New adaptive clutter rejection for ultrasound color Doppler imaging: in vivo study

Spatial Coherence Adaptive Clutter Filtering in Color Flow Imaging-Part I: Simulation Studies

The appropriate selection of a clutter filter is critical for ensuring the accuracy of velocity estimates in ultrasound color flow imaging. Given the complex spatio-temporal dynamics of flow signal and clutter, however, the manual selection of filters can be a significant challenge, increasing the risk for bias and variance introduced by the removal of flow signal and/or poor clutter suppression. We propose a novel framework to adaptively select clutter filter settings based on color flow image quality feedback derived from the spatial coherence of ultrasonic backscatter. This framework seeks to relax assumptions of clutter magnitude and velocity that are traditionally required in existing adaptive filtering methods to generalize clutter filtering to a wider range of clinically-relevant color flow imaging conditions. In this study, the relationship between color flow velocity estimation error and the spatial coherence of clutter filtered channel signals was investigated in Field II simulations for a wide range of flow and clutter conditions. This relationship was leveraged in a basic implementation of coherence-adaptive clutter filtering (CACF) designed to dynamically adapt clutter filters at each imaging pixel and frame based on local measurements of spatial coherence. In simulation studies with known scatterer and clutter motion, CACF was demonstrated to reduce velocity estimation bias while maintaining variance on par with conventional filtering.

Blind Source Separation for Clutter and Noise Suppression in Ultrasound Imaging: Review for Different Applications

Blind source separation (BSS) refers to a number of signal processing techniques that decompose a signal into several "source" signals. In recent years, BSS is increasingly employed for the suppression of clutter and noise in ultrasonic imaging. In particular, its ability to separate sources based on measures of independence rather than their temporal or spatial frequency content makes BSS a powerful filtering tool for data in which the desired and undesired signals overlap in the spectral domain. The purpose of this work was to review the existing BSS methods and their potential in ultrasound imaging. Furthermore, we tested and compared the effectiveness of these techniques in the field of contrast-ultrasound super-resolution, contrast quantification, and speckle tracking. For all applications, this was done in silico, in vitro, and in vivo. We found that the critical step in BSS filtering is the identification of components containing the desired signal and highlighted the value of a priori domain knowledge to define effective criteria for signal component selection.

Microvascular Flow Imaging of Residual or Recurrent Hepatocellular Carcinoma after Transarterial Chemoembolization: Comparison with Color/Power Doppler Imaging

Objective

To determine the feasibility of microvascular flow imaging (MVFI) in comparison with color/power Doppler imaging (CDI/PDI) for detection of intratumoral vascularity in suspected post-transarterial chemoembolization (TACE) residual or recurrent hepatocellular carcinomas (HCCs) by using contrast-enhanced ultrasonography (CEUS) or hepatic angiography (HA) findings as the reference standard.

Materials and Methods

One hundred HCCs (mean size, 2.2 cm) in 100 patients treated with TACE were included in this prospective study. CDI, PDI, and MVFI were performed in tandem for evaluating intratumoral vascularity of the lesions by using an RS85 ultrasound scanner (Samsung Medison Co., Ltd.). Intratumoral vascularity in each technique was assessed by two radiologists in consensus by using a 5-point scale. Then, one of the two radiologists and another radiologist performed additional image review in the reverse order (MVFI-PDI-CDI) for evaluation of intra- and interobserver agreements. Results were then compared with those of either HA or CEUS as the reference. The McNemar test, logistic regression analysis, and intraclass correlation coefficient (ICC) were used.

Results

CEUS or HA revealed intratumoral vascularity in 87% (87/100) of the tumors. Sensitivity (79.3%, 69/87) and accuracy (80.0%, 80/100) of MVFI were significantly higher than those of CDI (sensitivity, 27.6% [24/87]; accuracy, 37.0% [37/100]) or PDI (sensitivity, 36.8% [32/87]; accuracy, 44.0% [44/100]) (all p < 0.05). CDI, PDI, and MVFI presented excellent intraobserver (ICCs > 0.9) and good interobserver agreements (ICCs > 0.6).

Conclusion

MVFI demonstrated significantly higher sensitivity and accuracy than did CDI and PDI for the detection of intratumoral vascularity in suspected residual or recurrent HCCs after TACE.

