Motion Artifacts and Correction in Multipulse High-Frame Rate Contrast-Enhanced Ultrasound

Assessment of Coronary Microcirculation with High Frame-Rate Contrast-Enhanced Echocardiography

OBJECTIVE

Assessing myocardial perfusion in acute myocardial infarction is important for guiding clinicians in choosing appropriate treatment strategies. Echocardiography can be used due to its direct feedback and bedside nature, but it currently faces image quality issues and an inability to differentiate coronary macro- from micro-circulation. We previously developed an imaging scheme using high frame-rate contrast-enhanced ultrasound (HFR CEUS) with higher order singular value decomposition (HOSVD) that provides dynamic perfusion and vascular flow visualization. In this study, we aim to show the ability of this technique to image perfusion deficits and investigate the potential occurrence of false-positive contrast detection.

METHODS

We used a porcine model comprising occlusion and release of the left anterior descending coronary artery. During slow contrast agent infusion, the afore-mentioned imaging scheme was used to capture and process the data offline using HOSVD.

RESULTS

Fast and slow coronary flow was successfully differentiated, presumably representing the different compartments of the micro-circulation. Low perfusion was seen in the area that was affected, as expected by vascular occlusion. Furthermore, we also imaged coronary flow dynamics before, during and after release of the occlusion, the latter showing hyperemia as expected. A contrast agent destruction test showed that the processed images contained actual contrast signal in the cardiac phases with minimal motion. With larger tissue motion, tissue signal leaked into the contrast-enhanced images.

CONCLUSION

Our results demonstrate the feasibility of HFR CEUS with HOSVD as a viable option for assessing myocardial perfusion. Flow dynamics were resolved, which potentially helped to directly evaluate coronary flow deficits.

Fast inter-frame motion correction in contrast-free ultrasound quantitative microvasculature imaging using deep learning

Contrast-free ultrasound quantitative microvasculature imaging shows promise in several applications, including the assessment of benign and malignant lesions. However, motion represents one of the major challenges in imaging tumor microvessels in organs that are prone to physiological motions. This study aims at addressing potential microvessel image degradation in in vivo human thyroid due to its proximity to carotid artery. The pulsation of the carotid artery induces inter-frame motion that significantly degrades microvasculature images, resulting in diagnostic errors. The main objective of this study is to reduce inter-frame motion artifacts in high-frame-rate ultrasound imaging to achieve a more accurate visualization of tumor microvessel features. We propose a low-complex deep learning network comprising depth-wise separable convolutional layers and hybrid adaptive and squeeze-and-excite attention mechanisms to correct inter-frame motion in high-frame-rate images. Rigorous validation using phantom and in-vivo data with simulated inter-frame motion indicates average improvements of 35% in Pearson correlation coefficients (PCCs) between motion corrected and reference data with respect to that of motion corrupted data. Further, reconstruction of microvasculature images using motion-corrected frames demonstrates PCC improvement from 31 to 35%. Another thorough validation using in-vivo thyroid data with physiological inter-frame motion demonstrates average improvement of 20% in PCC and 40% in mean inter-frame correlation. Finally, comparison with the conventional image registration method indicates the suitability of proposed network for real-time inter-frame motion correction with 5000 times reduction in motion corrected frame prediction latency.

An Adaptive Harmonic Separation Technique for Ultrasound Harmonic Imaging

OBJECTIVE

An adaptive harmonic separation (HS) technique is proposed to overcome the limitations in conventional filtering techniques for ultrasound (US) tissue harmonic imaging (THI).

METHODS

Based on expectation-maximization source separation, the proposed HS technique adaptively models the depth-varying fundamental and harmonic components in the frequency domain and separates the two by applying their calculated posterior probabilities. Phantom experiments with a Tx center frequency of 2 MHz are conducted to evaluate the proposed HS-based US THI schemes.

RESULTS

The phantom images show that the proposed single-pulse THI scheme utilizing the HS technique provides not only an average improvement of 19.2% in axial resolution compared to the conventional bandpass filtering scheme but also similar image quality to that of the conventional pulse-inversion (PI) scheme which requires two Tx/Rx sequences for each scan line. Furthermore, when combined with the PI technique, the HS technique provides a uniform axial resolution over the entire 170 mm imaging depth with an average improvement of 17.1% compared to the conventional PI scheme.

CONCLUSION

These results show that the proposed adaptive HS technique is capable of improving both the frame rate and the image quality of US THI.

The Effect of Spatial Velocity Gradients on Block-Matching Accuracy for Ultrasound Velocimetry

OBJECTIVE

Block matching serves as the foundation for ultrasound velocimetry techniques such as blood speckle tracking and echo-particle image velocimetry. Any spatial velocity gradients (SVGs) inside a block-matching pair will result in tracking error, due to both the finite block size and the ultrasound point-spread-function. We assess, using an in silico sinusoidal flow phantom, the effect of SVG magnitude and beam-to-flow angle on block-matching bias and precision. Secondarily we assess the effect that SVGs have on velocimetry bias when using angled plane-wave compounding.

