforming

Coherent plane-wave compounding (CPWC) is a widely used technique in medical ultrasound imaging due to its high frame rate property. It is well-known that increasing the plane waves leads to improving the image quality. However, the image quality still needs to be further improved in CPWC. In this regard, a variety of methods have been proposed. In this paper, a new compressive sensing (CS) based approach is introduced with the combination of the adaptive minimum variance (MV) algorithm to further improve the image quality in terms of resolution and contrast. In the proposed method, which is called the CS-based MV technique, the CS method is used in the receive direction to produce the beamformed data for each plane wave. Then, the MV algorithm is performed in the plane wave transmit angle direction to coherently compound the images and improve the resolution. Moreover, to deal with the high computational complexity and also, the needing for high memory space during the CS method implementation, an approximation is considered which results in considerably reduced computational burden and memory space. The results obtained from the simulated point targets show that the proposed method leads to resolution improvement for about 71%, 5.5%, and 37% respectively, compared to DAS, DAS+MV, and CS+DAS beamformers. Also, the quantitative results obtained from the experimental contrast phantom in plane wave imaging challenge in medical ultrasound (PICMUS) data show a 3.02 dB, 2.57 dB, and 2.24 dB improvement of the contrast ratio metric using the proposed method compared to DAS, DAS+MV, and double-MV methods, respectively, indicating the good performance of the proposed method in image quality improvement.

Adaptive scaled coherence factor for ultrasound pixel-based beamforming

Synthetic aperture (SA) ultrasound imaging can obtain images with high-resolution owing to its ability to dynamically focus in both directions. The signal-to-noise ratio (SNR) of SA imaging is poor because the pulse energy using one array element is quite low. Thus, the SA method with bidirectional pixel-based focusing (SA-BiPBF) was previously proposed as a solution to this challenge. However, using the nonadaptive delay-and-sum (DAS) beamforming still limits its imaging performance. This study proposes an adaptive scaled coherence factor (AscCF) for SA-BiPBF to further boost the image quality. The AscCF exploits generalized coherence factor (GCF) to measure the signal coherence to adaptively adapt the parameters in SNR estimation rather than fixed ones. Comparisons were made with several other weighting techniques by performing simulations and experiments for performance evaluation. Results confirm that AscCF applied to SA-BiPBF offers a good image contrast while reservation of the speckle pattern. AscCF achieves maximal improvements of contrast ratio (CR) by 48.5% and 47.76 % compared with scaled coherence factor (scCF), respectively in simulation and experiment. Simultaneously, the maximum of improvements in speckle signal-to-noise ratio (sSNR) of AscCF are 11.28 % and 20.01 % upon scCF in simulation and experiment, respectively. From the in vivo result, it also appears a potential for AscCF to act in clinical situations to better detect lesion and retain speckle pattern.

Joint Generalized Coherence Factor and Minimum Variance Beamformer for Synthetic Aperture Ultrasound Imaging

The delay-and-sum (DAS) beamformer is the most commonly used method in medical ultrasound imaging. Compared with the DAS beamformer, the minimum variance (MV) beamformer has an excellent ability to improve lateral resolution by minimizing the output of interference and noise power. However, it is hard to overcome the tradeoff between satisfactory lateral resolution and speckle preservation performance due to the fixed subarray length of covariance matrix estimation. In this study, a new approach for MV beamforming with adaptive spatial smoothing is developed to address this problem. In the new approach, the generalized coherence factor (GCF) is used as a local coherence detection tool to adaptively determine the subarray length for spatial smoothing, which is called adaptive spatial-smoothed MV (AMV). Furthermore, another adaptive regional weighting strategy based on the local signal-to-noise ratio (SNR) and GCF is devised for AMV to enhance the image contrast, which is called GCF regional weighted AMV (GAMV). To evaluate the performance of the proposed methods, we compare them with the standard MV by conducting the simulation, in vitro experiment, and the in vivo rat mammary tumor study. The results show that the proposed methods outperform MV in speckle preservation without an appreciable loss in lateral resolution. Moreover, GAMV offers excellent performance in image contrast. In particular, AMV can achieve maximal improvements of speckle signal-to-noise ratio (SNR) by 96.19% (simulation) and 62.82% (in vitro) compared with MV. GAMV achieves improvements of contrast-to-noise ratio by 27.16% (simulation) and 47.47% (in vitro) compared with GCF. Meanwhile, the losses in lateral resolution of AMV are 0.01 mm (simulation) and 0.17 mm (in vitro) compared with MV. Overall, this indicates that the proposed methods can effectively address the inherent limitation of the standard MV in order to improve the image quality.

