A Spatial Coherence Beamformer Design for Power Doppler Imaging

Ultrafast Coherence-Based Power Doppler Estimation Using Nonlinear Compounding With Complementary Subset Transmit

OBJECTIVE

Conventional coherent plane wave compounding (CPWC) and sum-of-square power Doppler (PD) estimation lead to low contrast and high noise level in ultrafast PD imaging when the number of plane-wave angle and the ensemble length is limited. The coherence-based PD estimation using temporal-multiply-and-sum (TMAS) of high-lag autocorrelation can effectively suppress the uncorrelated noises but at the cost of signal power due to the blood flow decorrelation.

METHODS

In this study, the TMAS PD estimation is incorporated with complementary subset transmit in nonlinear compounding (DMAS-CST) to leverage the signal coherence in both angular and temporal dimensions for improvement of PD image quality. The CST correlation can be performed not only within the same Doppler ensemble (i.e., intra-correlation) but also across the adjacent Doppler ensembles (i.e., inter-correlation) to increase the number of correlation pairs in TMAS PD estimation.

RESULTS

In both simulations and experiments, DMAS-CST is capable of improving the contrast of TMAS PD image by over 10 dB compared to the nonlinear compounding alone by enhanced noise suppression and lower flow decorrelation. When the CST correlations are performed both intra and inter Doppler ensembles, the noise level further reduces in DMAS-CST.

CONCLUSION

Since the TMAS PD estimation is often limited by the loss of signal power due to temporal decorrelation, the design of complementary subsets in DMAS-CST should be carefully examined to preserve the blood flow signal. Future work of this study will focus on how to combine the conventional PD and the TMAS PD for better signal preservation and effective noise suppression.

Improved Ultrafast Power Doppler Imaging Using United Spatial-Angular Adaptive Scaling Wiener Postfilter

Ultrafast power Doppler imaging (uPDI) using high-frame-rate plane-wave transmission is a new microvascular imaging modality that offers high Doppler sensitivity. However, due to the unfocused transmission of plane waves, the echo signal is subject to interference from noise and clutter, resulting in a low signal-to-noise ratio (SNR) and poor image quality. Adaptive beamforming techniques are effective in suppressing noise and clutter for improved image quality. In this study, an adaptive beamformer based on a united spatial-angular adaptive scaling Wiener (uSA-ASW) postfilter is proposed to improve the resolution and contrast of uPDI. In the proposed method, the signal power and noise power of the Wiener postfilter are estimated by uniting spatial and angular signals, and a united generalized coherence factor (uGCF) is introduced to dynamically adjust the noise power estimation and enhance the robustness of the method. Simulation and in vivo data were used to verify the effectiveness of the proposed method. The results show that the uSA-ASW can achieve higher resolution and significant improvements in image contrast and background noise suppression compared with conventional delay-and-sum (DAS), coherence factor (CF), spatial-angular CF (SACF), and adaptive scaling Wiener (ASW) postfilter methods. In the simulations, uSA-ASW improves contrast-to-noise ratio (CNR) by 34.7 dB (117.3%) compared with DAS, while reducing background noise power (BNP) by 52 dB (221.4%). The uSA-ASW method provides full-width at half-maximum (FWHM) reductions of [Formula: see text] (59.5%) and [Formula: see text] (56.9%), CNR improvements of 25.6 dB (199.9%) and 42 dB (253%), and BNP reductions of 46.1 dB (319.3%) and 12.9 dB (289.1%) over DAS in the experiments of contrast-free human neonatal brain and contrast-free human liver, respectively. In the contrast-free experiments, uSA-ASW effectively balances the performance of noise and clutter suppression and enhanced microvascular visualization. Overall, the proposed method has the potential to become a reliable microvascular imaging technique for aiding in more accurate diagnosis and detection of vascular-related diseases in clinical contexts.