Receiver-Operating Characteristic Analysis of Eigen-Based Clutter Filters for Ultrasound Color Flow Imaging

The eigen-based filter has theoretically established itself as a potent solution in ultrasound color flow imaging (CFI) for combating against clutter arising from moving tissues. Yet, it remains poorly understood on how much gain in flow detection sensitivity and specificity can be delivered by this adaptive clutter filter. Here, we investigated the receiver operating characteristic (ROC) of the eigen-based clutter filter to statistically evaluate its efficacy. Our investigation was conducted using a new vascular phantom testbed that incorporated both intrinsic tissue motion (vessel pulsation: 7.58 cm/s peak velocity) and extrinsic tissue motion (vibration: 5-Hz frequency, 2.98 cm/s peak velocity), as well as pulsatile flow (pulse rate: 60 beats/min; systolic flow rate: 6.5 mL/s). The eigen-filter (single-ensemble formulation) was applied to CFI raw data sets obtained from the phantom's short-axis view (slow-time ensemble size: 12; pulse repetition frequency: 2 kHz; and ultrasound frequency: 5 MHz), and post-filter Doppler power was compared between flow and tissue regions. Results show that, in the presence of vessel pulsation and tissue vibration, the eigen-filter yielded a high true positive rate in depicting flow pixels in CFI frames (0.945 and 0.917, respectively, during peak systole and end diastole at 60° beam-flow angle), while maintaining a low false alarm rate (0.10) in rendering tissue pixels. Also, the eigen-filter posed ROC curves whose area under curve was higher than those for the polynomial regression filter (statistically significant; t-test p values were less than 0.05). These findings serve well to substantiate the merit of using eigen-filters to enhance the vascular visualization capability of CFI.

New adaptive clutter rejection based on spectral analysis for ultrasound color Doppler imaging: phantom and in vivo abdominal study

Effective rejection of time-varying clutter originating from slowly moving vessels and surrounding tissues is important for depicting hemodynamics in ultrasound color Doppler imaging (CDI). In this paper, a new adaptive clutter rejection method based on spectral analysis (ACR-SA) is presented for suppressing nonstationary clutter. In ACR-SA, tissue and flow characteristics are analyzed by singular value decomposition and tissue acceleration of backscattered Doppler signals to determine an appropriate clutter filter from a set of clutter filters. To evaluate the ACR-SA method, 20 frames of complex baseband data were acquired by a commercial ultrasound system equipped with a research package (Accuvix V10, Samsung Medison, Seoul, Korea) using a 3.5-MHz convex array probe by introducing tissue movements to the flow phantom (Gammex 1425 A LE, Gammex, Middleton, WI, USA). In addition, 20 frames of in vivo abdominal data from five volunteers were captured. From the phantom experiment, the ACR-SA method provided 2.43 dB (p <; 0.001) and 1.09 dB ( ) improvements in flow signal-to-clutter ratio (SCR) compared to static (STA) and down-mixing (ACR-DM) methods. Similarly, it showed smaller values in fractional residual clutter area (FRCA) compared to the STA and ACR-DM methods (i.e., 2.3% versus 5.4% and 3.7%, respectively, ). The consistent improvements in SCR from the proposed ACR-SA method were obtained with the in vivo abdominal data (i.e., 4.97 dB and 3.39 dB over STA and ACR-DM, respectively). The ACR-SA method showed less than 1% FRCA values for all in vivo abdominal data. These results indicate that the proposed ACR-SA method can improve image quality in CDI by providing enhanced rejection of nonstationary clutter.

An efficient pulse compression method of chirp-coded excitation in medical ultrasound imaging

Coded excitation can improve the SNR in medical ultrasound imaging. In coded excitation, pulse compression is applied to compress the elongated coded signals into a short pulse, which typically requires high computational complexity, i.e., a compression filter with a few hundred coefficients. In this paper, we propose an efficient pulse compression method of chirp-coded excitation, in which the pulse compression is conducted with complex baseband data after downsampling, to lower the computational complexity. In the proposed method, although compression is conducted with the complex data, the L-fold downsampling is applied for reducing both data rates and the number of compression filter coefficients; thus, total computational complexity is reduced to the order of 1/L(2). The proposed method was evaluated with simulation and phantom experiments. From the simulation and experiment results, the proposed pulse compression method produced similar axial resolution compared with the conventional pulse compression method with negligible errors, i.e., ≫36 dB in signal-to-error ratio (SER). These results indicate that the proposed method can maintain the performance of pulse compression of chirp-coded excitation while substantially reducing computational complexity.

A single-ensemble clutter rejection method based on the analytic geometry for ultrasound color flow imaging

In ultrasound color flow imaging (CFI), the single-ensemble eigen-based filters can reject clutter components using each slow-time ensemble individually. They have shown excellent spatial adaptability. This article proposes a novel clutter rejection method called the single-ensemble geometry filter (SGF), which is derived from an analytic geometry perspective. If the transmitted pulse number M equals two, the clutter component distribution on a two-dimensional (2-D) plane will be similar to a tilted ellipse. Therefore, the direction of the major axis of the ellipse can be used as the first principal component of the autocorrelation matrix estimated from multiple ensembles. Then the algorithm is generalized from 2-D to a higher dimensional space by using linear algebra representations of the ellipse. Comparisons have been made with the high-pass filter (HPF), the Hankel-singular value decomposition (SVD) filter and the recursive eigen-decomposition (RED) method using both simulated and human carotid data. Results show that compared with HPF and Hankel-SVD, the proposed filter causes less bias on the velocity estimation when the clutter velocity is close to that of the blood flow. On the other hand, the proposed filter does not need to update the autocorrelation matrix and can achieve better spatial adaptability than the RED.