Incoherent Clutter Suppression Using Lag-One Coherence

A beamforming algorithm with composite multi-conditional cross correlation and range standard deviation factor for high quality ultrasound imaging

The traditional delay-and-sum (DAS) beamformer is inadequate for clutter suppression in ultrasound imaging. The coherence factor beamformer can effectively suppress clutter, but it is prone to cause dark-region artifacts during incoherent signal suppression. In this paper, the resolution and contrast of ultrasound images are improved by using a novel adaptive beamforming algorithm called composite multi-conditional cross correlation (MCC) and range standard deviation factor (CMCC-RSF). The MCC algorithm is obtained by integrating the conditional coherence and cross correlation factor, which can better balance clutter suppression ability inside the anechoic cyst and speckle background quality. Then, we propose a range standard deviation factor (RSF) to improve the resolution of MCC without destroying the speckle background. The simulation and experiment results show that compared with traditional DAS, the full-width at half-maximum of CMCC-RSF is improved by 75.14%, 47.62%, 49.27%, and 55.97%, respectively. According to the experiment results, the contrast ratio and speckle signal-to-noise ratio of CMCC-RSF are maximally improved by 154.38%, and 124.53%, respectively. In general, the proposed CMCC-RSF algorithm can improve comprehensive image quality with low relative computational complexity.

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.

Optimal transmit apodization for the maximization of lag-one coherence with applications to aberration delay estimation

Phase aberration is one of the major sources of image degradation in medical ultrasound imaging. One of the earliest and simplest techniques to correct for phase aberration involves nearest-neighbor cross correlation to estimate delays between neighboring receive channels and the compensation of aberration delays in a delay-and-sum beamformer. The main challenge is that neighboring receive channels may not have sufficient signal correlation to accurately estimate the aberration delays. Although algorithms such as the translating transmit aperture or the common midpoint gather are designed to perfectly maximize signal correlations between received signals, these algorithms require the use of different transmit apertures for each received signal. Instead, this work proposes the use of a single globally-applicable transmit apodization function that optimizes the lag-one coherence based on the van Cittert-Zernike theorem. For the application to phase aberration correction, it is shown across 20 different zero-mean Gaussian-random aberrators that the proposed optimal apodization function reduces the estimation error in the aberration delay profile from 22.85% to 15.72%.

Large-Array Deep Abdominal Imaging in Fundamental and Harmonic Mode

Deep abdominal images suffer from poor diffraction-limited lateral resolution. Extending the aperture size can improve resolution. However, phase distortion and clutter can limit the benefits of larger arrays. Previous studies have explored these effects using numerical simulations, multiple transducers, and mechanically swept arrays. In this work, we used an 8.8-cm linear array transducer to investigate the effects of aperture size when imaging through the abdominal wall. We acquired channel data in fundamental and harmonic modes using five aperture sizes. To avoid motion and increase the parameter sampling, we decoded the full-synthetic aperture data and retrospectively synthesized nine apertures (2.9-8.8 cm). We imaged a wire target and a phantom through ex vivo porcine abdominal samples and scanned the livers of 13 healthy subjects. We applied bulk sound speed correction to the wire target data. Although point resolution improved from 2.12 to 0.74 mm at 10.5 cm depth, contrast resolution often degraded with aperture size. In subjects, larger apertures resulted in an average maximum contrast degradation of 5.5 dB at 9-11 cm depth. However, larger apertures often led to visual detection of vascular targets unseen with conventional apertures. An average 3.7-dB contrast improvement over fundamental mode in subjects showed that the known benefits of tissue-harmonic imaging extend to larger arrays.

Coherence Metrics for Reader-Independent Differentiation of Cystic From Solid Breast Masses in Ultrasound Images

Traditional breast ultrasound imaging is a low-cost, real-time and portable method to assist with breast cancer screening and diagnosis, with particular benefits for patients with dense breast tissue. We previously demonstrated that incorporating coherence-based beamforming additionally improves the distinction of fluid-filled from solid breast masses, based on qualitative image interpretation by board-certified radiologists. However, variable sensitivity (range: 0.71-1.00 when detecting fluid-filled masses) was achieved by the individual radiologist readers. Therefore, we propose two objective coherence metrics, lag-one coherence (LOC) and coherence length (CL), to quantitatively determine the content of breast masses without requiring reader assessment. Data acquired from 31 breast masses were analyzed. Ideal separation (i.e., 1.00 sensitivity and specificity) was achieved between fluid-filled and solid breast masses based on the mean or median LOC value within each mass. When separated based on mean and median CL values, the sensitivity/specificity decreased to 1.00/0.95 and 0.92/0.89, respectively. The greatest sensitivity and specificity were achieved in dense, rather than non-dense, breast tissue. These results support the introduction of an objective, reader-independent method for automated diagnoses of cystic breast masses.

