Abstract:Purpose
The contrasts of flowing blood in in vitro experiments using porcine blood and in vivo measurements of human jugular veins were analyzed to demonstrate that the hemorheological property was dependent on the shear rate.
Methods
Blood samples (45% hematocrit) suspended in saline or plasma were compared with examine the difference in viscoelasticity. Ultrafast plane-wave imaging at an ultrasonic center frequency of 7.5 MHz was performed on dif… Show more
“…The result of the conventional contrast method 16) was compared to that with deep learning model. The flow of the conventional contrast method is shown in Fig.…”
Section: Conventional Contrast Methodsmentioning
confidence: 99%
“…Our previous study has reported that a higher flow rate (high flow velocity) induces a higher rate of presence of nonspeckle regions in in vitro experiment using porcine blood. 16) Since the main component in blood is RBC, the detection of non-speckle regions may lead to the evaluation of blood properties such as viscoelasticity dependence, morphology, and acoustic impedance contrast between RBCs and plasma or other cells such as white blood cells and platelets. Also, in our previous study, the contrasts between vessel lumen and surrounding tissues have been analyzed.…”
Section: Introductionmentioning
confidence: 99%
“…The area ratio of non-speckle regions, defined as components beyond twice of the standard deviation of a contrast map, were detected from a clutter-filtered B-mode image. In this method, the area ratio of non-speckle regions increased in the porcine blood in the high flow velocity, and were more frequently detected in the venous blood of healthy subjects than diabetic subjects 16) in the range from several 5%-10%. However, this contrast analysis method is dependent on the analysis conditions such as degree of smoothing and threshold for outliers.…”
Section: Introductionmentioning
confidence: 99%
“…The performance of the developed learning model was evaluated in both in silico and experimental data of blood-mimicking fluid to compare among several conditions of the U-Net model without or with the adversarial learning 27) and conventional contrast thresholding method. 16) 2. Datasets…”
In our previous study, we analyzed the contrast of blood flow echo, and non-speckle regions were more frequently detected in the porcine blood with the high flow velocity. However, this contrast method is dependent on the degree of smoothing and threshold for outliers. This study developed a new U-Net model incorporating domain adaptation with both in silico and experimental data. This model segments blood flow echo into speckle and non-speckle regions. The performance of the developed U-Net model with several conditions of scatterer number density from 0.1 to 1.5 scatterers/mm3 and scatterer amplitude from 2 to 50 times against the speckle component was assessed using in silico data and experimental data with blood-mimicking fluid. The results indicated that the developed U-Net model with adversarial learning could stably detect non-speckle regions compared to the model without the adversarial learning and the contrast analysis method, in both in silico and experimental data.
“…The result of the conventional contrast method 16) was compared to that with deep learning model. The flow of the conventional contrast method is shown in Fig.…”
Section: Conventional Contrast Methodsmentioning
confidence: 99%
“…Our previous study has reported that a higher flow rate (high flow velocity) induces a higher rate of presence of nonspeckle regions in in vitro experiment using porcine blood. 16) Since the main component in blood is RBC, the detection of non-speckle regions may lead to the evaluation of blood properties such as viscoelasticity dependence, morphology, and acoustic impedance contrast between RBCs and plasma or other cells such as white blood cells and platelets. Also, in our previous study, the contrasts between vessel lumen and surrounding tissues have been analyzed.…”
Section: Introductionmentioning
confidence: 99%
“…The area ratio of non-speckle regions, defined as components beyond twice of the standard deviation of a contrast map, were detected from a clutter-filtered B-mode image. In this method, the area ratio of non-speckle regions increased in the porcine blood in the high flow velocity, and were more frequently detected in the venous blood of healthy subjects than diabetic subjects 16) in the range from several 5%-10%. However, this contrast analysis method is dependent on the analysis conditions such as degree of smoothing and threshold for outliers.…”
Section: Introductionmentioning
confidence: 99%
“…The performance of the developed learning model was evaluated in both in silico and experimental data of blood-mimicking fluid to compare among several conditions of the U-Net model without or with the adversarial learning 27) and conventional contrast thresholding method. 16) 2. Datasets…”
In our previous study, we analyzed the contrast of blood flow echo, and non-speckle regions were more frequently detected in the porcine blood with the high flow velocity. However, this contrast method is dependent on the degree of smoothing and threshold for outliers. This study developed a new U-Net model incorporating domain adaptation with both in silico and experimental data. This model segments blood flow echo into speckle and non-speckle regions. The performance of the developed U-Net model with several conditions of scatterer number density from 0.1 to 1.5 scatterers/mm3 and scatterer amplitude from 2 to 50 times against the speckle component was assessed using in silico data and experimental data with blood-mimicking fluid. The results indicated that the developed U-Net model with adversarial learning could stably detect non-speckle regions compared to the model without the adversarial learning and the contrast analysis method, in both in silico and experimental data.
