Abstract: This paper describes a new pulse sequence design and estimation approach, which can increase the maximum detectable velocity in synthetic-aperture (SA) velocity imaging. In SA, $N$ spherical or plane waves are emitted, and the sequence is repeated continuously. The $N$ emissions are combined to form a high-resolution image (HRI). Correlation of HRIs is employed to estimate velocity, and the combination of $N$ emissions lowers the effective pulse repetition frequency by $N$ . Interleaving emission sequences can increase the effective pulse repetition frequency to the actual pulse repetition frequency, thereby increasing the maximum detectable velocity by a factor of $N$ . This makes it possible to use longer sequences with better focusing properties. It can also increase the possible interrogation depth for vessels with large velocities. A new cross-correlation vector flow estimator is also presented, which can further increase the maximum detectable velocity by a factor of 3. It is based on transverse oscillation (TO), a preprocessing stage, and cross-correlation of signals beamformed orthogonal to the ultrasound propagation direction. The estimator is self-calibrating without estimating the lateral TO wavelength. This paper develops the theory behind the two methods. The performance is demonstrated in the accompanying paper for convex and phased array probes connected to the synthetic aperture real-time ultrasound system scanner for parabolic flow for both conventional and SA imaging.

Abstract: Stenosis of the superficial femoral artery (SFA) induces complex blood flow with increased velocities. Disease assessment is performed with Doppler ultrasound and digital subtraction angiography (DSA), but Doppler ultrasound is limited by angle dependency and DSA by ionizing radiation. An alternative is the vector flow imaging method based on transverse oscillation (TO), an angle-independent vector velocity technique using ultrasound. In this study, flow complexity and velocity measured with TO were compared with DSA for the assessment of stenosis in the SFA. The vector concentration, a measure of flow complexity, and the velocity ratio obtained from the stenosis and a disease-free adjacent vessel segment, were estimated with TO in 11 patients with a total of 16 stenoses of the SFA. TO data were compared with the corresponding stenosis degree percentage obtained with DSA. The correlation between the vector concentration and DSA was very strong (R=0.93; p The study indicated that flow changes in the SFA induced by stenosis can be quantified with TO, and that stenosis grading may be improved by estimation of flow complexity instead of velocity ratio. TO is a potential diagnostic tool for the assessment of atherosclerosis and peripheral arterial disease.

Abstract: A vascular vector flow mapping (VFM) method visualizes 2-D cardiac flow dynamics by estimating the radial component of flow from the Doppler velocities and wall motion velocities using the mass conservation equation. Although VFM provides 2-D flow, the algorithm is applicable only to bounded regions. Here, a modified VFM algorithm, vascular VFM, is proposed so that the velocities are estimated regardless of the flow geometry. To validate the algorithm, a phantom mimicking a carotid artery was fabricated and VFM velocities were compared with optical particle image velocimetry (PIV) data acquired in the same imaged plane. The validation results indicate that given optimal beam angle condition, VFM velocitiy is fairly accurate, where the correlation coefficient R between VFM and PIV velocities is 0.95. The standard deviation of the total VFM error, normalized by the maximum velocity, ranged from 8.1% to 16.3%, whereas the standard deviation of the measured input errors ranged from 8.9% to 12.7% for color flow mapping and from 4.5% to 5.9% for subbeam calculation. These results indicate that vascular VFM is reliable as its accuracy is comparable to that of conventional Doppler-flow images.

Abstract: Recently, deep learning entered the ultrasound (US) image reconstruction community, demonstrating unprecedented performances on image reconstruction tasks. The use of deep neural networks to reconstruct, restore or enhance US images has been challenged on its capability to preserve time-coherence, mostly because of their inherent non-linear properties. Most novel image reconstruction methods are typically only evaluated on static images, lacking any demonstration of their potential applicability to other imaging modes, such as vector flow imaging (VFI) and shear-wave elastography, which heavily rely on the time-coherence of consecutive reconstructed images. In this work, we demonstrate that our previously proposed convolutional neural network (CNN)-based image restoration approach, trained exclusively to improve the quality of static images, does not harm the time-coherence of consecutive frames in the context of VFI. Using dynamical numerical phantoms inspired by the synthetic aperture vector flow imaging (SA-VFI) challenge, we quantitatively show that the use of such an image restoration technique does not damage vector flow estimations, computed with a state-of-the-art speckle tracking algorithm, on simple phantoms with constant echogenicity, and that it even has the potential to improve such estimations in more complex scenarios.

