Kazuyuki Dei

A model and regularization scheme for ultrasonic beamforming clutter reduction

Acoustic clutter produced by off-axis and multipath scattering is known to cause image degradation, and in some cases these sources may be the prime determinants of in vivo image quality. We have previously shown some success addressing these sources of image degradation by modeling the aperture domain signal from different sources of clutter, and then decomposing aperture domain data using the modeled sources. Our previous model had some shortcomings including model mismatch and failure to recover B-Mode speckle statistics. These shortcomings are addressed here by developing a better model and by using a general regularization approach appropriate for the model and data. We present results with L1 (lasso), L2 (ridge), and L1/L2 combined (elastic-net) regularization methods. We call our new method aperture domain model image reconstruction (ADMIRE). Our results demonstrate that ADMIRE with L1 regularization, or weighted toward L1 in the case of elastic-net regularization, have improved image quality. L1 by itself works well, but additional improvements are seen with elastic-net regularization over the pure L1 constraint. On in vivo example cases, L1 regularization showed mean contrast improvements of 4.6 and 6.8 dB on fundamental and harmonic images, respectively. Elastic net regularization (α = 0.9) showed mean contrast improvements of 17.8 dB on fundamental images and 11.8 dB on harmonic images. We also demonstrate that in uncluttered Field II simulations the de-cluttering algorithm produces the same contrast, contrast-to-noise ratio, and speckle SNR as normal B-mode imaging, demonstrating that ADMIRE preserves typical image features.

Nonlinear beamforming of aperture domain signals

Image quality continues to be a challenge for medical ultrasound. Recent evidence implicates wavefront distortion from sound speed inhomogeneity and reverberation. To target the reverberation problem, we developed an algorithm called Aperture Domain Model Image REconstruction (ADMIRE). ADMIRE explicitly models, identifies and suppresses acoustic clutter resulting from multipath or off-axis scattering. Also because multipath scattering can be hard to study, we introduce a new method for obtaining pulse-echo measures of reverberation from ex vivo tissue samples. We demonstrate that ADMIRE improves contrast and CNR by 7.1±2.5 dB and 0.86±0.92 dB, respectively. We also apply ADMIRE to multipath scattering from a porcine abdominal wall layer and show that we can reduce the relative clutter level by 13 dB compared to standard beamforming.

An improved acoustic clutter model and direct in vivo assessment of off-axis and multipath clutter energy in the liver

The role of multipath and off-axis scattering induced clutter has recently received renewed attention. In order to address these clutter sources and preserve the ultrasound channel data, we proposed a model based approach. The original model had some shortcomings and did not preserve the expected speckle SNR. Here we present a new model and decomposition scheme that preserves the expected speckle statistics of B-mode images. The new speckle statistics are 1.90±0.057 compared to 1.32±0.047 for the old model. The recovery of speckle SNR also results in improvements in contrast-to-noise ratio compared to both normal B-mode and the previous method.

The Impact of Model-Based Clutter Suppression on Cluttered, Aberrated Wavefronts

Recent studies reveal that both phase aberration and reverberation play a major role in degrading ultrasound image quality. We previously developed an algorithm for suppressing clutter, but we have not yet tested it in the context of aberrated wavefronts. In this paper, we evaluate our previously reported algorithm, called aperture domain model image reconstruction (ADMIRE), in the presence of phase aberration and in the presence of multipath scattering and phase aberration. We use simulations to investigate phase aberration corruption and correction in the presence of reverberation. As part of this paper, we observed that ADMIRE leads to suppressed levels of aberration. In order to accurately characterize aberrated signals of interest, we introduced an adaptive component to ADMIRE to account for aberration, referred to as adaptive ADMIRE. We then use ADMIRE, adaptive ADMIRE, and conventional filtering methods to characterize aberration profiles on in vivo liver data. These in vivo results suggest that adaptive ADMIRE could be used to better characterize a wider range of aberrated wavefronts. The aberration profiles’ full-width at half-maximum of ADMIRE, adaptive ADMIRE, and postfiltered data with 0.4-