Adaptive subarray coherence based post-filter using array gain in medical ultrasound imaging

This paper presents an adaptive subarray coherence-based post-filter (ASCBP) applied to the eigenspace-based forward-backward minimum variance (ESB-FBMV) beamformer to simultaneously improve image quality and beamformer robustness. Additionally, the ASCBP can separate close targets. The ASCBP uses an adaptive noise power weight based on the concept of the beamformer's array gain (AG) to suppress the noise adaptively and achieve improved images. Moreover, a square neighborhood average was applied to the ASCBP in order to provide more smoothed square neighborhood ASCBP (SN-ASCBP) values and improve the speckle quality. Through simulations of point phantoms and cyst phantoms and experimental validation, the performance of the proposed methods was compared to that of delay-and-sum (DAS), MV-based beamformers, and subarray coherence-based post-filter (SCBP). The simulated results demonstrated that the ASCBP method improved the full width at half maximum (FWHM) by 57 % and the coherent interference suppression power (CISP) by 52 dB compared to the SCBP post-filter. Considering the experimental results, the SN-ASCBP method presented the best enhancement in terms of generalized contrast to noise ratio (gCNR) and contrast ratio (CR) while the ASCBP showed the best improvement in FWHM among other methods. Furthermore, the proposed methods presented a striking performance in low SNRs. The results of evaluating the different methods under aberration and sound speed error illustrated the better robustness of the proposed methods in comparison with others.

Adaptive Models for Multi-Covariate Imaging of Sub-Resolution Targets (MIST)

Multi-covariate imaging of sub-resolution targets (MIST) is a statistical, model-based image formation technique that smooths speckles and reduces clutter. MIST decomposes the measured covariance of the element signals into modeled contributions from mainlobe, sidelobes, and noise. MIST covariance models are derived from the well-known autocorrelation relationship between transmit apodization and backscatter covariance. During in vivo imaging, the effective transmit aperture often deviates from the applied apodization due to nonlinear propagation and wavefront aberration. Previously, the backscatter correlation length provided a first-order measure of these patient-specific effects. In this work, we generalize and extend this approach by developing data-adaptive covariance estimation, parameterization, and model-formation techniques. We performed MIST imaging using these adaptive models and evaluated the performance gains using 152 tissue-harmonic scans of fetal targets acquired from 15 healthy pregnant subjects. Compared to standard MIST imaging, the contrast-to-noise ratio (CNR) is improved by a median of 8.3%, and the speckle signal-to-noise ratio (SNR) is improved by a median of 9.7%. The median CNR and SNR gains over B-mode are improved from 29.4% to 40.4% and 24.7% to 38.3%, respectively. We present a versatile empirical function that can parameterize an arbitrary speckle covariance and estimate the effective coherent aperture size and higher order coherence loss. We studied the performance of the proposed methods as a function of input parameters. The implications of system-independent MIST implementation are discussed.

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.

Covariance Matrix-Based Statistical Beamforming for Medical Ultrasound Imaging

Medical ultrasound image quality is often limited by clutter, which is the dominant mechanism of image degradation. A variety of beamforming methods have been extensively studied to reduce clutter and, thus, enhance ultrasound image quality. This article introduces a new beamforming approach, called covariance matrix-based statistical beamforming (CMSB), to improve the image contrast and preserve the background speckle pattern while simultaneously achieving a high-resolution performance. In CMSB, adaptive selection of subarray length, diagonal reducing, and mean-to-standard-deviation ratio-based subarray averaging are inherently combined to differentiate and reduce off-axis energy effectively. Moreover, rotary averaging prior to diagonal reducing is introduced to preserve speckle statistics. Simulated, experimental, and in vivo datasets were used to evaluate the imaging performance of the proposed method. The quantitative results indicate that, compared with delay-and-sum (DAS) beamforming, CMSB leads to average improvements of 44.5% and 97.3% in lateral resolution and contrast, respectively, in phantom experiments. Our work shows that CMSB is capable of improving image resolution and contrast while maintaining the speckle reliably. Preliminary in vivo study also demonstrates that the CMSB can enhance image contrast and lesion detection.

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.

