Hypothesis of Improved Visualization of Microstructures in the Interventricular Septum with Ultrasound and Adaptive Beamforming

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.

Increasing frame rate of echocardiography based on a novel 2D spatio-temporal meshless interpolation

BACKGROUND

Increasing temporal resolution through numerical methods aids clinicians to evaluate fast moving structures of the heart with more confidence.

METHODOLOGY

In this study, a spatio-temporal numerical method is proposed to increase the frame rate based on two-dimensional (2D) interpolation. More specifically, we propose a novel intensity variation time surface (IVTS) strategy to incorporate both temporal and spatial information in the reconstruction. In this regard, we exploit radial basis functions (RBFs) for 2D interpolation. The reason for choosing RBFs for this task is manifold. First, RBFs are able to interpolate on large-scale datasets. Moreover, their mathematical implementation is simple. Another important property of this interpolation technique, which is addressed in this study, is its meshless nature. The meshless property enables higher up-sampling (UpS) rates for echocardiography to improve temporal resolution without noticeably degrading image quality. To evaluate the proposed approach, we tested the RBF interpolation on 2D/3D echocardiography datasets. The reconstructed frames were analyzed using different image quality metrics, and the results were compared with two popular techniques from the literature.

RESULTS

The findings demonstrated that, with a down-sampling rate of 3, the proposed technique outperformed the best existing method by 42%, 87%, 8%, and 11%, respectively, in terms of mean square error (MSE), contrast to noise ratio (CNR), peak signal-to-noise ratio (PSNR), and figure of merit (FOM). It should be noted that the proposed method is comparable to the best available method in terms of structural similarity (SSIM) index. Furthermore, when compared to the original images, the results of employing our technique on radio-frequency (RF) level analysis demonstrated that the reconstruction accuracy is satisfactory in terms of image quality criterion.

CONCLUSION

Finally, it is worthwhile noting that the proposed method is better than (or comparable to) the other methods in terms of reconstruction performance and processing time. Therefore, the RBF interpolation can be a promising alternative to the existing methods.

Improving axial resolution based on the deconvolution recovery method combined with adaptive weighting techniques for ultrasound imaging

BACKGROUND

A fundamental challenge in medical ultrasound imaging is to improve the resolution accurately. Adaptive beamforming is often used to improve lateral resolution, such as minimum variance (MV) and phase coherence factor (PCF). However, it is difficult to improve the axial resolution due to the limitation of the spatial pulse length (SPL) of the transmitted signal.

OBJECTIVE

A deconvolution recovery method combines two adaptive weighting techniques to improve axial resolution.

METHODS

A deconvolution recovery (DR) technique is used to improve axial resolution with a shorter SPL. Then, the DR is combined with MV and PCF (DR-MVPCF) to suppress the sidelobe. The influence of different transmission modes, regularization parameters, and the estimation of point spread function are discussed on the proposed algorithm.

RESULTS

In simulation, DR-MVPCF improved axial resolution from 0.41 mm (0.98 λ) to 0.09 mm (0.21 λ) compared with MV-PCF. In the water bath experiment, DR-MVPCF provided improvement of axial resolution from 0.39 mm (0.93 λ) to 0.07 mm (0.17 λ) compared with MV-PCF. In-vivo data experiment, the DR-MVPCF method increased the speckle signal-to-noise ratio and visibility of the structure while the contrast ratio and contrast-noise ratio decreased.

CONCLUSIONS

The proposed method can improve the axial resolution significantly.

High Frame Rate Ultrasound Imaging by Means of Tensor Completion: Application to Echocardiography

High frame rate ultrasound (US) imaging enables the monitoring of fast-moving organs. In echocardiography, this is especially needed due to the existence of rapidly moving structures, such as the heart valves. In the last two decades, various methods have been proposed to improve the frame rate. Here, we propose a novel method, based on binary coding patterns (BCPs) and tensor completion (TC), to increase the temporal resolution (i.e., frame rate) in the preprocessing stage of conventional focused ultrasound imaging (CFUI). The rationale behind our proposal is to perform, at first, the beamforming of a fraction of the scan lines, randomly selected in each frame based on BCP. Then, we reconstruct the missing scan lines through TC. The latter is an effective technique for recovering missing information from a low-rank tensor, based on a small number of observations using rank minimization. Following our approach, reducing the transmissions events needed to generate an image, the frame rate is increased by the same proportion. We have applied the proposed technique to a pre-beamformed radio frequency (RF) echocardiographic dataset. Our results show that we can improve the frame rate by a factor from 3 to 4, while keeping the structural similarity (SSIM) of the reconstructed tensor and the original one at values higher than 0.98.

