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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.

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.

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.

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.

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.