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

Plane wave ultrasound imaging using compressive sensing and minimum variance beamforming

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.

Plane-wave imaging (PWI) is a best way to improve the frame rate in ultrasound imaging. However, due to the lack of transmit focus in PWI, the image quality is poor. A novel coherent plane-wave compounding (CPWC) was proposed to handle this issue, but the increased plane waves (PWs) reduced the frame rate. Thus, we combine a novel Double-Stage Delay Multiply and Sum (DS-DMAS) beamformer, which is shown to be better to improve the image quality, with plane-wave compounding to analyze the performance in terms of resolution and contrast. The simulation tests are carried out to show that applying DS-DMAS beamformer can achieve better results even if the number of transmitted PWs are decreased.

BACKGROUND: The coherent plane wave compounding (CPWC) is a promising technique to enhance the imaging quality while maintaining the high frame rate in the plane wave ultrasound imaging. Recently, the spatial-coherence-based method has been specially designed to process echo matrix required by the minimum variance (MV) method.OBJECTIVE: In this paper, a novel beamforming method that integrates the submatrix-spatial-coherence-based MV with the sign coherence factor (SCF) is proposed to further improve the imaging quality.METHOD: The submatrix smoothing technique is modified to smooth and de-correlate signals of the receiving array dimension. Then, the SCF is used to modify the input vector of the beamformer, which can reduce side lobe noises with almost no increase in the amount of calculation. Simulation, phantom, in vivo, and sound velocity error experiments have been performed to verify the superiority of the proposed beamformer.RESULTS: The imaging results show that the proposed approach performs better in the imaging resolution and contrast compared to the traditional CPWC method.CONCLUSION: The robustness of the proposed method is enhanced, and the over-suppression phenomenon can be alleviated, which is a phenomenon that occurs in the original spatial-coherence and SCF methods.

Plane wave (PW) imaging is fast, but limited by poor imaging quality. Coherent PW compounding (CPWC) improves image quality but decrease frame rate. In this study, we propose a modified CycleGAN model that combines a residual attention module with a space-frequency dual-domain discriminator, termed RADD-CycleGAN, to rapidly reconstruct high-quality ultrasound images. To enhance the ability to reconstruct image details, we specially design a process of hybrid dynamic and static channel selection followed by the frequency domain discriminator. The low-quality images are generated by the 3-angle CPWC, while the high-quality images are generated as real images (ground truth) by the 75-angle CPWC. The training set includes unpaired images, whereas the images in the test set are paired to verify the validity and superiority of the proposed model. Finally, we respectively design ablation and comparison experiments to evaluate the model performance. Compared with the basic CycleGAN, our proposed method reaches a better performance, with a 7.8% increase in the peak signal-to-noise ratio and a 22.2% increase in the structural similarity index measure. The experimental results show that our method achieves the best unsupervised reconstruction from low quality images in comparison with several state-of-the-art methods.

Synthetic aperture focusing based on planar waves also known as coherent plane wave compounding (CPWC) was introduced to reduce the diffraction spreading effect of ultrasound at depths (Song et al. US Patent 6736780, 2004). CPWC has been widely used in shear wave elasticity imaging due to its capability of ultrafast frame rate (Montaldo, et al. IEEE TUFFC, vol. 56, 2009). However, CPWC based on unfocused (plane waves) or defocused Tx beams has an inherent limitation in imaging wider field of view (FOV) due to the use of sampling angle in transmitting steered ultrasound wave. The sampling angle causes an unwanted side-lobe artifact if the sampling interval of angle is not small enough to cancel the artifact.

Coherent plane wave compounding (CPWC) reconstructs transmit focusing by coherently summing several low-resolution plane-wave (PW) images from different transmit angles to improve its image resolution and quality. The high frame rate of CPWC imaging enables a much larger number of Doppler ensembles such that the Doppler estimation of blood flow becomes more reliable. Due to the unfocused PW transmission, however, one major limitation of the Doppler estimation in CPWC imaging is the relatively low signal-to-noise ratio (SNR). Conventionally, the Doppler power is estimated by a zero-lag autocorrelation which reduces the noise variance, but not the noise level. A higher-lag autocorrelation method such as the first-lag (R(1)) power Doppler image has been developed to take advantage of the signal coherence in the temporal direction for suppressing uncorrelated random noises. In this paper, we propose a novel Temporal Multiply-and-Sum (TMAS) power Doppler detection method to further improve the noise suppression of the higher-lag method by modulating the signal coherence among the temporal correlation pairs in the higher-lag autocorrelation with a tunable pt value. Unlike the adaptive beamforming methods which demand for either receive–channel–domain or transmit–domain processing to exploit the spatial coherence of the blood flow signal, the proposed TMAS power Doppler can share the routine beamforming architecture with CPWC imaging. The simulated results show that when it is compared to the original R(1) counterpart, the TMAS power Doppler image with the pt value of 2.5 significantly improves the SNR by 8 dB for the cross-view flow velocity within the Nyquist rate. The TMAS power Doppler, however, suffers from the signal decorrelation of the blood flow, and thus, it relies on not only the pt value and the flow velocity, but also the flow direction relative to the geometry of acoustic beam. The experimental results in the flow phantom and in vivo dataset also agree with the simulations.