Computationally-efficient model-based clutter suppression with ADMIRE

{\rm mm}^{-1}

A Robust Method for Ultrasound Beamforming in the Presence of Off-Axis Clutter and Sound Speed Variation

spatial cutoff frequency are 4.0 ± 0.28 mm, 2.8 ± 1.3 mm, and 2.8 ± 0.57 mm, respectively, while the average root-mean square values in the same order are 16 ± 5.4 ns, 20 ± 6.3 ns, and 19 ± 3.9 ns, respectively. Finally, because ADMIRE suppresses aberration, we perform a limited evaluation of image quality using simulations and in vivo data to determine how ADMIRE and adaptive ADMIRE perform with and without aberration correction.

Feasibility of non-linear beamforming ultrasound methods to characterize and size kidney stones

Aperture domain model image reconstruction (ADMIRE) algorithm demonstrates off-axis and reverberation clutter suppression while preserving the signal of interest. However, ADMIRE is computationally slow due to the large size of the model matrix. Generating a large in vivo image may require a total serial run-time of over ten hours per frame. Such a lengthy run-time limits the usefulness of ADMIRE in practice. The goal of this study is to accelerate ADMIRE without losing image information. We used a singular value decomposition (SVD) algorithm to reduce the number of predictors in the ADMIRE model matrix, which is necessarily oversampled due to the correlation characteristics of reverberation clutter. To assess the SVD-based reduced model, we simulated 12 realizations of an anechoic cyst phantom with varying levels of clutter. We then measured image quality metrics resulting from the full and reduce models, along with delay-and-sum (DAS). The contrast improvement relative to DAS for the full model was 3.2 ± 0.4 dB, 12.6 ± 1.0 dB, 8.2 ± 0.7 dB and 4.7 ± 1.1 dB for the uncluttered, -10, 0 and 10 dB of signal-to-clutter ratio, while the improvement for the reduced model was 3.6 ± 0.3 dB, 12.7 ± 0.7 dB, 8.2 ± 0.4 dB and 4.7 ± 0.6 dB for each case, respectively. We also measured the total serial run-time of each form of ADMIRE in MATLAB on a 3.40 GHz CPU desktop computer. The full model had 1,320 predictors versus 96 in the reduced model at the focal depth. This resulted in total single-core serial run-times of 1,294 sec and 91 sec for the full versus reduced models, respectively.

ADMIRE applied to fundamental and harmonic data acquired using a modern clinical platform

HIGHLIGHTSWe introduced a model‐based beamforming algorithm.We assessed the algorithms limitation and ability to suppress off‐axis scattering.We tested the performance of ADMIRE in the presence of gross sound speed deviation. ABSTRACT Previously, we introduced a model‐based beamforming algorithm to suppress ultrasound imaging artifacts caused by clutter sources, such as reverberation and off‐axis scattering. We refer to this method as aperture domain model image reconstruction (ADMIRE). In this study, we evaluated the algorithms limitations and ability to suppress off‐axis energy using Field II‐based simulations, experimental phantoms and in vivo data acquired by a Verasonics ultrasound system with a curvilinear transducer (C5–2). We compared image quality derived from a standard delay‐and‐sum (DAS) beamformer, DAS with coherence factor (CF) weighting, ADMIRE and ADMIRE plus CF weighting. Simulations, phantoms and in vivo scan results demonstrate that ADMIRE substantially suppresses off‐axis energy, while preserving the spatial resolution of standard DAS beamforming. We also observed that ADMIRE with CF weighting further improves some aspects of image quality. We identified limitations of ADMIRE when suppressing off‐axis clutter in the presence of strong scattering, and we suggest a solution. Finally, because ADMIRE is a model‐based beamformer, we used simulated phantoms to test the performance of ADMIRE under model‐mismatch caused by gross sound speed deviation. The impact of sound speed errors largely mimics DAS beamforming, but ADMIRE never does worse than DAS itself in resolution or contrast. As expected the CF weighting used as a post processing technique provides a boost in contrast but decreases CNR and speckle SNR. The results indicate that ADMIRE is robust in terms of model‐mismatch caused by sound speed variation, especially when the actual sound speed is slower than the assumed sound speed. As an example, the image contrast obtained using DAS, DAS+CF, ADMIRE and ADMIRE+CF in the presence of −5% gross sound speed error are 24.9±0.71dB, 39.1±1.2dB, 43.2±2.3dB and 52.5±2.9dB, respectively.

