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Echocardiography has been a prominent tool for the diagnosis of cardiac disease. However, these diagnoses can be heavily impeded by poor image quality. Acoustic clutter emerges due to multipath reflections imposed by layers of skin, subcutaneous fat, and intercostal muscle between the transducer and heart. As a result, haze and other noise artifacts pose a real challenge to cardiac ultrasound imaging. In many cases, especially with difficult-to-image patients such as patients with obesity, a diagnosis from B-Mode ultrasound imaging is effectively rendered unusable, forcing sonographers to resort to contrast-enhanced ultrasound examinations or refer patients to other imaging modalities. Tissue harmonic imaging has been a popular approach to combat haze, but in severe cases is still heavily impacted by haze. Alternatively, denoising algorithms are typically unable to remove highly structured and correlated noise, such as haze. It remains a challenge to accurately describe the statistical properties of structured haze, and develop an inference method to subsequently remove it. Diffusion models have emerged as powerful generative models and have shown their effectiveness in a variety of inverse problems. In this work, we present a joint posterior sampling framework that combines two separate diffusion models to model the distribution of both clean ultrasound and haze in an unsupervised manner. Furthermore, we demonstrate techniques for effectively training diffusion models on radio-frequency ultrasound data and highlight the advantages over image data. Experiments on both in-vitro and in-vivo cardiac datasets show that the proposed dehazing method effectively removes haze while preserving signals from weakly reflected tissue.

A change in elastin and collagen content is indicative of damage caused by hypertension, which changes the non-linear behavior of the vessel wall. This study was aimed at investigating the feasibility of monitoring the non-linear material behavior in an angiotensin II hypertensive mice model. Aortas from 13 hypertensive mice were imaged with pulse wave imaging (PWI) over 4 wk using a 40-MHz linear array. The pulse wave velocity was estimated using two wave features: (i) the maximum axial acceleration of the foot (PWV dia ) and (ii) the maximum axial acceleration of the dicrotic notch (PWV end-sys ). The Bramwell-Hill equation was used to derive the compliance at diastolic and end-systolic pressure. This study determined the potential of PWI in a hypertensive mouse model to image and quantify the non-linear material behavior in vivo. End-systolic compliance could differentiate between the sham and angiotensin II groups, whereas diastolic compliance could not, indicating that PWI can detect early collagen-dominated remodeling., Competing Interests: Conflict of interest disclosure The authors have no relationships to disclose., (Copyright © 2021 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)

Pulse wave imaging (PWI) is an ultrasound imaging modality that estimates the wall stiffness of an imaged arterial segment by tracking the pulse wave propagation. The aim of the present study is to integrate PWI with vector flow imaging, enabling simultaneous and co-localized mapping of vessel wall mechanical properties and 2-D flow patterns. Two vector flow imaging techniques were implemented using the PWI acquisition sequence: 1) multiangle vector Doppler and 2) a cross-correlation-based vector flow imaging (CC VFI) method. The two vector flow imaging techniques were evaluated in vitro using a vessel phantom with an embedded plaque, along with spatially registered fluid structure interaction (FSI) simulations with the same geometry and inlet flow as the phantom setup. The flow magnitude and vector direction obtained through simulations and phantom experiments were compared in a prestenotic and stenotic segment of the phantom and at five different time frames. In most comparisons, CC VFI provided significantly lower bias or precision than the vector Doppler method ( ) indicating better performance. In addition, the proposed technique was applied to the carotid arteries of nonatherosclerotic subjects of different ages to investigate the relationship between PWI-derived compliance of the arterial wall and flow velocity in vivo. Spearman's rank-order test revealed positive correlation between compliance and peak flow velocity magnitude ( r s = 0.90 and ), while significantly lower compliance ( ) and lower peak flow velocity magnitude ( ) were determined in older (54-73 y.o.) compared with young (24-32 y.o.) subjects. Finally, initial feasibility was shown in an atherosclerotic common carotid artery in vivo. The proposed imaging modality successfully provided information on blood flow patterns and arterial wall stiffness and is expected to provide additional insight in studying carotid artery biomechanics, as well as aid in carotid artery disease diagnosis and monitoring.