METHODS

The magnitude and angle of SVGs were varied by adjusting the wavelength and direction of a sinusoidal flow profile. Scatterers displaced by this flow profile were used for simulating ultrasound radio frequency data at discrete time points. After beamforming, the 2-D flow field was estimated using block matching. Two imaging sequences were tested, a single plane-wave and a three-angled plane-wave.

RESULTS

Smaller sinusoidal flow wavelengths resulted in increased bias and reduced precision, revealing an inverse relationship between sinusoidal flow wavelength and tracking error, with median errors ranging from 69%-90% for the smallest flow wavelengths (highest SVGs) down to 3%-5% for the largest (lowest SVGs). The SVG angle was also important, in which lateral SVGs (with axially oriented flows) resulted in significant speckle decorrelation and high tracking errors in regions with high SVGs. Conversely, axial SVGs (laterally oriented flow) experienced higher bias in the peak velocity regions of the flow profile. Coherent compounding resulted in higher velocity errors than using a single transmission for lateral SVGs but not for axial SVGs.

CONCLUSION

The highest SVGs that could be measured with ≤10% error was when the sinusoidal flow wavelength was less than 20 times the ultrasound pulse wavelength. The clinical significance is that the high SVGs present in high kinetic energy flows, such as severe carotid stenosis and aortic regurgitation, will limit the ability to accurately quantify the velocities in these flow structures.

Ultrafast Cardiac Imaging Using Deep Learning for Speckle-Tracking Echocardiography

High-quality ultrafast ultrasound imaging is based on coherent compounding from multiple transmissions of plane waves (PW) or diverging waves (DW). However, compounding results in reduced frame rate, as well as destructive interferences from high-velocity tissue motion if motion compensation (MoCo) is not considered. While many studies have recently shown the interest of deep learning for the reconstruction of high-quality static images from PW or DW, its ability to achieve such performance while maintaining the capability of tracking cardiac motion has yet to be assessed. In this article, we addressed such issue by deploying a complex-weighted convolutional neural network (CNN) for image reconstruction and a state-of-the-art speckle-tracking method. The evaluation of this approach was first performed by designing an adapted simulation framework, which provides specific reference data, i.e., high-quality, motion artifact-free cardiac images. The obtained results showed that, while using only three DWs as input, the CNN-based approach yielded an image quality and a motion accuracy equivalent to those obtained by compounding 31 DWs free of motion artifacts. The performance was then further evaluated on nonsimulated, experimental in vitro data, using a spinning disk phantom. This experiment demonstrated that our approach yielded high-quality image reconstruction and motion estimation, under a large range of velocities and outperforms a state-of-the-art MoCo-based approach at high velocities. Our method was finally assessed on in vivo datasets and showed consistent improvement in image quality and motion estimation compared to standard compounding. This demonstrates the feasibility and effectiveness of deep learning reconstruction for ultrafast speckle-tracking echocardiography.

Higher Order Singular Value Decomposition Filter for Contrast Echocardiography

Assessing the coronary circulation with contrast-enhanced echocardiography has high clinical relevance. However, it is not being routinely performed in clinical practice because the current clinical tools generally cannot provide adequate image quality. The contrast agent's visibility in the myocardium is generally poor, impaired by motion and nonlinear propagation artifacts. The established multipulse contrast schemes (MPCSs) and the more experimental singular value decomposition (SVD) filter also fall short to solve these issues. Here, we propose a scheme to process amplitude modulation/amplitude-modulated pulse inversion (AM/AMPI) echoes with higher order SVD (HOSVD) instead of conventionally summing the complementary pulses. The echoes from the complementary pulses form a separate dimension in the HOSVD algorithm. Then, removing the ranks in that dimension with dominant coherent signals coming from tissue scattering would provide the contrast detection. We performed both in vitro and in vivo experiments to assess the performance of our proposed method in comparison with the current standard methods. A flow phantom study shows that HOSVD on AM pulsing exceeds the contrast-to-background ratio (CBR) of conventional AM and an SVD filter by 10 and 14 dB, respectively. In vivo porcine heart results also demonstrate that, compared to AM, HOSVD improves CBR in open-chest acquisition (up to 19 dB) and contrast ratio (CR) in closed-chest acquisition (3 dB).