Adaptive and Robust Vessel Quantification in Contrast-Free Ultrafast Ultrasound Microvessel Imaging

The morphological features of vasculature in diseased tissue differ significantly from those in normal tissue. Therefore, vasculature quantification is crucial for disease diagnosis and staging. Ultrasound microvessel imaging (UMI) with ultrafast ultrasound acquisitions has been determined to have potential in clinical applications given its superior sensitivity in blood flow detection. However, the presence of spatial-dependent noise caused by a low imaging signal-to-noise ratio and incoherent clutter artifacts caused by moving hyperechoic scatterers degrades the performance of UMI and the reliability of vascular quantification. To tackle these issues, we proposed an improved UMI technique along with an adaptive vessel segmentation workflow for robust vessel identification and vascular feature quantification. A previously proposed sub-aperture cross-correlation technique and a normalized cross-correlation technique were applied to equalize the spatially dependent noise level and suppress the incoherent clutter artifact. A square operator and non-local means filter were then used to better separate the blood flow signal from residual background noise. On the de-noised ultrasound microvessel image, an automatic and adaptive vessel segmentation method was developed based on the different spatial patterns of blood flow signal and background noise. The proposed workflow was applied to a CIRS phantom, to a Doppler flow phantom and to an inflammatory bowel, kidney and liver, to validate its feasibility. Results revealed that automatic adaptive, and robust vessel identification performance can be achieved using the proposed method without the subjectivity caused by radiologists/operators.

Spatial Coherence in Medical Ultrasound: A Review

Traditional pulse-echo ultrasound imaging heavily relies on the discernment of signals based on their relative magnitudes but is limited in its ability to mitigate sources of image degradation, the most prevalent of which is acoustic clutter. Advances in computing power and data storage have made it possible for echo data to be alternatively analyzed through the lens of spatial coherence, a measure of the similarity of these signals received across an array. Spatial coherence is not currently explicitly calculated on diagnostic ultrasound scanners but a large number of studies indicate that it can be employed to describe image quality, to adaptively select system parameters and to improve imaging and target detection. With the additional insights provided by spatial coherence, it is poised to play a significant role in the future of medical ultrasound. This review details the theory of spatial coherence in pulse-echo ultrasound and key advances made over the last few decades since its introduction in the 1980s.

Spatiotemporal Coherence to Quantify Sources of Image Degradation in Ultrasonic Imaging

Thermal noise and acoustic clutter signals degrade ultrasonic image quality and contribute to unreliable clinical assessment. When both noise and clutter are prevalent, it is difficult to determine which one is a more significant contributor to image degradation because there is no way to separately measure their contributions in vivo. Efforts to improve image quality often rely on an understanding of the type of image degradation at play. To address this, we derived and validated a method to quantify the individual contributions of thermal noise and acoustic clutter to image degradation by leveraging spatial and temporal coherence characteristics. Using Field II simulations, we validated the assumptions of our method, explored strategies for robust implementation, and investigated its accuracy and dynamic range. We further proposed a novel robust approach for estimating spatial lag-one coherence. Using this robust approach, we determined that our method can estimate the signal-to-thermal noise ratio (SNR) and signal-to-clutter ratio (SCR) with high accuracy between SNR levels of -30 to 40 dB and SCR levels of -20 to 15 dB. We further explored imaging parameter requirements with our Field II simulations and determined that SNR and SCR can be estimated accurately with as few as two frames and sixteen channels. Finally, we demonstrate in vivo feasibility in brain imaging and liver imaging, showing that it is possible to overcome the constraints of in vivo motion using high-frame rate M-Mode imaging.

Regional-Lag Signed Delay Multiply and Sum Beamforming in Ultrafast Ultrasound Imaging

Ultrafast ultrasound imaging provides very high frame rates but provides poor imaging quality due to unfocused beams. The delay multiply and sum (DMAS) beamformer has been used to improve ultrafast ultrasound imaging contrast but is always accompanied by oversuppression, which produces low-quality speckle images and degrades the contrast performance. A smaller maximum lag in the signed DMAS (sDMAS) contributes better speckle preservation but lower resolution for hyperechoic scatters. To overcome this tradeoff, a regional-lag signed delay multiply and sum (rsDMAS) beamformer is proposed in this article. Innovatively, a region discrimination tool realized by the generalized coherence factor (GCF) is used to limit the maximum lag for spatial coherence estimation. Subaperture coherence smoothing estimates the short-lag coherence instead of multiplication in pairs, thereby reducing calculation complexity and smoothing the speckle texture. Normalization and sign correction are also introduced to achieve better beamforming output. The simulated, phantom, and in vivo data are adopted to evaluate the effectiveness of the proposed beamformer. Numerical results show that the proposed method achieves improvements of the contrast ratio (CR) by 9%, contrast-to-noise ratio (CNR) by 41%, speckle signal-to-noise ratio (sSNR) by 41%, and generalized contrast-to-noise ratio (gCNR) by 0.0004 compared with DMAS (in simulation). Resolution experiments show that the proposed method obtains a loss of 0.07 mm in the full width at half maximum (FWHM) and the same separability of close point scatters as DMAS. These findings indicate that the proposed method achieves higher contrast performance at less obvious sacrifice of the lateral resolution than DMAS.