In recent years, multiple-line acquisition (MLA) has been introduced to increase frame rate in cardiac ultrasound medical imaging. However, this method induces blocklike artifacts in the image. One approach suggested, synthetic transmit beamforming (STB), involves overlapping transmit beams which are then interpolated to remove the MLA blocking artifacts. Independently, the application of minimum variance (MV) beamforming has been suggested in the context of MLA. We demonstrate here that each approach is only a partial solution and that combining them provides a better result than applying either approach separately. This is demonstrated by using both simulated and real phantom data, as well as cardiac data. We also show that the STB-compensated MV beamfomer outperforms single-line acquisition (SLA) delay- and-sum in terms of lateral resolution.

Minimum variance (MV) beamformers have been introduced in medical ultrasound imaging to improve image quality. In most cases, the MV beamformers have been investigated in terms of resolution improvement. However, the contrast-to-noise ratio (CNR) is also a clinically important metrics and gathers attention recently. In this study, we examined the diagonal loading parameter σ in MV beamforming and determined its appropriate value by evaluating image quality evaluation metrics including CNR. In order to further improve the image quality, a method for determining the value of σ based on the difference in statistical properties of received ultrasonic echo signals was also investigated. The phantom experimental results showed that the proposed method achieved a better CNR than the conventional MV beamformer while keeping resolution significantly better than that in delay-and-sum beamforming.

Image quality improvement in single plane-wave imaging using deep learning

In order to enhance the robustness and contrast in the minimum variance (MV) beamformer, adaptive diagonal loading method was proposed. The conventional diagonal loading technique has already been used in the MV beamformer, but has the drawback that its level is specified by predefined parameter and without consideration of input-data. To alleviate this problem, the level of diagonal loading was computed appropriately and automatically from the given data by shrinkage method in the proposed adaptive diagonal loaded beamformer. The performance of the proposed beamformer was tested on the simulated point target and cyst phantom was obtained using Field II. In the point target simulation, it is shown that the proposed method has higher lateral resolution than the conventional delay-and-sum beamformer and could be more robust in estimating the amplitude peak than the MV beamformer when acoustic velocity error exists. In the cyst phantom simulation, the proposed beamformer has shown that it achieves an improvement in contrast ratio and without distorting the edges of cyst.

In order to improve the efficiency of the eigenspace-based minimum variance (ESBMV) algorithm, a low-complexity minimum variance combined with eigenvalue decomposition (IBMV) algorithm is proposed. Firstly, the echo signals are transformed into beam domain by the discrete cosine transformation. Then, the eigenvalue decomposition of the sample covariance matrix is used to extract the signal subspace. The largest eigenvalue and its corresponding eigenvector can be extracted and other eigenvalues are taken the same value when the trace of sample covariance matrix remains unchanged. By these ways, the inverse operation of matrix can be simplified to the multiplication of vectors. In order to verify the validity of the proposed algorithm, Field II is introduced to test point targets and the cyst phantom. Experimental results indicate that the proposed IBMV with robustness to noises is obviously more efficient than ESBMV. Besides, the imaging quality of IBMV is better than delay and sum (DAS) method, minimum variance (MV) algorithm and beam domain minimum variance (BMV) method in terms of resolution and contrast ratio.

A new approach to implement minimum variance distortionless response (MVDR) beamforming is introduced for coherent plane-wave compounding (CPWC). MVDR requires the covariance matrix of the incoming signal to be estimated and a spatial smoothing approximation is usually adopted to prevent this calculation from being underconstrained. In the new approach, we analyze MVDR as a spatial filter that decorrelates signals received at individual channels before summation. Based on the analysis, we develop two MVDR beamformers without using any spatial smoothing. First, MVDR weights are applied to the received signals after accumulating the data over transmits at different angles, while the second involves weighting the data collected in individual transmits and compounding over the transducer elements. In both cases, the covariance matrix is estimated using a set of slightly different combinations of the echo data. We show the sufficient statistic for this estimation that can be described by approximating the correlation among the backscattered ultrasound signals to their spatial coherence. Using the van Cittert-Zernike theorem, their statistical similarity is assessed by relating the spatial coherence to the profile of the source intensity. Both spatial-coherence-based MVDR beamformers are evaluated on data sets acquired from simulation, phantom, and in vivo studies. Imaging results show that they offer improvements over simple coherent compounding in terms of spatial and contrast resolutions. They also outperform other existing MVDR-based methods in the literature that are applied to CPWC.

Monitoring the performance of a large network would involve a high measurement cost. To reduce the overhead, sparse network monitoring techniques may be applied to select paths or time intervals to take the measurements, while the remaining monitoring data can be inferred leveraging the spatial-temporal correlations among data. The quality of missing data recovery, however, highly relies on the specific inference technique adopted. Tensor completion is a promising technique for more accurate missing data inference by exploiting the multi-dimensional data structure. However, data processing for higher dimensional tensors involves a large amount of computation, which prevents conventional tensor completion algorithms from practical application in the presence of large amount of data. This work takes the initiative to investigate the potential and methodologies of performing parallel processing for high-speed and high accuracy tensor completion over Graphics Processing Units (GPUs). We propose a GPU-accelerated parallel Tensor Completion scheme (GPU-TC) for accurate and fast recovery of missing data. To improve the data recovery accuracy and speed, we propose three novel techniques to well exploit the tensor factorization structure and the GPU features: grid-based tensor partition, independent task assignment based on Fisher-Yates shuffle, sphere facilitated and memory-correlated scheduling. We have conducted extensive experiments using network traffic trace data to compare the proposed GPU-TC with the state of art tensor completion algorithms and matrix-based algorithms. The experimental results demonstrate that GPU-TC can achieve significantly better performance in terms of two relative error ratio metrics and computation time.

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.