Doppler and Pair-Wise Optical Flow Constrained 3D Motion Compensation for 3D Ultrasound Imaging

Volumetric (3D) ultrasound imaging using a 2D matrix array probe is increasingly developed for various clinical procedures. However, 3D ultrasound imaging suffers from motion artifacts due to tissue motions and a relatively low frame rate. Current Doppler-based motion compensation (MoCo) methods only allow 1D compensation in the in-range dimension. In this work, we propose a new 3D-MoCo framework that combines 3D velocity field estimation and a two-step compensation strategy for 3D diverging wave compounding imaging. Specifically, our framework explores two constraints of a round-trip scan sequence of 3D diverging waves, i.e., Doppler and pair-wise optical flow, to formulate the estimation of the 3D velocity fields as a global optimization problem, which is further regularized by the divergence-free and first-order smoothness. The two-step compensation strategy is to first compensate for the 1D displacements in the in-range dimension and then the 2D displacements in the two mutually orthogonal cross-range dimensions. Systematical in-silico experiments were conducted to validate the effectiveness of our proposed 3D-MoCo method. The results demonstrate that our 3D-MoCo method achieves higher image contrast, higher structural similarity, and better speckle patterns than the corresponding 1D-MoCo method. Particularly, the 2D cross-range compensation is effective for fully recovering image quality.

Fully automatic carotid arterial stiffness assessment from ultrasound videos based on machine learning

Arterial stiffness (AS) refers to the loss of arterial compliance and alterations in vessel wall properties. The study of local carotid stiffness (CS) is particularly important since carotid artery stiffening raises the risk of stroke, cognitive impairment, and dementia. So, stiffness measurement as a screening tool approach is crucial because it can reduce mortality and facilitate therapy planning. This study aims to evaluate the stiffness of the CCA using machine learning (ML) through the features of diameter change (ΔD) and stiffness parameters. This study was conducted in seven stages: data collection, preprocessing, CCA segmentation, CCA lumen diameter (DCCA) computing during cardiac cycles, denoising signals of DCCA, computational of AS parameters, and stiffness assessment using ML. The 51 videos (with 25 s) of CCA B-mode ultrasound (US) were used and analyzed. Each US video yielded approximately 750 sequential frames spanning about 24 cardiac cycles. Firstly, US preset settings with time gain compensation with a U-pattern were employed to enhance CCA segmentations. The study showed that auto region-growing, employed three times, is appropriate for segmenting walls with a short running time (4.56 s/frame). The diameter computed for frames constructs a signal (diameter signal) with noisy parts in the shape of peak variance and an un-smooth side. Among the 12 employed smoothing methods, spline fitting with a mean peak difference per cycle (MPDCY) of 0.58 pixels was the most effective for the diameter signal. The authors propose the MPDCY as a new selection criterion for smoothing methods with highly preserved peaks. The ΔD (D_sys-D_dia) determined in this study was validated by statistical analysis as a viable replacement for manual ΔD measurement. Statistical analysis was carried out by Mann-Whitney t-test with a p-value of 0.81, regression line R² = 0.907, and there was no difference in means between the two groups for box plots. The stiffness parameters of the carotid arteries were calculated based on auto-ΔD and pulse pressure. Five ML models, including K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), logistic regression (LR), and random forest (RF), fed by distension (ΔD) and five stiffness parameters, were used to distinguish between the stiffened and un-stiffened CCA. Except for SVM, all models performed excellently in terms of specificity, sensitivity, precision, and area under the curve (AUC). In addition, the scatterplot and statistical analysis of the fed features confirm these remarkable outcomes. The scatter plot demonstrates that a linear hyperline can easily distinguish between the two classes. The statistical analysis shows that the stiffness parameters computed from the database of this work were statistically (p < 0.05) distributed into the non-stiffness and stiffness groups. The presented models are validated by applying them to additional datasets. Applying models to other datasets reveals a model performance of 100%. The proposed ML models could be applied in clinical practice to detect CS early, which is essential for preventing stroke.

Very Low Frequency Radial Modulation for Deep Penetration Contrast-Enhanced Ultrasound Imaging

