Aperture Domain Model Image Reconstruction Research Articles

Enhancing sizing accuracy in ultrasound images with an alternative ADMIRE model and dynamic range considerations

Ultrasound imaging can struggle with sizing accuracy, especially when the targets have a significantly different amplitude compared to the surrounding background. In this work, we consider the challenging task of accurately sizing hyperechoic structures, and specifically kidney stones, where accurate sizing is critical for determining medical intervention. AD-Ex, an extended alternative model of our aperture domain model image reconstruction (ADMIRE) pre-processing method, is introduced and is designed to improve clutter removal and improve sizing accuracy. We compare this method against other resolution enhancing methods such as minimum variance (MV) and generalized coherence factor (GCF), and against those methods using AD-Ex as a pre-processing tool. These methods are evaluated among patients with kidney stone disease, with the task of accurately sizing the stones against the gold standard, computed tomography (CT). Stone ROI’s were selected using contour maps as reference from which the lateral stone size was estimated. Among the in vivo kidney stone cases we processed, AD-Ex+MV had the overall lowest sizing error among the methods, with an average error of 10.8% compared to the next best method AD-Ex which had an average error of 23.4%. For reference, DAS had an average error of 82.4%. Though dynamic range was evaluated to determine optimal thresholding for sizing applications, variability between stone cases was too high for any conclusions to be drawn at this time.

Combining ADMIRE and MV to Improve Image Quality.

Aperture domain model image reconstruction (ADMIRE) is a frequency-domain, model-based beamformer, in part designed for removing reverberation and off-axis clutter. Minimum variance (MV) is alternatively designed to reduce off-axis interference and improve lateral resolution. MV is known to be less effective in high incoherent noise scenarios, and its performance in the presence of reverberation has not been evaluated. By implementing ADMIRE before MV, the benefits of both these beamformers can be achieved. In this article, the assumptions of MV are discussed, specifically their relationship to reverberation clutter. The use of ADMIRE as a preprocessing step to suppress noise from simulations with linear scanning and in vivo curvilinear kidney data is demonstrated, and both narrowband and broadband implementations of MV are applied. With optimal parameters, ADMIRE + MV demonstrated sizing improvements over MV alone by an average of 52.1% in 0-dB signal-to-clutter ratio reverberation cyst simulations and 14.5% in vivo while improving the contrast ratio compared to ADMIRE alone by an average of 15.1% in simulations and 14.0% in vivo. ADMIRE + MV demonstrated a consistent improvement compared to DAS, MV, and ADMIRE both in terms of sizing and contrast ratio.

A Real-Time, GPU-Based Implementation of Aperture Domain Model Image REconstruction.

Multipath and off-axis scattering are two of the primary mechanisms for ultrasound image degradation. To address their impact, we have proposed Aperture Domain Model Image REconstruction (ADMIRE). This algorithm utilizes a model-based approach in order to identify and suppress sources of acoustic clutter. The ability of ADMIRE to suppress clutter and improve image quality has been demonstrated in previous works, but its use for real-time imaging has been infeasible due to its significant computational requirements. However, in recent years, the use of graphics processing units (GPUs) for general-purpose computing has enabled the significant acceleration of compute-intensive algorithms. This is because many modern GPUs have thousands of computational cores that can be utilized to perform massively parallel processing. Therefore, in this work, we have developed a GPU-based implementation of ADMIRE. The implementation on a single GPU provides a speedup of two orders of magnitude when compared to a serial CPU implementation, and additional speedup is achieved when the computations are distributed across two GPUs. In addition, we demonstrate the feasibility of the GPU implementation to be used for real-time imaging by interfacing it with a Verasonics Vantage 128 ultrasound research system. Moreover, we show that other beamforming techniques, such as delay-and-sum (DAS) and short-lag spatial coherence (SLSC), can be computed and simultaneously displayed with ADMIRE. The frame rate depends upon various parameters, and this is exhibited in the multiple imaging cases that are presented. An open-source code repository containing CPU and GPU implementations of ADMIRE is also provided.

Iterative Model-Based Beamforming for High Dynamic Range Applications.

Clutter produced using bright acoustic sources can obscure weaker acoustic targets, degrading the quality of the image in scenarios with high dynamic ranges. Many adaptive beamformers seek to improve image quality by reducing these sidelobe artifacts, generating a boost in contrast ratio or contrast-to-noise ratio. However, some of these beamformers inadvertently introduce a dark region artifact in place of the strong clutter, a situation that occurs when both clutter and the underlying signal of interest are removed. We introduce the iterative aperture domain model image reconstruction (iADMIRE) method that is designed to reduce clutter while preserving the underlying signal. We compare the contrast ratio dynamic range (CRDR) of iADMIRE to several other adaptive beamformers plus delay-and-sum (DAS) to quantify the accuracy and reliability of the reported measured contrast for each beamformer over a wide range of contrast levels. We also compare all beamformers in the presence of bright targets ranging from 40 to 120 dB to observe the presence of sidelobes. In cases with no added reverberation clutter, iADMIRE had a CRDR of 75.6 dB when compared with the next best method DAS with 60.8 dB. iADMIRE also demonstrated the best performance for levels of reverberation clutter up to 0-dB signal-to-clutter ratio. Finally, iADMIRE restored underlying speckle signal in dark artifact regions while suppressing sidelobes in bright target cases up to 100 dB.