Occult Regions of Suppressed Coherence in Liver B-Mode Images

Ultrasound is an essential tool for diagnosing and monitoring diseases, but it can be limited by poor image quality. Lag-one coherence (LOC) is an image quality metric that can be related to signal-to-noise ratio and contrast-to-noise ratio. In this study, we examine matched LOC and B-mode images of the liver to discern patterns of low image quality, as indicated by lower LOC values, occurring beneath the abdominal wall, near out-of-plane vessels and adjacent to hyperechoic targets such the liver capsule. These regions of suppressed coherence are often occult; they present as temporally stable uniform speckle on B-mode images, but the LOC measurements in these regions suggest substantially degraded image quality. Quantitative characterization of the coherence suppression beneath the abdominal wall reveals a consistent pattern both in simulations and in vivo; sharp drops in coherence occurring beneath the abdominal wall asymptotically recover to a stable coherence at depth. Simulation studies suggest that abdominal wall reverberation clutter contributes to the initial drop in coherence but does not influence the asymptotic LOC value. Clinical implications are considered for contrast loss in B-mode imaging and estimation errors for elastography and Doppler imaging.

Multidimensional Clutter Filtering of Aperture Domain Data for Improved Blood Flow Sensitivity

Singular value decomposition (SVD) is a valuable factorization technique used in clutter rejection filtering for power Doppler imaging. Conventionally, SVD is applied to a Casorati matrix of radio frequency data, which enables filtering based on spatial or temporal characteristics. In this article, we propose a clutter filtering method that uses a higher order SVD (HOSVD) applied to a tensor of aperture data, e.g., delayed channel data. We discuss temporal, spatial, and aperture domain features that can be leveraged in filtering and demonstrate that this multidimensional approach improves sensitivity toward blood flow. Further, we show that HOSVD remains more robust to short ensemble lengths than conventional SVD filtering. Validation of this technique is shown using Field II simulations and in vivo data.

Simultaneous Noise Suppression and Incoherent Artifact Reduction in Ultrafast Ultrasound Vascular Imaging

Ultrasound vascular imaging based on ultrafast plane wave imaging and singular value decomposition (SVD) clutter filtering has demonstrated superior sensitivity in blood flow detection. However, ultrafast ultrasound vascular imaging is susceptible to electronic noise due to the weak penetration of unfocused waves, leading to a lower signal-to-noise ratio (SNR) at larger depths. In addition, incoherent clutter artifacts originating from strong and moving tissue scatterers that cannot be completely removed create a strong mask on top of the blood signal that obscures the vessels. Herein, a method that can simultaneously suppress the background noise and incoherent artifacts is proposed. The method divides the tilted plane or diverging waves into two subgroups. Coherent spatial compounding is performed within each subgroup, resulting in two compounded data sets. An SVD-based clutter filter is applied to each data set, followed by a correlation between the two data sets to produce a vascular image. Uncorrelated noise and incoherent artifacts can be effectively suppressed with the correlation process, while the coherent blood signal can be preserved. The method was evaluated in wire-target simulations and phantom, in which around 7-10-dB SNR improvement was shown. Consistent results were found in a flow channel phantom with improved SNR by the proposed method (39.9 ± 0.2 dB) against conventional power Doppler (29.1 ± 0.6 dB). Last, we demonstrated the effectiveness of the method combined with block-wise SVD clutter filtering in a human liver, breast tumor, and inflammatory bowel disease data sets. The improved blood flow visualization may facilitate more reliable small vessel imaging for a wide range of clinical applications, such as cancer and inflammatory diseases.

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.