Blood Velocity Estimation Research Articles

Automatic detection of aortic valve pathology using 3-chamber cine MRI

Abstract Funding Acknowledgements Type of funding sources: None. Introduction The assessment of aortic valve pathology using magnetic resonance imaging (MRI) typically relies on blood velocity estimates acquired using phase contrast (PC) MRI. However, abnormalities in blood flow through the aortic valve often manifest by the dephasing of blood signal in gated balanced steady-state free precession (bSSFP) scans (cine MRI) [1]. This suggests cine MRI may be used to identify patients with blood flow abnormalities who would benefit from additional detailed PC-MRI scans. Nevertheless, there are currently no tools to automatically identify abnormalities in blood flow through the aortic root in cine MRI which can be used to improve diagnosis and optimise clinical CMR protocols. Aim To determine whether a classification neural network (NN) can automatically detect aortic valve pathology from 3-chamber cine MRI, without the need for dedicated additional imaging or manual annotation. Data We train and test our approach on a retrospective clinical dataset from three UK hospitals, using single-slice bSSFP 3-chamber cine MRI (cardiac frames: 31 ± 15, spatial resolution: 1.17–1.56 x 1.17–1.56 x 8 mm3) from n = 577 patients. Using information from clinical assessments, each image was labelled as: aortic regurgitation (AR) (13%), aortic stenosis (AS) (11%), mixed valve disease (MVD) (9%), or showing no relevant pathology (67%). 322 randomly selected images were used for training, 81 for validation and 174 for testing. Methods We trained a 3D classification NN (DenseNet201 model [2] with 4 dense blocks which include 3D (i.e., spatiotemporal) convolutional layers; Adam optimisation and a cross-entropy loss function) to classify the time-resolved cine MR images. We used binary labels: "pathology" (corresponding to AR, AS or MVD) or "no pathology". As a pre-processing step, we automatically identified the heart in all cine MRI frames using an in-house algorithm that detected motion in the image across the cardiac cycle, and cropped the images to remove non-cardiac anatomical structures. After the classification, we used gradient-weighted class activation mapping (Grad-CAM) [3] to gain confidence that the classification was most dependent on voxels close to the aortic root. Fig 1 shows an overview of the image processing pipeline. Results Our classification model accurately predicts aortic valve pathology using 3-chamber cine MRI: Fig 2 shows the confusion matrix and the evaluation metrics of the study. The Grad-CAM heatmaps showed the aortic region contributed the most to the classification, as seen in Fig 1. Conclusion Convolutional NNs can identify aortic valve pathology automatically from 3-chamber cine MRI, without the need for manual annotations. This can be used to improve diagnosis and optimise clinical CMR protocols, namely as a triage tool to identify the patients most likely to benefit from additional PC-MRI sequences.

Angiographic velocimetry analysis using contrast dilution gradient method with a 1000 frames per second photon-counting detector.

Contrast dilution gradient (CDG) analysis is a quantitative method allowing blood velocity estimation using angiographic acquisitions. Currently, CDG is restricted to peripheral vasculature due to the suboptimal temporal resolution of current imaging systems. We investigate extension of CDG methods to the flow conditions of proximal vasculature using 1000 frames per second (fps) high-speed angiographic (HSA) imaging. We performed in-vitro HSA acquisitions using the XC-Actaeon detector and 3D-printed patient-specific phantoms. The CDG approach was used for blood velocity estimation expressed as the ratio of temporal and spatial contrast gradients. The gradients were extracted from 2D contrast intensity maps synthesized by plotting intensity profiles along the arterial centerline at each frame. In-vitro results obtained at various frame rates via temporal binning of 1000 fps data were retrospectively compared to computational fluid dynamics (CFD) velocimetry. Full-vessel velocity distributions were estimated at 1000 fps via parallel line expansion of the arterial centerline analysis. Using HSA, the CDG method displayed agreement with CFD at or above 250 fps [mean-absolute error (MAE): , ]. Relative velocity distributions correlated well with CFD at 1000fps with universal underapproximation due to effects of pulsatile contrast injection (MAE: 4.3cm/s). Using 1000fps HSA, CDG-based extraction of velocities across large arteries is possible. The method is sensitive to noise; however, image processing techniques and a contrast injection, which adequately fills the vessel assist algorithm accuracy. The CDG method provides high resolution quantitative information for rapidly transient flow patterns observed in arterial circulation.