Computationally Efficient Implementation of Aperture Domain Model Image Reconstruction.

Aperture domain model image reconstruction (ADMIRE) is a useful tool to mitigate ultrasound imaging artifacts caused by acoustic clutter. However, its lengthy run-time impairs its usefulness. To overcome this drawback, we evaluated the reduced model methods with otherwise similar performance to ADMIRE. We also assessed other approaches to speed up ADMIRE, including the use of different levels of short-time Fourier transform (STFT) window overlap and examining the degrees of freedom of the model fit. In this study, we conducted an analysis of the reduced models, including those using Gram-Schmidt orthonormalization (GSO), singular value decomposition (SVD), and independent component analysis (ICA). We evaluated these reduced models using the data from simulations, experimental phantoms, and in vivo liver scans. We then tested ADMIRE's performance using full, GSO, SVD, and ICA-fourth-order blind identification (ICA-FOBI) models. The results from simulations, experimental phantoms, and in vivo data indicate that a model reduced using the ICA-FOBI method is the most promising for accelerating ADMIRE implementation. In the in vivo liver data, the improvements in contrast relative to delay-and-sum (DAS) using a full model, GSO, SVD, and ICA-FOBI models are 6.29 ± 0.25 dB, 11.88 ± 0.90 dB, 9.01 ± 0.67 dB, and 6.36 ± 0.27 dB, respectively; whereas, the contrast-to-noise ratio (CNR) improvement values in the same order are 0.04 ± 0.06 dB, -1.70 ± 0.17 dB, -1.51 ± 0.19 dB, and 0.12 ± 0.07 dB, respectively. The implementation of ADMIRE using the reduced model methods can decrease ADMIRE's computational complexity over three orders of magnitude. The use of a 50% STFT window overlap reduces ADMIRE's serial run time by more than one order of magnitude without any remarkable loss of image quality, when compared to the use of a 90% window overlap used previously. Based on these findings, a combination of the ICA-FOBI model and the use of a 50% STFT window overlap makes the ADMIRE algorithm more computationally efficient.

Advanced beamforming for improved functional assessment in echocardiography

Echocardiography is one of the most used medical imaging exams. The data from these exams are often used to compute quantitative metrics of cardiac health including measures such as the global longitudinal strain (GLS) or ejection fraction. Metrics related to blood flow are also derived from echocardiography data. These metrics have great potential because ideally, they provide quantitative biomarkers to monitor cardiac function over time and compare patient function to population values. Unfortunately, echocardiography data is often severely corrupted by various forms of acoustic clutter including wavefront aberration, reverberation and off-axis scattering from bright structures like the ribs and lungs. These sources of clutter degrade ultrasound image quality and corrupt the ability to derive reliable quantitative biomarkers of cardiac function. To resolve problems like these, we have been developing non-linear (mathematically) beamformers. These include our aperture domain model image reconstruction (ADMIRE) beamformer and, more recently, our deep neural network beamformer. We have shown that these beamformers are able to reduce errors related to wavefront aberration, reverberation and off-axis scattering, and also that the ADMIRE beamformer can eliminate the underestimation of global longitudinal strain caused by high levels of stationary reverberation caused by the chest wall.

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

PurposeUltrasound 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 methodsThe 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.ResultsMean 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.ConclusionsThe 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.

Model-based beamforming with plane wave synthesis in medical ultrasound.

We are interested in examining how our model-based beamforming algorithm, referred to as aperture-domain model image reconstruction (ADMIRE), performs on plane wave sequences in conjunction with synthetic aperture beamforming. We also aim to identify the impact of ADMIRE applied before and after synthetic focusing. We employed simulated phantoms using Field II and tissue-mimicking phantoms to evaluate ADMIRE as applied to synthetic sequencing. We generated plane wave images with and without synthetic aperture focusing (SAF) and measured contrast and contrast-to-noise ratio (CNR). For simulated cyst images formed from single plane waves, the contrast for delay-and-sum (DAS) and ADMIRE are 15.64 and 28.34dB, respectively, whereas the CNR are 1.76 and 3.90dB, respectively. We also applied ADMIRE to simulated resolution phantoms having a point target at 3cm depth on-axis. We simulated the point spread functions from data obtained from 1 plane wave and 75 steered plane waves, along with linear scans with 3 and 4cm- focal depths. We then compared the outcome of applying ADMIRE before and after SAF using 3 and 11 steered plane waves. Finally, we applied this to an in vivo carotid artery. Based on the findings in this study, ADMIRE can be adapted to full field insonification sequences to improve image quality in plane wave imaging. Additionally, we investigated how robustly ADMIRE performs in the presence of random noise. We then address identified limitations using a conventional envelope detection method with decluttered signals.