Abstract: Flow batteries using suspension electrodes, e.g., zinc-air flow batteries (ZABs), have recently gained renewed interest as potential candidates for grid energy storage or mobile applications. The performance of ZABs depends on the local flow conditions of the suspension in the electrochemical cell, which acts as an electrode. Hence, it is crucial to measure and understand the complex flow characteristics of such solid–liquid suspensions. The investigated suspension electrode is an opaque slurry that consists of microscopic zinc particles and an aqueous potassium hydroxide electrolyte. Commonly, ultrasound Doppler velocimetry is used for flow imaging in opaque fluids. However, due to the high particle concentration in the suspension electrode, strong scattering and wavefront distortions of the ultrasound are introduced. In this paper, we show that this results in an increased measurement uncertainty for Doppler-based velocity estimation. Instead, ultrasound image velocimetry is applied to measure the 2-D and two-component flow field in the zinc-electrolyte suspension. This is possible by adapting the measurement system to the suspension with a calibration setup. The total measurement uncertainties of 4.1% and 2.5% for the axial and lateral flow components are derived from the calibration measurements. For the first time, the flow field of such a suspension could be measured in a scaled fluidic model of a ZAB. The comparison of the estimated flow rates from the velocity profiles showed good agreement to a gravimetric reference. A significant difference in the flow characteristics of a macroscopically homogeneous electrolyte and the same electrolyte loaded with 8 vol.-% zinc particles, i.e., the suspension electrode, was found. Along with the demonstration of the measurement technique for opaque, concentrated suspensions, the measurement data will be used to calibrate and validate numerical models for comparable multiphase fluids.

Abstract: While diffusion MRI (dMRI) is currently the most widely used in vivo imaging tool for studying brain connectivity, the biological validity of the tractography techniques based on dMRI is often debated. The wide presence of topographic regularity in various brain circuits provides a unique opportunity to examine and improve the reliability of tractography results. In this work, we develop a novel framework for the analysis of the topographic regularity of brain connectivity generated by modern FOD-based tractography techniques. Our method is based on the consistency between the mathematical property of smooth vector field flows and topographically regular fiber tracts. The main idea of our method is that we compute a principal vector field (PVF) for a given tractogram from the FODs by solving a Markov Random Field problem. By quantifying the consistency between each tract and the PVF, we develop a Vector Flow Deviation (VFD) measure and apply it to filter out topographically irregular tracts. In our experiments, we successfully applied our method to remove irregular fiber tracts in two fiber bundles with known connectopy: the visual pathway and the colossal motor pathway, which were reconstructed from the multi-shell diffusion imaging data of the Human Connectome Project (HCP). We also performed quantitative evaluation based on a G2SD distance proposed in previous work to quantitatively demonstrate the effectiveness of our filtering method.

Abstract: Ultrasound imaging of flow has seen a tremendous development over the last sixty years from 1-D spectral displays to color flow mapping and the latest Vector Flow Imaging (VFI). The paper gives an overview of the development from current commercial vector flow systems to the latest advances in fast 4-D volumetric visualizations. It includes a description of the radical break with the current sequential data acquisition by the introduction of synthetic aperture imaging, where the whole region of interest is insonified using either spherical or plane waves also known as ultrafast imaging. This makes it possible to track flow continuously in all directions at frame rates of thousands of images per second. The latest research translates this to full volumetric imaging by employing matrix arrays and row-column arrays for full 3-D vector velocity estimation at all spatial points visualized at very high volume rates (4-D).

Abstract: Vector flow imaging (VFI) is a novel velocity measurement technique that provides flow velocity information in both azimuth and axial dimensions. Compared to conventional color Doppler imaging, VFI provides velocity estimation that is independent of flow directions. Previous VFI techniques utilize either multiple transmit or receive beams or angles, or speckle tracking. This creates a trade-off between computational intensity and estimate quality or equipment cost. In this work, we present a vector flow velocity estimation technique based on deep neural networks using only beamsummed radio-frequency (RF) data. The deep neural network extracts features from the RF data, and performs flow velocity estimation on the features, and maps the estimates back to the spatial domain. The structure and training of the neural network model is presented. The performance of the technique is demonstrated and evaluated using simulations and flow phantom experiments.