FLUST: A fast, open source framework for ultrasound blood flow simulations

Background and objective:Ultrasound based blood velocity estimation is a continuously developing frontier, where the vast number of possible acquisition setups and velocity estimators makes it challenging to assess which combination is better suited for a given imaging application. FLUST, the Flow-Line based Ultrasound Simulation Tool, may be used to address this challenge, providing a common platform for evaluation of velocity estimation schemes on in silico data. However, the FLUST approach had some limitations in its original form, including reduced robustness for phase sensitive setups and the need for manual selection of integrity parameters. In addition, implementation of the technique and therefore also documentation of signal integrity was left to potential users of the approach.Methods: In this work, several improvements to the FLUST technique are proposed and investigated, and a robust, open source simulation framework developed. The software supports several transducer types and acquisition setups, in addition to a range of different flow phantoms. The main goal of this work is to offer a robust, computationally cheap and user-friendly framework to simulate ultrasound data from stationary blood velocity fields and thereby facilitate design and evaluation of estimation schemes, including acquisition design, velocity estimation and other post-processing steps.Results: The technical improvements proposed in this work resulted in reduced interpolation errors, reduced variability in signal power, and also automatic selection of spatial and temporal discretization parameters. Results are presented illustrating the challenges and the effectiveness of the solutions. The integrity of the improved simulation framework is validated in an extensive study, with results indicating that speckle statistics, spatial and temporal correlation and frequency content all correspond well with theoretical predictions. Finally, an illustrative example shows how FLUST may be used throughout the design and optimization process of a velocity estimator.Conclusions: The FLUST framework is available as a part of the UltraSound ToolBox (USTB), and the results in this paper demonstrate that it can be used as an efficient and reliable tool for the development and validation of ultrasound-based velocity estimation schemes.

Enhanced High-Gain Observer for Blood and Micro-robot Velocity Estimation in Magnetic Drug Targeting

Micro-robots have a promising prospect to be used in healthcare and bioengineering applications due to their capability to access hard-to-reach areas, allowing them to overcome the limitations of many conventional clinical methods. Controlling these micro-robots in blood vessels is an essential technology for several applications such as targeted drug delivery. One approach is to use a magnetic resonant navigation control strategy which allows precise manipulation of the micro-robot by adjusting the frequency and intensity of the magnetic field. The accuracy and robustness of this technique rely on the knowledge of some parameters such as blood and micro-robot velocities. In previous control designs, the blood velocity was assumed to be known and its pulse was supposed to be constant. This assumption did not reflect reality and reduced the feasibility of the control system. Considering this fact, this paper addresses the problem of estimating the velocity of the blood and micro-robot using an enhanced high-gain observer. Simulation results are carried out in MATLAB/Simulink to demonstrate the effectiveness of the proposed observer with comparisons to the standard high-gain observer.

Anatomic and Functional Imaging Using Row-Column Arrays.

Row-column (RC) arrays have the potential to yield full 3-D ultrasound imaging with a greatly reduced number of elements compared to fully populated arrays. They, however, have several challenges due to their special geometry. This review article summarizes the current literature for RC imaging and demonstrates that full anatomic and functional imaging can attain a high quality using synthetic aperture (SA) sequences and modified delay-and-sum beamforming. Resolution can approach the diffraction limit with an isotropic resolution of half a wavelength with low sidelobe levels, and the field of view can be expanded by using convex or lensed RC probes. GPU beamforming allows for three orthogonal planes to be beamformed at 30 Hz, providing near real-time imaging ideal for positioning the probe and improving the operator's workflow. Functional imaging is also attainable using transverse oscillation and dedicated SA sequence for tensor velocity imaging for revealing the full 3-D velocity vector as a function of spatial position and time for both blood velocity and tissue motion estimation. Using RC arrays with commercial contrast agents can reveal super-resolution imaging (SRI) with isotropic resolution below [Formula: see text]. RC arrays can, thus, yield full 3-D imaging at high resolution, contrast, and volumetric rates for both anatomic and functional imaging with the same number of receive channels as current commercial 1-D arrays.