Aperture domain model image reconstruction (ADMIRE) with plane wave synthesis

Purpose Ultrasound methods for kidney stone imaging suffer from poor sensitivity and size overestimation. The study objective was to demonstrate feasibility of non-linear ultrasound beamforming methods for stone imaging, including plane wave synthetic focusing (PWSF), short-lag spatial coherence (SLSC) imaging, mid-lag spatial coherence (MLSC) imaging with incoherent compounding, and aperture domain model image reconstruction (ADMIRE). Materials and methods The ultrasound techniques were evaluated in an in vitro kidney stone model and in a pilot study of 5 human stone formers (n = 6 stones). Stone contrast, contrast-to-noise ratio (CNR), sizing, posterior shadow contrast, and shadow width sizing were compared among the different techniques and to B-mode. CT imaging within 60 days was considered the gold standard stone size. Paired t-tests using Bonferroni correction were performed to evaluate comparing each technique with B-mode. Results Mean CT measured stone size was 6.0mm (range 2.9–12.2mm) with mean skin-to-stone distance 10.2cm (range 5.4–16.3cm). Compared to B-mode, stone contrast was best with ADMIRE (mean +12.2dB), while SLSC and MLSC showed statistically improved CNR. Sizing was best with ADMIRE (mean +1.3mm error), however this was not significantly improved over B-mode (+2.4mm). PWSF performed similarly to B-mode for stone contrast, CNR, SNR, and stone sizing. In the in vitro model, the shadow contrast was highest with ADMIRE (mean 10.5 dB vs 3.1 dB with B-mode). Shadow sizing was best with SLSC (mean error +0.9mm ± 2.9), however the difference compared to B-mode was not significant. Conclusions The detection and sizing of stones are feasible with advanced beamforming methods with ultrasound. ADMIRE, SLSC, and MLSC hold promise for improving stone detection, shadow contrast, and sizing.

Plane wave image quality improvement using ADMIRE algorithm

Previous studies demonstrated that our aperture domain model image reconstruction (ADMIRE) beamforming algorithm mitigates some common ultrasound imaging artifacts, which may increase ultrasounds clinical utility and reliability. Specifically, ADMIRE can suppress clutter caused by reverberation, off-axis scattering and wavefront aberration. Along with this, we demonstrated that ADMIRE is robust to model-mismatch caused by gross sound speed deviation. These findings suggest that ADMIRE may be an effective tool to provide high quality images in real clinical applications. Many of our previous effort have occurred on research platforms, but it is thought that dedicated clinical systems have better front-end electronics and transducers compared to research oriented platforms. If this is true then it is important to perform in vivo evaluations using the highest quality data possible in order to appropriately characterize (and not overemphasize possible) algorithmic gains. To this end, we modified a Siemens ACUSON SC2000 ultrasound system to capture I/Q channel signals. We acquired channel data using a full synthetic receive sequence. We also acquired channel data in conjunction with pulse inversion sequencing to obtain harmonic images. In this study, we collected data from a tissue-mimicking phantom and a human subjects abdomen and liver. We reconstructed both fundamental and harmonic B-mode images before and after applying ADMIRE. We then measured contrast and contrast-to-noise ratio (CNR). When comparing in vivo images, ADMIRE using low and high degrees of freedom improves contrast by 12.2 ± 2.6 dB and 2.5 ± 0.5 dB, respectively, relative to fundamental delay-and-sum(DAS) B-mode, and boosts contrast by 8.7 ± 3.7 dB and 2.0 ± 0.7 dB, respectively, with harmonic B-mode images.