Abstract: Carotid plaque vulnerability assessment is an important factor in guiding the decision to treat significant carotid stenosis. Ultrafast Ultrasound Imaging (UF) offers the possibility of evaluating local flow velocities over an entire 2D image, allowing access to velocity measurements in contact with the arterial wall and to measure the wall shear stress (WSS). To evaluate the feasibility of WSS measurement in a prospective series of patients with carotid stenosis. A 7.5 MHz linear probe of an Aixplorer scanner was used. UF acquisitions had 3 tilted plane waves transmits (−10; 0; 10°) and an effective frame rate of 5000Hz. We evaluated the flow velocity in 5 areas of the carotid wall: common carotid artery (1), plaque ascent (2), plaque peak (3), plaque descent (4), internal carotid artery (5) (Figure). WSS was computed with the vector field speed using the following formula, WSS=μ·δn·v with v the blood velocity, n the normal vector to the vessel wall and μ, the blood viscosity, calculated from the hematocrit value for each patient. WSS measurement method was first validated using a laminar flow phantom and known viscosity. And then, 33 patients were then prospectively evaluated, with a median carotid stenosis degree of 80% [75–85]. Significant correlation was found between in vitro measurement and the theoretical WSS values (R2=0.95; p<0.001).In patients,the maximum WSS value over the cardiac cycle follows the shape of the plaque with an increase during the ascend, reaching its maximum value of 3.57 Pa [2.47–4.45] at the peak of the plaque, and a fall after passing the peak (0.99 Pa [0.8–1.32]) lower than the WSS values in the non-stenotic areas (1.55 Pa [1.13–1.90] for the common carotid artery) (Table). Table 1 Wall's area Wall shear stress (Pa) Min Max Delta 1. Common carotid artery 0.14 [0.05–0.27] 1.55 [1.13–1.90] 0.73 [0.55–0.96] 2. Plaque's ascent 0.39 [0.24–0.59] 2.63 [1.89–3.28] 1.20 [0.89–1.79] 3. Plaque's peak 0.60 [0.32–0.89] 3.57 [2.47–4.45] 1.78 [1.44–2.46] 4. Plaque's descent 0.16 [0.13–0.22] 0.99 [0.80–1.32] 0.52 [0.34–0.73] 5. Internal carotid artery 0.17 [0.13–0.35] 1.37 [1.04–1.75] 0.72 [0.50–0.87] Results are median [25th–75th percentile]. Figure 1 UF provide reliable WSS values. High WSS was present at the peak of the plaque, whereas lowest WSS values were found at the post-stenotic zone. WSS evaluation may help to better characterize the carotid plaque vulnerability.

Abstract: Row Column (RC) Arrays can produce high-resolution 3-D volumetric images with only 2N interconnections compared to N2 for matrix probes. A 62+62 RC probe has four times larger surface area and one-eighth of the channel count when compared to the same-pitch fully populated 32x32 matrix probe. This research investigates the performance of such a prototype array for volumetric Synthetic Aperture (SA) B-mode and vector flow imaging using defocused waves. An interleaved SA sequence was implemented on the SARUS scanner using a 3 MHz, λ/2-pitch 62+62 RC piezoelectric probe. The sequence contains repeated emissions with rows and columns interleaved with B-mode emissions. The sequence contains 80 emissions in total and can provide a volume rate above 125 Hz yielding continuous data. Velocities were estimated using the Directional Transverse Oscillation Cross-Correlation method. Measurements were made on a circulating flow rig with a parabolic profile with a peak velocity of 0.25 m/s and beam-to-flow angle of 90°, and two different rotation angles (0°, 45°). Results showed a maximum bias of -17.5% and a standard deviation of 3.9%. A second setup used a tissue mimicking phantom with pulsating flow showing full volumetric flow estimated using the method. The flow was visualized in the entire rectilinear volume at once, with B-mode planes selectable in the entire region. This was attained using only 62 channels in receive making full volumetric imaging and velocity estimation implementable on current scanner hardware.