Pulse wave imaging (PWI) is a non-invasive, ultrasound-based technique, which provides information on arterial wall stiffness by estimating the pulse wave velocity (PWV) along an imaged arterial wall segment. The aims of the present study were to: (1) utilize the PWI information to automatically and optimally divide the artery into the segments with most homogeneous properties and (2) assess the feasibility of this method to provide arterial wall mechanical characterization in normal and atherosclerotic carotid arteries in vivo. A silicone phantom consisting of a soft and stiff segment along its longitudinal axis was scanned at the stiffness transition, and the PWV in each segment was estimated through static testing. The proposed algorithm detected the stiffness interface with an average error of 0.98  ±  0.49 mm and 1.04  ±  0.27 mm in the soft-to-stiff and stiff-to-soft pulse wave transmission direction, respectively. Mean PWVs estimated in the case of the soft-to-stiff pulse wave transmission direction were 2.47 [Formula: see text] 0.04 m s -1 and 3.43 [Formula: see text] 0.08 m s -1 for the soft and stiff phantom segments, respectively, while in the case of stiff-to-soft transmission direction PWVs were 2.60 [Formula: see text] 0.18 m s -1 and 3.72 [Formula: see text] 0.08 m s -1 for the soft and stiff phantom segments, respectively, which were in good agreement with the PWVs obtained through static testing (soft segment: 2.41 m s -1 , stiff segment: 3.52 m s -1 ). Furthermore, the carotid arteries of N  =  9 young subjects (22-32 y.o.) and N  =  9 elderly subjects (60-73 y.o.) with no prior history of carotid artery disease were scanned, in vivo, as well as the atherosclerotic carotid arteries of N  =  12 (59-85 y.o.) carotid artery disease patients. One-way ANOVA with Holm-Sidak correction showed that the number of most homogeneous segments in which the artery was divided was significantly higher in the case of carotid artery disease patients compared to young (3.25 [Formula: see text] 0.86 segments versus 1.00 [Formula: see text] 0.00 segments, p -value  <  0.0001) and elderly non-atherosclerotic subjects (3.25 [Formula: see text] 0.86 segments versus 1.44 [Formula: see text] 0.51 segments p -value  <  0.0001), indicating increased wall inhomogeneity in atherosclerotic arteries. The compliance provided by the proposed algorithm was significantly higher in non-calcified/high-lipid plaques as compared with calcified plaques (3.35 [Formula: see text] 2.45 *[Formula: see text] versus 0.22 [Formula: see text] 0.18 * [Formula: see text], p -value  <  0.01) and the compliance estimated in elderly subjects (3.35 [Formula: see text] 2.45 * [Formula: see text] versus 0.79 [Formula: see text] 0.30 * [Formula: see text], p -value  <  0.01). Moreover, lower compliance was estimated in cases where vulnerable plaque characteristics were present (i.e. necrotic lipid core, thrombus), compared to stable plaque components (calcification), as evaluated through plaque histological examination. The proposed algorithm was thus capable of evaluating arterial wall inhomogeneity and characterize wall mechanical properties, showing promise in vascular disease diagnosis and monitoring.

Imaging arterial mechanical properties may improve vascular disease diagnosis. Pulse wave velocity (PWV) is a marker of arterial stiffness linked to cardio-vascular mortality. Pulse wave imaging (PWI) is a technique for imaging the pulse wave propagation at high spatial and temporal resolution. In this paper, we introduce adaptive PWI, a technique for the automated partition of heterogeneous arteries into individual segments characterized by most homogeneous pulse wave propagation, allowing for more robust PWV estimation. This technique was validated in a silicone phantom with a soft-stiff interface. The mean detection error of the interface was 4.67 ± 0.73 mm and 3.64 ± 0.14 mm in the stiff-to-soft and soft-to-stiff pulse wave transmission direction, respectively. This technique was tested in monitoring the progression of atherosclerosis in mouse aortas in vivo ( n = 11 ). The PWV was found to already increase at the early stage of 10 weeks of high-fat diet (3.17 ± 0.67 m/sec compared to baseline 2.55 ± 0.47 m/sec, ) and further increase after 20 weeks of high-fat diet (3.76±1.20 m/sec). The number of detected segments of the imaged aortas monotonically increased with the duration of high-fat diet indicating an increase in arterial wall property inhomogeneity. The performance of adaptive PWI was also tested in aneurysmal mouse aortas in vivo. Aneurysmal boundaries were detected with a mean error of 0.68±0.44 mm. Finally, initial feasibility was shown in the carotid arteries of healthy and atherosclerotic human subjects in vivo ( n = 3 each). Consequently, adaptive PWI was successful in detecting stiffness inhomogeneity at its early onset and monitoring atherosclerosis progression in vivo.