Improving Image Quality by Deconvolution Recovery Filter in Ultrasound Imaging

Due to the advantages of non-radiation and real-time performance, ultrasound imaging is essential in medical imaging. Image quality is affected by the performance of the transducer in an ultrasound imaging system. For example, the bandwidth controls the pulse length, resulting in different axial resolutions. Therefore, a transducer with a large bandwidth helps to improve imaging quality. However, large bandwidths lead to increased system cost and sometimes a loss of sensitivity and lateral resolution in attenuating media. In this paper, a deconvolution recovery method combined with a frequency-domain filtering technique (DRF) is proposed to improve the imaging quality, especially for the axial resolution. In this method, the received low-bandwidth echo signals are converted into high-bandwidth signals, which is similar to the echo signals produced by a high-bandwidth transducer, and the imaging quality is improved. Simulation and experiment results show that, compared with Delay-and-sum (DAS) method, the DRF method improved axial resolution from 0.60 to 0.41 mm in simulation and from 0.62 to 0.47 mm in the tissue-mimicking phantom experiment. The contrast ratio performance is improved to some extent compared with the DAS in experimental and in-vivo images. Besides, the proposed method has the potential to further improve image quality by combining it with adaptive weightings, such as the minimum variance method.

Minimum variance beamforming combined with covariance matrix-based adaptive weighting for medical ultrasound imaging

BACKGROUND

The minimum variance (MV) beamformer can significantly improve the image resolution in ultrasound imaging, but it has limited performance in noise reduction. We recently proposed the covariance matrix-based statistical beamforming (CMSB) for medical ultrasound imaging to reduce sidelobes and incoherent clutter.

METHODS

In this paper, we aim to improve the imaging performance of the MV beamformer by introducing a new pixel-based adaptive weighting approach based on CMSB, which is named as covariance matrix-based adaptive weighting (CMSAW). The proposed CMSAW estimates the mean-to-standard-deviation ratio (MSR) of a modified covariance matrix reconstructed by adaptive spatial smoothing, rotary averaging, and diagonal reducing. Moreover, adaptive diagonal reducing based on the aperture coherence is introduced in CMSAW to enhance the performance in speckle preservation.

RESULTS

The proposed CMSAW-weighted MV (CMSAW-MV) was validated through simulation, phantom experiments, and in vivo studies. The phantom experimental results show that CMSAW-MV obtains resolution improvement of 21.3% and simultaneously achieves average improvements of 96.4% and 71.8% in average contrast and generalized contrast-to-noise ratio (gCNR) for anechoic cyst, respectively, compared with MV. in vivo studies indicate that CMSAW-MV improves the noise reduction performance of MV beamformer.

CONCLUSION

Simulation, experimental, and in vivo results all show that CMSAW-MV can improve resolution and suppress sidelobes and incoherent clutter and noise. These results demonstrate the effectiveness of CMSAW in improving the imaging performance of MV beamformer. Moreover, the proposed CMSAW with a computational complexity of [Formula: see text] has the potential to be implemented in real time using the graphics processing unit.

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.

Deep Learning for Ultrasound Image Formation: CUBDL Evaluation Framework and Open Datasets

Deep learning for ultrasound image formation is rapidly garnering research support and attention, quickly rising as the latest frontier in ultrasound image formation, with much promise to balance both image quality and display speed. Despite this promise, one challenge with identifying optimal solutions is the absence of unified evaluation methods and datasets that are not specific to a single research group. This article introduces the largest known international database of ultrasound channel data and describes the associated evaluation methods that were initially developed for the challenge on ultrasound beamforming with deep learning (CUBDL), which was offered as a component of the 2020 IEEE International Ultrasonics Symposium. We summarize the challenge results and present qualitative and quantitative assessments using both the initially closed CUBDL evaluation test dataset (which was crowd-sourced from multiple groups around the world) and additional in vivo breast ultrasound data contributed after the challenge was completed. As an example quantitative assessment, single plane wave images from the CUBDL Task 1 dataset produced a mean generalized contrast-to-noise ratio (gCNR) of 0.67 and a mean lateral resolution of 0.42 mm when formed with delay-and-sum beamforming, compared with a mean gCNR as high as 0.81 and a mean lateral resolution as low as 0.32 mm when formed with networks submitted by the challenge winners. We also describe contributed CUBDL data that may be used for training of future networks. The compiled database includes a total of 576 image acquisition sequences. We additionally introduce a neural-network-based global sound speed estimator implementation that was necessary to fairly evaluate the results obtained with this international database. The integration of CUBDL evaluation methods, evaluation code, network weights from the challenge winners, and all datasets described herein are publicly available (visit https://cubdl.jhu.edu for details).