Contrast-enhanced ultrasound imaging allows vascular imaging in a variety of diseases. Radial modulation imaging is a contrast agent-specific imaging approach for improving microbubble detection at high imaging frequencies (≥7.5 MHz), with imaging depth limited to a few centimeters. To provide high-sensitivity contrast-enhanced ultrasound imaging at high penetration depths, a new radial modulation imaging strategy using a very low frequency (100 kHz) ultrasound modulation wave in combination with imaging pulses ≤5 MHz is proposed. Microbubbles driven at 100 kHz were imaged in 10 successive oscillation states by manipulating the pulse repetition frequency to unlock the frame rate from the number of oscillation states. Tissue background was suppressed using frequency domain radial modulation imaging (F-RMI) and singular value decomposition-based radial modulation imaging (S-RMI). One hundred-kilohertz modulation resulted in significantly higher microbubble signal magnitude (63-88 dB) at the modulation frequency relative to that without 100-kHz modulation (51-59 dB). F-RMI produced images with high contrast-to-tissue ratios (CTRs) of 15 to 22 dB in a stationary tissue phantom, while S-RMI further improved the CTR (19-26 dB). These CTR values were significantly higher than that of amplitude modulation pulse inversion images (11.9 dB). In the presence of tissue motion (1 and 10 mm/s), S-RMI produced high-contrast images with CTR up to 18 dB; however, F-RMI resulted in minimal contrast enhancement in the presence of tissue motion. Finally, in transcranial ultrasound imaging studies through a highly attenuating ex vivo cranial bone, CTR values with S-RMI were as high as 23 dB. The proposed technique demonstrates successful modulation of microbubble response at 100 kHz for the first time. The presented S-RMI low-frequency radial modulation imaging strategy represents the first demonstration of real-time (20 frames/s), high-penetration-depth radial modulation imaging for contrast-enhanced ultrasound imaging.

High Frame Rate Volumetric Imaging of Microbubbles Using a Sparse Array and Spatial Coherence Beamforming

Volumetric ultrasound imaging of blood flow with microbubbles enables a more complete visualization of the microvasculature. Sparse arrays are ideal candidates to perform volumetric imaging at reduced manufacturing complexity and cable count. However, due to the small number of transducer elements, sparse arrays often come with high clutter levels, especially when wide beams are transmitted to increase the frame rate. In this study, we demonstrate with a prototype sparse array probe and a diverging wave transmission strategy, that a uniform transmission field can be achieved. With the implementation of a spatial coherence beamformer, the background clutter signal can be effectively suppressed, leading to a signal to background ratio improvement of 25 dB. With this approach, we demonstrate the volumetric visualization of single microbubbles in a tissue-mimicking phantom as well as vasculature mapping in a live chicken embryo chorioallantoic membrane.

Contrast-Enhanced High-Frame-Rate Ultrasound Imaging of Flow Patterns in Cardiac Chambers and Deep Vessels

Cardiac function and vascular function are closely related to the flow of blood within. The flow velocities in these larger cavities easily reach 1 m/s, and generally complex spatiotemporal flow patterns are involved, especially in a non-physiologic state. Visualization of such flow patterns using ultrasound can be greatly enhanced by administration of contrast agents. Tracking the high-velocity complex flows is challenging with current clinical echographic tools, mostly because of limitations in signal-to-noise ratio; estimation of lateral velocities; and/or frame rate of the contrast-enhanced imaging mode. This review addresses the state of the art in 2-D high-frame-rate contrast-enhanced echography of ventricular and deep-vessel flow, from both technological and clinical perspectives. It concludes that current advanced ultrasound equipment is technologically ready for use in human contrast-enhanced studies, thus potentially leading to identification of the most clinically relevant flow parameters for quantifying cardiac and vascular function.

Plane wave versus focused transmissions for contrast enhanced ultrasound imaging: the role of parameter settings and the effects of flow rate on contrast measurements

Contrast enhanced ultrasound (CEUS) and dynamic contrast enhanced ultrasound (DCE-US) can be used to provide information about the vasculature aiding diagnosis and monitoring of a number of pathologies including cancer. In the development of a CEUS imaging system, there are many choices to be made, such as whether to use plane wave (PW) or focused imaging (FI), and the values for parameters such as transmit frequency, F-number, mechanical index, and number of compounding angles (for PW imaging). CEUS image contrast may also be dependent on subject characteristics, e.g. flow speed and vessel orientation. We evaluated the effect of such choices on vessel contrast for PW and FI in vitro, using 2D ultrasound imaging. CEUS images were obtained using a Vantage^TM (Verasonics Inc.) and a pulse-inversion (PI) algorithm on a flow phantom. Contrast (C) and contrast reduction (CR) were calculated, where C was the initial ratio of signal in vessel to signal in background and CR was its reduction after 200 frames (acquired in 20 s). Two transducer orientations were used: parallel and perpendicular to the vessel direction. Similar C and CR was achievable for PW and FI by choosing optimal parameter values. PW imaging suffered from high frequency grating lobe artefacts, which may lead to degraded image quality and misinterpretation of data. Flow rate influenced the contrast based on: (1) false contrast increase due to the bubble motion between the PI positive and negative pulses (for both PW and FI), and (2) contrast reduction due to the incoherency caused by bubble motion between the compounding angles (for PW only). The effects were less pronounced for perpendicular transducer orientation compared to a parallel one. Although both effects are undesirable, it may be more straight forward to account for artefacts in FI as it only suffers from the former effect. In conclusion, if higher frame rate imaging is not required (a benefit of PW), FI appears to be a better choice of imaging mode for CEUS, providing greater image quality over PW for similar rates of contrast reduction.