“…Techniques for measuring cardiovascular function using ultrasound have been extensively studied [1][2][3] and assessment of the mechanical properties of compositions in a carotid arterial wall is useful for the diagnosis of atherosclerosis. The stiffness of a carotid arterial wall is evaluated by pulse wave velocity [4][5][6] and stiffness parameter β 7,8) with ultrasound in clinical situations.…”
Conventional methods for estimating 1D or 2D velocities were developed for the dynamic measurement of carotid walls. However, a carotid wall moves in 3D due to a heart pulsation, and the wall motion velocity in the longitudinal-axis cross-section is affected by out-of-plane displacements that cannot be measured with a 1D array probe. To estimate the out-of-plane displacement, we proposed the crossed-shape probe. The crossed-shape probe can estimate 3D velocity vector with 256 transmit-receive channels. Single or multiple focused beams were transmitted by the main array of the crossed-shape probe, and the RF signals received all the elements were used for 3D velocity vector estimation based on the multi-angle Doppler method. Numerical simulations and basic experiments showed that out-of-plane displacements in the longitudinal-axis cross section can be estimated. Furthermore, in vivo experiments on a human common carotid artery showed that arterial wall motion during a cardiac cycle can be measured.
High-frame-rate imaging with a clutter filter can clearly visualize blood flow signals and provide more efficient discrimination with tissue signals. In vitro studies using clutter-less phantom and high-frequency ultrasound suggested a possibility of evaluating the red blood cell (RBC) aggregation by analyzing the frequency dependence of the backscatter coefficient (BSC). However, in in vivo applications, clutter filtering is required to visualize echoes from the RBC. This study initially evaluated the effect of the clutter filter for ultrasonic BSC analysis for in vitro and preliminary in vivo data to characterize hemorheology. Coherently compounded plane wave imaging at a frame rate of 2 kHz was carried out in high-frame-rate imaging. Two samples of RBCs suspended by saline and autologous plasma for in vitro data were circulated in two types of flow phantoms without or with clutter signals. The singular value decomposition was applied to suppress the clutter signal in the flow phantom. The BSC was calculated using the reference phantom method, and it was parametrized by spectral slope and mid-band fit (MBF) between 4–12 MHz. The velocity distribution was estimated by the block matching method, and the shear rate was estimated by the least squares approximation of the slope near the wall. Consequently, the spectral slope of the saline sample was always around four (Rayleigh scattering), independently of the shear rate, because the RBCs did not aggregate in the solution. Conversely, the spectral slope of the plasma sample was lower than four at low shear rates but approached four by increasing the shear rate, because the aggregations were presumably dissolved by the high shear rate. Moreover, the MBF of the plasma sample decreased from −36 to −49 dB in both flow phantoms with increasing shear rates, from approximately 10 to 100 s−1. The variation in the spectral slope and MBF in the saline sample was comparable to the results of in vivo cases in healthy human jugular veins when the tissue and blood flow signals could be separated.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.