Abstract: 3-D blood vector flow imaging is of great value in understanding and detecting cardiovascular diseases. Currently, 3-D ultrasound vector flow imaging requires 2-D matrix probes, which are expensive and suffer from suboptimal image quality. Our recent study proposed an interpolation algorithm to obtain a divergence-free reconstruction of the 3-D flow field from 2-D velocities obtained by high-frame-rate ultrasound particle imaging velocimetry (High Frame Rate echo-Particle Imaging Velocimetry, also known as HFR Ultrasound Imaging Velocimetry (UIV)), using a 1-D array transducer. The aim of this work was to significantly improve the accuracy and reduce the time-to-solution of our previous approach, thereby paving the way for clinical translation. More specifically, accuracy was improved by optimising the divergence-free basis to reduce Runge phenomena near domain boundaries, and time-to-solution was reduced by demonstrating that under certain conditions, the resulting system could be solved using widely available and highly optimised generalised minimum residual algorithms. To initially illustrate the utility of the approach, coarse 2-D subsamplings of an analytical unsteady Womersely flow solution and a steady helical flow solution obtained using computational fluid dynamics were used successfully to reconstruct full flow solutions, with 0.82% and 4.8% average relative errors in the velocity field, respectively. Subsequently, multiplane 2-D velocity fields were obtained through HFR UIV for a straight-tube phantom and a carotid bifurcation phantom, from which full 3-D flow fields were reconstructed. These were then compared with flow fields obtained via computational fluid dynamics in each of the two configurations, and average relative errors of 6.01% and 12.8% in the velocity field were obtained. These results reflect 15%-75% improvements in accuracy and 53- to 874-fold acceleration of reconstruction speeds for the four cases, compared with the previous divergence-free flow reconstruction method. In conclusion, the proposed method provides an effective and fast method to reconstruct 3-D flow in arteries using a 1-D array transducer.

Abstract: Ultrasound assessment of blood velocity vectors is usually performed on vessels, like the carotid artery, placed at shallow depths, while only few studies have been presented so far on the investigation of deep vessels. Some vector methods present clear disadvantages at great depth: for example, in multi-beam vector Doppler the inter-beam angle dramatically reduces due to the limited aperture. This problem, in principle, does not affect speckle tracking methods, which could potentially operate even with small transmission apertures. The aim of this work is to investigate the behavior, at different depths, of high frame rate Vector Flow Imaging methods based on the transmission of plane waves. Simulations show that accuracies better than 10% can be obtained for depths shallower than 6 cm but, at higher depths, the performance is significantly affected by the azimuthal broadening of the pressure field.

Abstract: In this paper, we propose a new gradient vector flow model with advection enhancement, called advection-enhanced gradient vector flow, for calculating the external force employed in the active-contour image segmentation. The proposed model is mainly inspired by the functional derivative of an adaptive total variation regularizer whose minimizer is expected to be able to effectively preserve the desired object boundary. More specifically, by incorporating an additional advection term into the usual gradient vector flow model, the resulting external force can much better help the active contour to recover missing edges, to converge to a narrow and deep concavity, and to preserve weak edges. Numerical experiments are performed to demonstrate the high performance of the newly proposed model. AMS subject classifications: 68U10, 65K10

Abstract: This chapter describes the systems of flow-field generation, flow sampling, and unit movement employed by Fieldrunners 2. The mobile tower defense game Fieldrunners 2 used a combination of vector flow fields and steering behaviors to efficiently simulate thousands of agents, referred to as units. The flow field represents the optimal path direction at every cell in the grid, and is an approximation of a continuous flow function. The flow field is generated via a modified traditional point-to-point pathfinding function. The flow-field generation technique used in Fieldrunners 2 was based on Dijkstra’s algorithm for simplicity and design reasons. More advanced pathfinding algorithms, such as Theta*, can generate smoother, more organic flow fields. Flow fields can be extended to incorporate alternate motivations and concerns for units by blending static and dynamic flow fields. Despite this potential improvement, static flow fields and steering behaviors provided a robust, realistic crowd simulation for Fieldrunners 2.

Abstract: To accurately investigate the state of the carotid artery by the local haemodynamics and motion of the plaque using ultrasound, high-frame rate volumetric imaging is necessary. We have specifically designed a matrix array for this purpose. In this proceeding we will focus on imaging a volumetric flow profile using this matrix. For this purpose, we extend a fast frequency domain vector flow imaging method to 3D and perform measurements on a flow phantom. The results indicate that it is feasible to estimate 3D velocity vectors on a 3D grid using our matrix transducer and the proposed algorithm.

Abstract: 3-D vector flow imaging is simulated with a 1024 element matrix array and vessels aligned with the x- and y- axes. Parabolic flow profiles with a peak velocity of 0.5 m/s are simulated with an interleaved synthetic aperture sequence with 5 emissions at a pulse repetition frequency of 15 kHz. Two-peak receive apodizations are used to induce transverse oscillations (TO) in the x- and y-directions, and a directional TO velocity estimator based on cross-correlation is used to estimate the 3 velocity components. The relative mean bias and relative mean standard deviation are calculated for each velocity component and the velocity magnitude. For the vessel aligned with the x-axis, the bias of the x-component is -10.1%, and for the vessel aligned with the y-axis, the bias of the y-component is -2.04%. Relative mean standard deviations of the x- and y-components are in the range 10% to 17% and below 3.2% for the z-component.