Methods used in clinical practice to diagnose and monitor atherosclerosis present limitations. Imaging the mechanical properties of the arterial wall has demonstrated the potential evaluate plaque vulnerability and assess the risk for stroke. Adaptive Pulse Wave Imaging (PWI) is a non-invasive ultrasound imaging technique, which automatically detects points of spatial mechanical inhomogeneity along the imaged artery and provides piecewise stiffness characterization. The aims of the present study are to: 1) demonstrate the initial feasibility of adaptive PWI to image the mechanical properties of an atherosclerotic plaque 2) demonstrate the feasibility to combine adaptive PWI with vector Doppler in a single imaging modality in order to simultaneously obtain information plaque mechanical properties and plaque hemodynamics. The common carotid arteries of 1 healthy subject and 2 carotid artery disease patients were scanned in vivo. One of the patients underwent carotid endarterectomy and a plaque sample was retrieved. In this patient, a higher compliance value of the stenotic segment was estimated by Adaptive PWI as compared with the adjacent arterial wall, and the healthy carotid artery. This was corroborated by histological staining of the plaque sample, which revealed the presence of a large necrotic core and a thrombus, characteristics associated with reduced stiffness. Moreover, the same sequence demonstrated the feasibility to obtain both stiffness maps and vector flow information, showing promise in atherosclerosis diagnosis and patient care.

Carotid stenosis involves narrowing of the lumen in the carotid artery potentially leading to a stroke, which is the third leading cause of death in the United States. Several recent investigations have found that plaque structure and composition may represent a more direct biomarker of plaque rupture risk compared with the degree of stenosis. In this study, pulse wave imaging was applied in 111 (n = 11, N = 13 plaques) patients diagnosed with moderate (>50%) to severe (>80%) carotid artery stenosis to investigate the feasibility of characterizing plaque properties based on the pulse wave-induced arterial wall dynamics captured by pulse wave imaging. Five (n = 5 patients, N = 20 measurements) healthy volunteers were also imaged as a control group. Both conventional and high-frame-rate plane wave radiofrequency imaging sequences were used to generate piecewise maps of the pulse wave velocity (PWV) at a single depth along stenotic carotid segments, as well as intra-plaque PWV mapping at multiple depths. Intra-plaque cumulative displacement and strain maps were also calculated for each plaque region. The Bramwell-Hill equation was used to estimate the compliance of the plaque regions based on the PWV and diameter. Qualitatively, wave convergence, elevated PWV and decreased cumulative displacement around and/or within regions of atherosclerotic plaque were observed and may serve as biomarkers for plaque characterization. Intra-plaque mapping revealed the potential to capture wave reflections between calcified inclusions and differentiate stable (i.e., calcified) from vulnerable (i.e., lipid) plaque components based on the intra-plaque PWV and cumulative strain. Quantitatively, one-way analysis of variance indicated that the pulse wave-induced cumulative strain was significantly lower (p < 0.01) in the moderately and severely calcified plaques compared with the normal controls. As expected, compliance was also significantly lower in the severely calcified plaques regions compared with the normal controls (p < 0.01). The results from this pilot study indicated the potential of pulse wave imaging coupled with strain imaging to differentiate plaques of varying stiffness, location and composition. Such findings may serve as valuable information to compensate for the limitations of currently used methods for the assessment of stroke risk., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Pulse wave imaging (PWI) is a noninvasive technique for tracking the propagation of the pulse wave along the arterial wall. The 3-D ultrasound imaging would aid in objectively estimating the pulse wave velocity (PWV) vector. This paper aims to introduce a novel PWV estimation method along the propagation direction, validate it in phantoms, and test its feasibility in vivo. A silicone vessel phantom consisting of a stiff and a soft segment along the longitudinal axis and a silicone vessel with a plaque were constructed. A 2-D array with a center frequency of 2.5 MHz was used. Propagation was successfully visualized in 3-D in each phantom and in vivo in six healthy subjects. In three of the healthy subjects, results were compared against conventional PWI using a linear array. PWVs were estimated in the stiff (3.42 ± 0.23 m [Formula: see text]) and soft (2.41 ± 0.07 m [Formula: see text]) phantom segments. Good agreement was found with the corresponding static testing values (stiff: 3.41 m [Formula: see text] and soft: 2.48 m [Formula: see text]). PWI-derived vessel compliance values were validated with dynamic testing. Comprehensive views of pulse propagation in the plaque phantom were generated and compared against conventional PWI acquisitions. Good agreement was found in vivo between the results of 4-D PWI (4.80 ± 1.32 m [Formula: see text]) and conventional PWI (4.28±1.20 m [Formula: see text]) ( n=3 ). PWVs derived for all of the healthy subjects ( n = 6 ) were within the physiological range. Thus, the 4-D PWI was successfully validated in phantoms and used to image the pulse wave propagation in normal human subjects in vivo.

