Ultrasonic Reverberation Clutter Suppression Using Multiphase Apodization With Cross Correlation

Adaptive complementary neighboring sub-aperture beamforming for thermoacoustic imaging

BACKGROUND

When applied to thermoacoustic imaging (TAI), the delay-and-sum (DAS) algorithm produces strong sidelobes due to its disadvantages of uniform aperture weighting. As a result, the quality of TAI images recovered by DAS is often severely degraded by strong non-coherent clutter, which restricts the development and application of TAI.

PURPOSE

To address this issue, we propose an adaptive complementary neighboring sub-aperture (NSA) beamforming algorithm for TAI.

METHODS

In NSA, we introduce a coordinate system transformation when calculating the normalized cross-correlation (NCC) matrix. This approach enables the computation of the NCC coefficient within the specified kernel without complex coordinate calculations. We first conducted the numerical simulation experiment to validate NSA using a tree branch phantom. In addition, we also conducted phantom (five sauce tubes), ex vivo (ablation needle in ex vivo porcine liver), and in vivo (human arm) TAI experiments using our TAI system with a center frequency of 3 GHz.

RESULTS

In the numerical simulation experiment, the structural similarity index (SSIM) value for NSA is increased from 0.37828 for DAS to 0.75492. In the point target phantom TAI experiment, the generalized contrast-to-noise ratio (gCNR) value for NSA is increased from 0.936 for DAS to 0.962. The experimental results show that NSA can recover clearer thermoacoustic images compared to DAS. In the ex vivo TAI experiment, the full width at half maxima (FWHM) of an ablation needle (diameter = 1.5 mm) for coherence factor (CF) weighted DAS and NSA are 0.9 and 1.3 mm, respectively. Furthermore, in the in vivo TAI experiment, CF reduces the signals within the arm compared to NSA. Therefore, compared with CF, NSA can maintain the integrity of target information in TAI while effectively suppressing non-coherent background clutter.

CONCLUSIONS

NSA can effectively reduce non-coherent background noise while ensuring the completeness of the target information. So, NSA offers the potential to provide high-quality thermoacoustic images and further advance their clinical application.

Low complexity adaptive ultrasound image beamformer combined with improved multiphase apodization with cross-correlation

In this paper, an ultrasound imaging method combined with low-complexity adaptive beamformer (LCA) and improved multiphase apodization with cross-correlation (IMPAX) is proposed to improve image resolution and contrast with low hardware cost. Firstly, the delayed echo signal is apodized by the LCA to obtain a narrow mainlobe width echo signal and LCA output. Then, multiple pairs of complementary square-wave phase apodizations are applied to the apodized echo signal to obtain corresponding signal pairs, which are used to calculate the normalized cross-correlation (NCC) matrix. Finally, the average value of the NCC matrices is filtered by 2-D means, and the filtered result is introduced as the weighting factor for the LCA output. The simulation and experimental results show that the proposed LCA-IMPAX can effectively reduce the mainlobe width, suppress clutter, and be robust to noise. Compared with DAS, LCA, and MPAX, for simulated point targets, the full-width at half-maximum (FWHM, -6dB) of LCA-IMPAX is reduced by 49.22%, 10.06%, and 48.67%, respectively. For simulated cyst, the CR is improved by 219.91%, 138.08%, and 103.44%, respectively. For experimental cysts, the CR is improved by an average of 145.00%, 136.14%, and 55.09%, respectively. The results of human heart data indicate that LCA-IMPAX has good imaging quality in vivo. Since the proposed method does not involve covariance matrix inversion, it can be applied in real-time imaging systems.

Subaperture Processing-Based Adaptive Beamforming for Photoacoustic Imaging

Delay-and-sum (DAS) beamformers, when applied to photoacoustic (PA) image reconstruction, produce strong sidelobes due to the absence of transmit focusing. Consequently, DAS PA images are often severely degraded by strong off-axis clutter. For preclinical in vivo cardiac PA imaging, the presence of these noise artifacts hampers the detectability and interpretation of PA signals from the myocardial wall, crucial for studying blood-dominated cardiac pathological information and to complement functional information derived from ultrasound imaging. In this article, we present PA subaperture processing (PSAP), an adaptive beamforming method, to mitigate these image degrading effects. In PSAP, a pair of DAS reconstructed images is formed by splitting the received channel data into two complementary nonoverlapping subapertures. Then, a weighting matrix is derived by analyzing the correlation between subaperture beamformed images and multiplied with the full-aperture DAS PA image to reduce sidelobes and incoherent clutter. We validated PSAP using numerical simulation studies using point target, diffuse inclusion and microvasculature imaging, and in vivo feasibility studies on five healthy murine models. Qualitative and quantitative analysis demonstrate improvements in PAI image quality with PSAP compared to DAS and coherence factor weighted DAS (DAS _CF ). PSAP demonstrated improved target detectability with a higher generalized contrast-to-noise (gCNR) ratio in vasculature simulations where PSAP produces 19.61% and 19.53% higher gCNRs than DAS and DAS _CF , respectively. Furthermore, PSAP provided higher image contrast quantified using contrast ratio (CR) (e.g., PSAP produces 89.26% and 11.90% higher CR than DAS and DAS _CF in vasculature simulations) and improved clutter suppression.