Abstract: In this paper, a flow estimation method based on transverse oscillation is applied, in high frame rate conditions, to the data collected using a convex array. All simulations were based on the transmission of the "natural" diverging wave due to the convex geometry of the probe when all elements are simultaneously excited. Transverse oscillation was introduced in the tangential direction in post-acquisition in the Fourier domain. Finally, 2D velocity vectors were extracted thanks to a phase-based estimator. Velocities were estimated with a bias of 8% of the peak velocity and a standard deviation lower than 7 %. The method is now ready for experimental tests on flow phantoms and in vivo studies.

Abstract: The invention provides an X-band radar ocean current inversion method based on cross-spectrum analysis, which comprises the steps of firstly respectively performing cross-spectrum analysis on two adjacent images in an X-band radar image to obtain a coherent coefficient spectrum and a phase spectrum; then respectively averaging the coherent coefficient spectrum and the phase spectrum, determining the number of dominant waves according to the peak value of the coherent coefficient spectrum, and eliminating the direction ambiguity of the dominant wave direction by using the average phase; selecting different wave directions, establishing a mode according to the phase velocity obtained from a frequency dispersion relation, and solving the model according to a least squares method to obtain anocean current vector. The X-band radar ocean current inversion method can solve the problems of low accuracy of the X-band radar in observing sea surface flow field and inability of obtaining the vector flow field by using a set of coherent X-band radar in the prior art, thereby meeting the requirements of business-oriented observation for the coastal marine environment and services for marine production and life and the like.

Abstract: Embodiments of the present invention provide a blood flow mapping processing method, comprising: determining blood flow mapping parameters, wherein the blood flow mapping parameters comprise a mapping sound velocity, a center frequency of a probe for transmitting signals, and a mapping depth; obtaining a velocity measuring range for the blood flow; and determining the first target quantity of different transmit angles according to the mapping sound velocity, the center frequency of the probe for transmitting signals, the mapping depth, and the velocity measuring range for the blood flow. The embodiments of the present invention also provide an ultrasonic imaging device. According to the present invention, the first target quantity of different transmit angles can be generated on the basis of blood flow mapping parameters and a velocity measuring range for the blood flow, so that a user can enter a vector flow mapping mode according to the instruction of the first target quantity of the different transmit angles, so as to obtain a blood flow image with high precision and less aliasing.

Abstract: Aiming at the problem of scarcity in traditional tracking algorithms in order to track behavior more rapidly and accurately, a behavior tracking algorithm based on linear discriminant analysis combined with the gradient vector flow is presented. First, the gradient vector flow is calculated in the reference coordinate system, the appropriate curve parameters are set, and the position of the behavior is determined. Then, the discreteness is calculated within the range of behavior, the discriminant function is defined, and the maximum value and the best eigenvector are obtained. Last, the behavior is detected when the value of the dispersion degree is less than the set threshold. The new algorithm is not affected by background changes. Compared with existing algorithms, the simulation results show that the method is easy to realize and has a more stable tracking effect.Aiming at the problem of scarcity in traditional tracking algorithms in order to track behavior more rapidly and accurately, a behavior tracking algorithm based on linear discriminant analysis combined with the gradient vector flow is presented. First, the gradient vector flow is calculated in the reference coordinate system, the appropriate curve parameters are set, and the position of the behavior is determined. Then, the discreteness is calculated within the range of behavior, the discriminant function is defined, and the maximum value and the best eigenvector are obtained. Last, the behavior is detected when the value of the dispersion degree is less than the set threshold. The new algorithm is not affected by background changes. Compared with existing algorithms, the simulation results show that the method is easy to realize and has a more stable tracking effect.

Abstract: Image retargeting is devoted to preserve the visual content of images with a proper resizing, removing vertical and/or horizontal paths of pixels which contain low semantic information. In this paper, a method based on the Generalised Gradient Vector Flow (GGVF) is presented. The GGVF formulation allows the balancing of the smoothing term and data term of the flow by proper parameter tuning. The proposed approach has been tested by considering a data set of 1000 images and varying the percentage of resizing from 10% to 50% and for different values of the aim involved parameter K. Results show that our algorithm better preserves the important information compared to GVF and Seam Carving approaches. Preliminary results show an underlying relation between parameter K and the percentage of resizing has been also exploited.