Pulse wave velocity (PWV) is a surrogate marker of arterial stiffness linked to cardiovascular morbidity. Pulse wave imaging (PWI) is a technique developed by our group for imaging the pulse wave propagation in vivo. PWI requires high temporal and spatial resolution, which conventional ultrasonic imaging is unable to simultaneously provide. Coherent compounding is known to address this tradeoff and provides full aperture images at high frame rates. This study aims to implement PWI using coherent compounding within a GPU-accelerated framework. The results of the implemented method were validated using a silicone phantom against static mechanical testing. Reproducibility of the measured PWVs was assessed in the right common carotid of six healthy subjects (n  =  6) approximately 10-15 mm before the bifurcation during two cardiac cycles over the course of 1-3 d. Good agreement of the measured PWVs (3.97  ±  1.21 m s -1 , 4.08  ±  1.15 m s -1 , p  =  0.74) was obtained. The effects of frame rate, transmission angle and number of compounded plane waves on PWI performance were investigated in the six healthy volunteers. Performance metrics such as the reproducibility of the PWVs, the coefficient of determination (r 2 ), the SNR of the PWI axial wall velocities ([Formula: see text]) and the percentage of lateral positions where the pulse wave appears to arrive at the same time-point, indicating inadequacy of the temporal resolution (i.e. temporal resolution misses) were used to evaluate the effect of each parameter. Compounding plane waves transmitted at 1° increments with a linear array yielded optimal performance, generating significantly higher r 2 and [Formula: see text] values (p  ⩽  0.05). Higher frame rates (⩾1667 Hz) produced improvements with significant gains in the r 2 coefficient (p  ⩽  0.05) and significant increase in both r 2 and [Formula: see text] from single plane wave imaging to 3-plane wave compounding (p  ⩽  0.05). Optimal performance was established at 2778 Hz with 3 plane waves and at 1667 Hz with 5 plane waves.

Atherosclerosis and Abdominal Aortic Aneurysms (AAAs) are two common vascular diseases associated with mechanical changes in the arterial wall. Pulse Wave Imaging (PWI), a technique developed by our group to assess and quantify the mechanical properties of the aortic wall in vivo, may provide valuable diagnostic information. This work implements piecewise PWI (pPWI), an enhanced version of PWI designed for focal vascular diseases. Localized, sub-regional PWVs and PWI moduli ( EPWI ) were estimated within 2-4 mm wall segments of murine normal, atherosclerotic and aneurysmal arteries. Overall, stiffness was found to increase in the atherosclerotic cases. The mean sub-regional PWV was found to be 2.57±0.18 m/s for the normal aortas (n = 7) with a corresponding mean EPWI of 43.82±5.86 kPa. A significant increase ( (p ≤ 0.001)) in the group means of the sub-regional PWVs was found between the normal aortas and the aortas of mice on high-fat diet for 20 ( 3.30±0.36 m/s) and 30 weeks ( 3.56±0.29 m/s). The mean of the sub-regional PWVs ( 1.57±0.78 m/s) and EPWI values ( 19.23±15.47 kPa) decreased significantly in the aneurysmal aortas (p ≤ 0.05) . Furthermore, the mean coefficient of determination (r(2)) of the normal aortas was significantly higher (p ≤ 0.05) than those of the aneurysmal and atherosclerotic cases. These findings demonstrated that pPWI may be able to provide useful biomarkers for monitoring focal vascular diseases.

Harmonic motion imaging for focused ultrasound (HMIFU) is a recently developed high-intensity focused ultrasound (HIFU) treatment monitoring method that utilizes an amplitude-modulated therapeutic ultrasound beam to induce an oscillatory radiation force at the HIFU focus and estimates the focal tissue displacement to monitor the HIFU thermal treatment. In this study, the performance of HMIFU under acoustic, thermal, and mechanical effects was investigated. The performance of HMIFU was assessed in ex vivo canine liver specimens (n = 13) under slow denaturation or boiling regimes. A passive cavitation detector (PCD) was used to assess the acoustic cavitation activity, and a bare-wire thermocouple was used to monitor the focal temperature change. During lesioning with slow denaturation, high quality displacements (correlation coefficient above 0.97) were observed under minimum cavitation noise, indicating the tissue initial-softening-then- stiffening property change. During HIFU with boiling, HMIFU monitored a consistent change in lesion-to-background displacement contrast (0.46 ± 0.37) despite the presence of strong cavitation noise due to boiling during lesion formation. Therefore, HMIFU effectively monitored softening-then-stiffening during lesioning under slow denaturation, and detected lesioning under boiling with a distinct change in displacement contrast under boiling in the presence of cavitation. In conclusion, HMIFU was shown under both boiling and slow denaturation regimes to be effective in HIFU monitoring and lesioning identification without being significantly affected by cavitation noise.