Reverberation Noise Suppression in Ultrasound Channel Signals Using a 3D Fully Convolutional Neural Network

Diffuse reverberation is ultrasound image noise caused by multiple reflections of the transmitted pulse before returning to the transducer, which degrades image quality and impedes the estimation of displacement or flow in techniques such as elastography and Doppler imaging. Diffuse reverberation appears as spatially incoherent noise in the channel signals, where it also degrades the performance of adaptive beamforming methods, sound speed estimation, and methods that require measurements from channel signals. In this paper, we propose a custom 3D fully convolutional neural network (3DCNN) to reduce diffuse reverberation noise in the channel signals. The 3DCNN was trained with channel signals from simulations of random targets that include models of reverberation and thermal noise. It was then evaluated both on phantom and in-vivo experimental data. The 3DCNN showed improvements in image quality metrics such as generalized contrast to noise ratio (GCNR), lag one coherence (LOC) contrast-to-noise ratio (CNR) and contrast for anechoic regions in both phantom and in-vivo experiments. Visually, the contrast of anechoic regions was greatly improved. The CNR was improved in some cases, however the 3DCNN appears to strongly remove uncorrelated and low amplitude signal. In images of in-vivo carotid artery and thyroid, the 3DCNN was compared to short-lag spatial coherence (SLSC) imaging and spatial prediction filtering (FXPF) and demonstrated improved contrast, GCNR, and LOC, while FXPF only improved contrast and SLSC only improved CNR.

Spatial Prediction Filtering for Medical Ultrasound in Aberration and Random Noise

While medical ultrasound imaging has become one of the most widely used imaging modalities in clinics, it often suffers from suboptimal image quality, especially in technically difficult patients with a large amount of fat content that induces severe phase aberration effects and decreases the signal-to-noise ratio. Several researchers have proposed various techniques, which can be broadly categorized as either a phase aberration correction (PAC) technique or a coherence-based imaging technique, to address the challenges in imaging technically difficult patients. Although both families of techniques have shown some success in improving the image quality in the presence of a mild level of phase aberration and/or random noise, they often fail to achieve meaningful improvements in the image quality and, in some cases, even create severe image artifacts. In this paper, we employ an adaptive filtering technique called frequency-space prediction filtering (FXPF), which we recently introduced in ultrasound imaging, to overcome the weaknesses of existing techniques and achieve image quality improvements more effectively under varying levels of phase aberration and random noise. Using simulated and experimental phantom data with varying levels of phase aberration and random noise, we evaluate and compare the performance of FXPF with the most representative technique for each category: nearest-neighbor cross correlation (NNCC)-based PAC and the generalized coherence factor (GCF). Our simulation, experimental phantom, and in vivo results demonstrate that FXPF is highly robust in varying levels of phase aberration and noise, and always outperforms both NNCC-based PAC and GCF in terms of the contrast-to-noise ratio (CNR) and the contrast when both random noise and phase aberration are present.

ASAP: Super-Contrast Vasculature Imaging Using Coherence Analysis and High Frame-Rate Contrast Enhanced Ultrasound

The very high frame rate afforded by ultrafast ultrasound, combined with microbubble contrast agents, opens new opportunities for imaging tissue microvasculature. However, new imaging paradigms are required to obtain superior image quality from the large amount of acquired data while allowing real-time implementation. In this paper, we report a technique-acoustic sub-aperture processing (ASAP)-capable of generating very high contrast/signal-to-noise ratio (SNR) images of macro-and microvessels, with similar computational complexity to classical power Doppler (PD) imaging. In ASAP, the received data are split into subgroups. The reconstructed data from each subgroup are temporally correlated over frames to generate the final image. As signals in subgroups are correlated but the noise is not, this substantially reduces the noise floor compared to PD. Using a clinical imaging probe, the method is shown to visualize vessels down to $200~\mu \text{m}$ with a SNR of 10 dB higher than PD and to resolve microvascular flow/perfusion information in rabbit kidneys noninvasively in vivo at multiple centimeter depths. With careful filter design, the technique also allows the estimation of flow direction and the separation of fast flow from tissue perfusion. ASAP can readily be implemented into hardware/firmware for real-time imaging and can be applied to contrast enhanced and potentially noncontrast imaging and 3-D imaging.

Performance Evaluation of Adaptive Imaging Based on Multiphase Apodization with Cross-correlation: A Pilot Study in Abdominal Ultrasound

Degradation of image contrast caused by phase aberration, off-axis clutter, and reverberation clutter remains one of the most important problems in abdominal ultrasound imaging. Multiphase apodization with cross-correlation (MPAX) is a novel beamforming technique that enhances ultrasound image contrast by adaptively suppressing unwanted acoustic clutter. MPAX employs multiple pairs of complementary sinusoidal phase apodizations to intentionally introduce grating lobes that can be used to derive a weighting matrix, which mostly preserves the on-axis signals from tissue but reduces acoustic clutter contributions when multiplied with the beamformed radio-frequency (RF) signals. In this paper, in vivo performance of the MPAX technique was evaluated in abdominal ultrasound using data sets obtained from 10 human subjects referred for abdominal ultrasound at the USC Keck School of Medicine. Improvement in image contrast was quantified, first, by the contrast-to-noise ratio (CNR) and, second, by the rating of two experienced radiologists. The MPAX technique was evaluated for longitudinal and transverse views of the abdominal aorta, the inferior vena cava, the gallbladder, and the portal vein. Our in vivo results and analyses demonstrate the feasibility of the MPAX technique in enhancing image contrast in abdominal ultrasound and show potential for creating high contrast ultrasound images with improved target detectability and diagnostic confidence.