High Resolution, High Contrast Beamformer Using Minimum Variance and Plane Wave Nonlinear Compounding with Low Complexity

The plane wave compounding (PWC) is a promising modality to improve the imaging quality and maintain the high frame rate for ultrafast ultrasound imaging. In this paper, a novel beamforming method is proposed to achieve higher resolution and contrast with low complexity. A minimum variance (MV) weight calculated by the partial generalized sidelobe canceler is adopted to beamform the receiving array signals. The dimension reduction technique is introduced to project the data into lower dimensional space, which also contributes to a large subarray length. Estimation of multi-wave receiving covariance matrix is performed and then utilized to determine only one weight. Afterwards, a fast second-order reformulation of the delay multiply and sum (DMAS) is developed as nonlinear compounding to composite the beamforming output of multiple transmissions. Simulations, phantom, in vivo, and robustness experiments were carried out to evaluate the performance of the proposed method. Compared with the delay and sum (DAS) beamformer, the proposed method achieved 86.3% narrower main lobe width and 112% higher contrast ratio in simulations. The robustness to the channel noise of the proposed method is effectively enhanced at the same time. Furthermore, it maintains a linear computational complexity, which means that it has the potential to be implemented for real-time response.

Multi-Operator Minimum Variance Adaptive Beamforming Algorithms Accelerated With GPU

The goal of this work is to design high-resolution, high-contrast and robust MV adaptive beamforming algorithms, which are also implemented in real-time frame rate. Multi-operator optimization is introduced into MV adaptive beamforming in this work to propose a multi-operator MV adaptive beamforming algorithmic optimization framework. Based on the proposed algorithmic optimization framework, the algorithm optimization can be either conducted by activating a single optimization operator, or conducted by activating multiple optimization operators. The multi-operator MV (MOMV) adaptive beamforming algorithms are then derived from this framework. Moreover, in order to promote the real-time imaging capability of MOMV beamforming, a GPU-based parallel acceleration framework is proposed along with the algorithmic optimization framework by exploring the image-level coarse-grained parallelization and pixel-level fine-grained parallelization. GPU computing resource allocation strategy and memory access strategy are both explored to better design the acceleration framework. Comprehensive quantitative simulation evaluations and qualitative in vivo experiments of imaging performance are studied, and the results demonstrate that the proposed MOMV adaptive beamforming algorithms significantly improve the imaging performance as compared with other MV beamforming algorithms, which have high resolution, high contrast, good robustness, and real-time imaging capability with thousands of acceleration speedup. Furthermore, the MOMV beamforming algorithm without eigen-decomposition and projection optimization operator achieves much higher beamforming frame rate with little downgrade of image quality as compared with the MOMV beamforming algorithm with all optimization operators.

A Mixed Transmitting-Receiving Beamformer With a Robust Generalized Coherence Factor: Enhanced Resolution and Contrast

Adaptive beamforming has been widely studied for ultrasound imaging over the past few decades. The minimum variance (MV) and generalized coherence factor (GCF) approaches have been validated as effective methods. However, the MV method had a limited contribution to contrast improvement, while the GCF method suffered from severe speckle distortion in previous studies. In this article, a novel ultrasound beamforming approach based on MV and GCF beamformers is proposed to enhance the spatial resolution and contrast in synthetic aperture (SA) ultrasound imaging. First, the MV optimization problem is conceptually redefined by minimizing the total power of the transmitting and receiving outputs. Estimation of the covariance matrices in transmit and receive apertures is carried out and then utilized to determine adaptive weighting vectors. Second, a data-compounding method, viewed as a spatial low-pass filter, is introduced to the GCF method to optimize the spatial spectrum of echo signals and obtain better performance. Robust principal component analysis (RPCA) processing is additionally employed to obtain the final output. Simulation, experimental, and in vivo studies are conducted on different data sets. Relative to the traditional delay-and-sum (DAS) beamformer, mean improvements in the full-width at half-maximum and contrast ratio of 89% and 94%, respectively, are achieved. Thus, considerable enhancement of the spatial resolution and contrast is obtained by the proposed method. Moreover, the proposed method performs better in terms of the computational complexity. In summary, the proposed scheme effectively enhances ultrasound imaging quality.

A dynamic generalized coherence factor for side lobe suppression in ultrasound imaging

Coherence-based weighting techniques have been widely studied to weight beamsummed data to improve image quality in ultrasound imaging. Although generalized coherence factor (GCF) enhances the robustness of coherence factor (CF) with preserved speckle pattern by including some incoherent components, the side lobe suppression performance is insufficient due to constant cut-off frequency M₀. To address this problem, we introduced in this paper a dynamic GCF method, referred to as DGCF-C, based on the amplitude standard deviation and the convolution output of aperture data. The cut-off frequency is adaptively selected for GCF at each imaging point using the amplitude standard deviation of aperture data. Moreover, the convolution output of aperture data is used to calculate the dynamic GCF. The proposed method is evaluated in simulation and tissue-mimicking phantom studies. The image quality was analyzed in terms of resolution, contrast ratio (CR), generalized contrast-to-noise ratio (GCNR), speckle signal-to-noise ratio (sSNR), and signal-to-noise ratio (SNR). The results demonstrate that DGCF-C (M_max=2) achieves mean resolution improvements of 35.1% in simulation, and 32.6% in experiment, compared with GCF (M₀=1). Moreover, DGCF-C (M_max=4) outperforms GCF (M₀=2) with an average GCNR improvement of 13.5% and an average sSNR improvement of 15.2%, which indicates the better-preservation of speckle.