Reverberation Clutter Suppression Using 2-D Spatial Coherence Analysis

Diffuse reverberation clutter often significantly degrades the visibility of abdominal structures. Reverberation clutter acts as a temporally stationary haze that originates from the multiple scattering within the subcutaneous layers and has a narrow spatial correlation length. We recently presented an adaptive beamforming technique, Lag-one Spatial Coherence Adaptive Normalization (LoSCAN), which can recover the contrast suppressed by incoherent noise. LoSCAN successfully suppressed reverberation clutter in numerous clinical examples. However, reverberation clutter is a 3-D phenomenon and can often exhibit a finite partial correlation between receive channels. Due to a strict noise-incoherence assumption, LoSCAN does not eliminate correlated reverberation clutter. This work presents a 2-D matrix array-based LoSCAN method and evaluates matrix-LoSCAN-based strategies to suppress partially correlated reverberation clutter. We validated the proposed matrix LoSCAN method using Field II simulations of a 64×64 symmetric 2-D array. We show that a subaperture beamforming (SAB) method tuned to the direction of noise correlation is an effective method to enhance LoSCAN's performance. We evaluated the efficacy of the proposed methods using fundamental and harmonic channel data acquired from the liver of two healthy volunteers using a 64×16 custom 2-D array. Compared to azimuthal LoSCAN, the proposed approach increased the contrast by up to 5.5 dB and the generalized contrast-to-noise ratio (gCNR) by up to 0.07. We also present analytic models to understand the impact of partially correlated reverberation clutter on LoSCAN images and explain the proposed methods' mechanism of image quality improvement.

Frequency-Dependent Spatial Coherence in Conventional and Chirp Transmissions

The development of adaptive imaging techniques is contingent on the accurate and repeatable characterization of ultrasonic image quality. Adaptive transmit frequency selection, filtering, and frequency compounding all offer the ability to improve target conspicuity by balancing the effects of imaging resolution, the signal-to-clutter ratio, and speckle texture, but these strategies rely on the ability to capture image quality at each desired frequency. We investigate the use of broadband linear frequency-modulated transmissions, also known as chirps, to expedite the interrogation of frequency-dependent tissue spatial coherence for real-time implementations of frequency-based adaptive imaging strategies. Chirp-collected measurements of coherence are compared to those acquired by individually transmitted conventional pulses over a range of fundamental and harmonic frequencies, in order to evaluate the ability of chirps to recreate conventionally acquired coherence. Simulation and measurements in a uniform phantom free of acoustic clutter indicate that chirps replicate not only the mean coherence in a region-of-interest but also the distribution of coherence values over frequency. Results from acquisitions in porcine abdominal and human liver models show that prediction accuracy improves with chirp length. Chirps are also able to predict frequency-dependent decreases in coherence in both porcine abdominal and human liver models for fundamental and pulse inversion harmonic imaging. This work indicates that the use of chirps is a viable strategy to improve the efficiency of variable frequency coherence mapping, thus presenting an avenue for real-time implementations for frequency-based adaptive strategies.

Histogram Matching for Visual Ultrasound Image Comparison

The widespread development of new ultrasound image formation techniques has created a need for a standardized methodology for comparing the resulting images. Traditional methods of evaluation use quantitative metrics to assess the imaging performance in specific tasks, such as point resolution or lesion detection. Quantitative evaluation is complicated by unconventional new methods and nonlinear transformations of the dynamic range of data and images. Transformation-independent image metrics have been proposed for quantifying task performance. However, clinical ultrasound still relies heavily on visualization and qualitative assessment by expert observers. We propose the use of histogram matching to better assess differences across image formation methods. We briefly demonstrate the technique using a set of sample beamforming methods and discuss the implications of such image processing. We present variations of histogram matching and provide code to encourage the application of this method within the imaging community.

Coherence-based quantification of acoustic clutter sources in medical ultrasound

The magnitudes by which aberration and incoherent noise sources, such as diffuse reverberation and thermal noise, contribute to degradations in image quality in medical ultrasound are not well understood. Theory predicting degradations in spatial coherence and contrast in response to combinations of incoherent noise and aberration levels is presented, and the theoretical values are compared to those from simulation across a range of magnitudes. A method to separate the contributions of incoherent noise and aberration in the spatial coherence domain is also presented and applied to predictions for losses in contrast. Results indicate excellent agreement between theory and simulations for beamformer gain and expected contrast loss due to incoherent noise and aberration. Error between coherence-predicted aberration contrast loss and measured contrast loss differs by less than 1.5 dB on average, for a -20 dB native contrast target and aberrators with a range of root-mean-square time delay errors. Results also indicate in the same native contrast target the contribution of aberration to contrast loss varies with channel signal-to-noise ratio (SNR), peaking around 0 dB SNR. The proposed framework shows promise to improve the standard by which clutter reduction strategies are evaluated.