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

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 algorithm’s 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 ± 0.71 dB, 39.1 ± 1.2 dB, 43.2 ± 2.3 dB and 52.5 ± 2.9 dB, respectively.

MP63-01 FEASIBILITY OF NEXT GENERATION NON-LINEAR BEAMFORMING ULTRASOUND METHODS TO CHARACTERIZE AND SIZE KIDNEY STONES

You have accessJournal of UrologyImaging/Radiology: Uroradiology IV1 Apr 2018MP63-01 FEASIBILITY OF NEXT GENERATION NON-LINEAR BEAMFORMING ULTRASOUND METHODS TO CHARACTERIZE AND SIZE KIDNEY STONES Ryan Hsi, Siegfried Schlunk, Jaime Tierney, Rebecca Jones, Mark George, Pranav Karve, Ravindra Duddu, and Brett Byram Ryan HsiRyan Hsi More articles by this author , Siegfried SchlunkSiegfried Schlunk More articles by this author , Jaime TierneyJaime Tierney More articles by this author , Rebecca JonesRebecca Jones More articles by this author , Mark GeorgeMark George More articles by this author , Pranav KarvePranav Karve More articles by this author , Ravindra DudduRavindra Duddu More articles by this author , and Brett ByramBrett Byram More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2018.02.2024AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Advanced ultrasound beamforming methods for imaging kidney stones may help address the sensitivity and size overestimation problems with standard B-mode ultrasound. The study objective was to demonstrate feasibility of advanced ultrasound beamforming methods 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). METHODS The ultrasound techniques were evaluated using a research-based ultrasound system applied to an in vitro kidney stone model (n=12, 4 and 8cm depths) and in a pilot study of human stone formers (n=5). Stone diameter sizing, shadow contrast, and stone contrast were compared among the different techniques and to B-mode. CT measurements were used to determine true stone size in the pilot study. Analysis of variance was used to analyze the differences among group means, and t-test when comparing to B-mode, with p<0.05 considered significant. RESULTS All stones were detectable with each method. In the in vitro study, MLSC performed best compared to B-mode for mean stone contrast (31.7 dB vs 14.7 dB, p<0.0025). On average, B-mode sizing error ± SD was >1mm (1.2±1.1 mm), while those for PWSF, ADMIRE, and MLSC were <1mm (-0.3±2.9mm, 0.6±0.8, 0.8±0.8, respectively). The mean posterior stone shadow contrast was highest with ADMIRE overall (ptrend=0.008), and when comparing between ADMIRE and B-mode (8.0 dB vs 2.1 dB, respectively; p=0.0131). In the pilot clinical study (Figure), mean stone contrast was best with ADMIRE and MLSC than with B mode (28.5 dB and 27.8 dB vs 18.1dB, respectively; ptrend=0.002). Mean sizing error was best with ADMIRE (+1.2mm), however there was no statistically significant difference when compared with B mode (+2.7mm). CONCLUSIONS The detection and sizing of stones are feasible with advanced beamforming methods with ultrasound. ADMIRE and MLSC hold promise for improving stone contrast, shadow contrast, and reducing sizing error. Future work will include imaging a broader population of human stone formers. © 2018FiguresReferencesRelatedDetails Volume 199Issue 4SApril 2018Page: e840-e841 Advertisement Copyright & Permissions© 2018MetricsAuthor Information Ryan Hsi More articles by this author Siegfried Schlunk More articles by this author Jaime Tierney More articles by this author Rebecca Jones More articles by this author Mark George More articles by this author Pranav Karve More articles by this author Ravindra Duddu More articles by this author Brett Byram More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

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- mm-1 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.

Pseudononlinear ultrasound simulation approach for reverberation clutter.

Multipath scattering, or reverberation, takes a substantial toll on image quality in many clinical exams. We have suggested a model-based solution to this problem, which we refer to as aperture domain model image reconstruction (ADMIRE). For ADMIRE to work well, it must be trained with precisely characterized data. To solve this specific problem and the general problem of efficiently simulating reverberation, we propose an approach to simulate reverberation with linear simulation tools. Our simulation method defines total propagation time, first scattering site, and a final scattering site. We use a linear simulation package, such as Field II, to simulate scattering from the final site and then shift the simulated wavefront later in time based on the total propagation time and the geometry of the first scattering site. We validate our simulations using theoretical descriptions of clutter in the literature and data acquired from ex vivo tissue. We found that ex vivo tissue clutter had a mean speckle SNR of [Formula: see text], which we could simulate with about 2 scatterers per resolution cell. Axial clutter distributions drawn from an exponential distribution with a mean of 5mm and at least 0.5 scatters per resolution cell resulted in clutter that was statistically indistinguishable from the van Cittert-Zernike behavior predicted by literature.

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 decluttering algorithm produces the same contrast, contrast-tonoise ratio, and speckle SNR as normal B-mode imaging, demonstrating that ADMIRE preserves typical image features.