Joint Generalized Coherence Factor and Minimum Variance Beamformer for Synthetic Aperture Ultrasound Imaging

The delay-and-sum (DAS) beamformer is the most commonly used method in medical ultrasound imaging. Compared with the DAS beamformer, the minimum variance (MV) beamformer has an excellent ability to improve lateral resolution by minimizing the output of interference and noise power. However, it is hard to overcome the tradeoff between satisfactory lateral resolution and speckle preservation performance due to the fixed subarray length of covariance matrix estimation. In this study, a new approach for MV beamforming with adaptive spatial smoothing is developed to address this problem. In the new approach, the generalized coherence factor (GCF) is used as a local coherence detection tool to adaptively determine the subarray length for spatial smoothing, which is called adaptive spatial-smoothed MV (AMV). Furthermore, another adaptive regional weighting strategy based on the local signal-to-noise ratio (SNR) and GCF is devised for AMV to enhance the image contrast, which is called GCF regional weighted AMV (GAMV). To evaluate the performance of the proposed methods, we compare them with the standard MV by conducting the simulation, in vitro experiment, and the in vivo rat mammary tumor study. The results show that the proposed methods outperform MV in speckle preservation without an appreciable loss in lateral resolution. Moreover, GAMV offers excellent performance in image contrast. In particular, AMV can achieve maximal improvements of speckle signal-to-noise ratio (SNR) by 96.19% (simulation) and 62.82% (in vitro) compared with MV. GAMV achieves improvements of contrast-to-noise ratio by 27.16% (simulation) and 47.47% (in vitro) compared with GCF. Meanwhile, the losses in lateral resolution of AMV are 0.01 mm (simulation) and 0.17 mm (in vitro) compared with MV. Overall, this indicates that the proposed methods can effectively address the inherent limitation of the standard MV in order to improve the image quality.

Phase Modulation Beamforming for Ultrafast Plane-Wave Imaging

In order to improve the spatial resolution for high-frame-rate imaging, a new image formation approach is proposed based on introducing very weak aberration into received data, then combining the multiple results by taking the pixel-wise standard deviation of multiple aberrated images and subtracting the result from the delay-and-sum image. This approach is demonstrated in simulations, tissue-mimicking phantom experiments, and in vivo imaging. Simulations indicate the lateral full-width half-maximum (FWHM) of targets decreases by 38.24% ± 6.38%. In imaging wire targets in a tissue-mimicking phantom at 7.8 MHz, wire target FWHM decreases by 35.91% ± 5.39%. However, contrast was observed to decrease by 1.23 dB and contrast-to-noise ratio (CNR) by 18.5% in phantom studies due to the subtraction of similar images, which increases the number of dark pixels in the image. Finally, the proposed technique is tested in vivo, with images showing improvements similar to those in tissue-mimicking phantoms, including increased separation between closely spaced targets.

Wireless, Real-Time Plane-Wave Coherent Compounding on an iPhone: A Feasibility Study

The processing power in commercially available hand-held devices has improved dramatically in recent years. In parallel, techniques used in high-frame-rate medical ultrasound imaging, especially plane-wave (PW) imaging, have reduced the number of ultrasound transmissions and amount of data necessary to reconstruct an ultrasound image. In combination, the processing power and data reduction allow all of the processing steps in ultrasound image formation, from raw ultrasound channel data to final rendering, to be performed on a hand-held device. In this study, we send the raw ultrasound channel data from a research scanner wirelessly to an off-the-shelf hand-held device. The hand-held unit's graphical processing unit is processing the raw ultrasound data into the final image, achieving real-time frame rates on the order of 60-90 frames per second (FPS) for a single-angle PW transmission. Higher quality images are achieved by trading off frame rate by coherently compounding multiple PW images, resulting in frame rates on the order of, e.g., 13 FPS when coherently compounding 7 PW transmissions. The presented setup has the potential of providing image quality which could be valuable for simple medical ultrasound diagnostic scans of, e.g., the carotid artery or thyroid. Also, since the computationally expensive beamforming can be done in off-the-shelf devices, this could reduce the price of hand-held ultrasound systems in the future.

Resolving Ultrasound Contrast Microbubbles Using Minimum Variance Beamforming

Minimum Variance (MV) beamforming is known to improve the lateral resolution of ultrasound images and enhance the separation of isolated point scatterers. This paper aims to evaluate the adaptive beamformer's performance with flowing microbubbles (MBs) which are relevant to super-resolution ultrasound imaging. Simulations using point scatterer data from single emissions were complemented by an experimental investigation performed using a capillary tube phantom and the Synthetic Aperture Real-time Ultrasound System (SARUS). The MV performance was assessed by the minimum distance that allows the display of two scatterers positioned side-by-side, the lateral Full-Width-at-Half-Maximum (FWHM), and the Peak-Sidelobe-Level (PSL). In the tube, scatterer responses separated by down to [Formula: see text] (or 1.05λ ) were distinguished by the MV method, while the standard Delay-And-Sum (DAS) beamformers were unable to achieve such separation. Up to ninefold FWHM decrease was also measured in favor of the MV beamformer for individual echoes from MBs. The lateral distance between two scatterers impacted on their FWHM value, and additional differences in the scatterers' axial or out-of-plane position also impacted on their size and appearance. The simulation and experimental results were in agreement in terms of lateral resolution. The point scatterer study showed that the proposed MV imaging scheme provided clear resolution benefits compared to DAS. Current super-resolution methods mainly depend on DAS beamformers. Instead, the use of the MV method may provide a larger number of detected, and potentially better localized, MB scatterers.