Abstract: A number of popular methods for segmentation are based on a generalized gradient vector flow (GGVF) snakes. One of the most successful recent extensions of this idea is the adaptive diffusion flow (ADF) snake. However, the good choice of the control parameters of the ADF such as the Gaussian smoothing coefficients and the weights associated with harmonic hypersurface functional and the infinity Laplacian are hard to select. In turn, the wrong choice often leads to inappropriate results. In this paper, we propose a new method to select the control parameters of ADF based on the vector field analysis (VFA). The experimental results obtained on a set of 40 US images of breast tumor show that the ADF combined with the VFA outperforms the original ADF.

Abstract: Synthetic Aperture Vector Flow Imaging (SA-VFI) can visualize complex cardiac and vascular blood flow patterns at high temporal resolution with a large field of view. Convolutional neural networks (CNNs) are commonly used in image and video recognition and classification. However, most recently presented CNNs also allow for making per-pixel predictions as needed in optical flow velocimetry. To our knowledge we demonstrate here for the first time a CNN architecture to produce 2D full flow field predictions from high frame rate SA ultrasound images using supervised learning. The CNN was initially trained using CFD-generated and augmented noiseless SA ultrasound data of a realistic vessel geometry. Subsequently, a mix of noisy simulated and real \textit{in vivo} acquisitions were added to increase the network's robustness. The resulting flow field of the CNN resembled the ground truth accurately with an endpoint-error percentage between 6.5\% to 14.5\%. Furthermore, when confronted with an unknown geometry of an arterial bifurcation, the CNN was able to predict an accurate flow field indicating its ability for generalization. Remarkably, the CNN also performed well for rotational flows, which usually requires advanced, computationally intensive VFI methods. We have demonstrated that convolutional neural networks can be used to estimate complex multidirectional flow.

Abstract: In the field of bedside cardiac diagnostic imaging, Doppler Ultrasound (DU) is the gold standard for diagnosing heart conditions. The largest benefit of DU is its ability to noninvasively image cardiac flow and allow the estimation of blood velocity and quantification of anatomical disease. However, to get correct velocity estimation, the position of the transducer in relation to the flow field needs to be known. This is the problem of angle/direction dependency and limits DUs accuracy when imaging in areas where perfect alignment or exact position of the transducer in relation to flow field is not possible or known, such as in the left ventricle. As a solution to the problem of angle dependency, Vector Flow Imaging (VFI) is used because it is non-invasive and angle-independent. In this study, VFI was used in 12 pediatric patients from Arkansas Children’s Hospital to analyze left ventricular flow using the 4-chamber view. The shape, in the form of ellipse Major:Minor axis ratio, of ventricular vortices was then measured. The deviation of an individual patients heart flow from what is theoretically healthy as defined in literature, an ellipse with Major:Minor axis ratio of 1.9, was compared to what was measured with VFI. The average directional deviation for these 12 patients was 64.85o ± 10.34o from what is theoretically healthy. After optimizing ellipse parameters to actual patient flow, the true average optimal ratio was found to be 1.98 ± 0.58. Additionally, it was found that heart rate (p < 0.0001), age (p = 0.003), and weight (p < 0.0001) had a significant effect on angle deviation. However, there was no trend in the data. This preliminary study paves the way for using VFI to define healthy parameters for left ventricular flow and assist clinicians with more accurate diagnoses in anatomical areas with complex flow. Table of

Abstract: This study is an initial evaluation of a validation platform for computational fluid dynamics (CFD) pipelines made for human intra-cardiac flow estimation. The pipelines use image-based prescribed geometry CFD from computed tomography angiography (CTA). In this study the CTA provides approximately 20 volumetric images within one cardiac cycle. The validation platform consists of a dynamic heart phantom which mimics the human heart in CTA and ultrasound (US) measurements. The flow inside the phantom right ventricle (RV) was measured using two methods: 1) a novel CFD pipeline applied using the CTA data (3D+time). 2) US vector flow imaging (VFI) measured directly on the phantom (2D+time). The CFD and VFI are compared quantitatively by comparing point evaluations (line averages) of the in-plane fluid velocity magnitude. The similarity of the line averages, assessed from plots, is found to be depending on the spatial position of the lines. Some positions are very similar in CFD and VFI and some are not. Furthermore a qualitatively comparison is made by plotting the corresponding 2D slices of the vector fields which confirms the quantitative assessment: the overall flow patterns are similar but not everywhere.