REVIEW OF BLOOD VESSELS VELOCITY ESTIMATIONS

The vascular diseases are reflected by impairments in both morphology and hemodynamics. Digital medical imaging has revolutionized the diagnosis and treatment of these diseases. The extraction of the blood flow information from the daily clinical medical imaging acquisitions is one of the greatest importance for the physicians nowadays. The paper presents a review of the most noticeable methods of extracting blood velocity from medical images. X-ray angiography is the gold standard of the vascular diseases’ diagnosis, and it can be used successfully along with blood velocity estimation methods presented in this paper.

Validation of red blood cell flux and velocity estimations based on optical coherence tomography intensity fluctuations

We present a validation of red blood cell flux and speed measurements based on the passage of erythrocytes through the OCT’s focal volume. We compare the performance of the so-called RBC-passage OCT technique to co-localized and simultaneously acquired two-photon excitation fluorescence microscopy (TPEF) measurements. Using concurrent multi-modal imaging, we show that fluctuations in the OCT signal display highly similar features to TPEF time traces. Furthermore, we demonstrate an overall difference in RBC flux and speed of 2.5 ± 3.27 RBC/s and 0.12 ± 0.67 mm/s (mean ± S.D.), compared to TPEF. The analysis also revealed that the OCT RBC flux estimation is most accurate between 20 RBC/s to 60 RBC/s, and is severely underestimated at fluxes beyond 80 RBC/s. Lastly, our analysis shows that the RBC speed estimations increase in accuracy as the speed decreases, reaching a difference of 0.16 ± 0.25 mm/s within the 0–0.5 mm/s speed range.

Blood flow patterns estimation in the left ventricle with low-rate 2D and 3D dynamic contrast-enhanced ultrasound

Background and Objective: Left ventricle (LV) dysfunction always occurs at early heart-failure stages, producing variations in the LV flow patterns. Cardiac diagnostics may therefore benefit from flow-pattern analysis. Several visualization tools have been proposed that require ultrafast ultrasound acquisitions. However, ultrafast ultrasound is not standard in clinical scanners. Meanwhile techniques that can handle low frame rates are still lacking. As a result, the clinical translation of these techniques remains limited, especially for 3D acquisitions where the volume rates are intrinsically low.Methods: To overcome these limitations, we propose a novel technique for the estimation of LV blood velocity and relative-pressure fields from dynamic contrast-enhanced ultrasound (DCE-US) at low frame rates. Different from other methods, our method is based on the time-delays between time-intensity curves measured at neighbor pixels in the DCE-US loops. Using Navier-Stokes equation, we regularize the obtained velocity fields and derive relative-pressure estimates. Blood flow patterns were characterized with regard to their vorticity, relative-pressure changes (dp/dt) in the LV outflow tract, and viscous energy loss, as these reflect the ejection efficiency.Results: We evaluated the proposed method on 18 patients (9 responders and 9 non-responders) who underwent cardiac resynchronization therapy (CRT). After CRT, the responder group evidenced a significant (p<0.05) increase in vorticity and peak dp/dt, and a non-significant decrease in viscous energy loss. No significant difference was found in the non-responder group. Relative feature variation before and after CRT evidenced a significant difference (p<0.05) between responders and non-responders for vorticity and peak dp/dt. Finally, the method feasibility is also shown with 3D DCE-US.Conclusions: Using the proposed method, adequate visualization and quantification of blood flow patterns are successfully enabled based on low-rate DCE-US of the LV, facilitating the clinical adoption of the method using standard ultrasound scanners. The clinical value of the method in the context of CRT is also shown.

Adaptive transverse blood velocity estimation in medical ultrasound: A simulation study

Undoubtedly, highly valuable information about vascular anomalies is attained by the examination of the blood flow profile. The chief drawback of the conventional medical ultrasound in preparation of the blood periodogram is the measurement system shortcoming at the beam to flow angles near 90°. Recently, a method based on transverse oscillation (TO) approach, known as “Fourth-order estimation”, has been developed to directly estimate the transverse power spectral density (PSD) of the fully transverse blood flow. One of the basic requirements to accomplish acceptable PSDs by this technique is the sufficiently large observation window. In this paper, two adaptive approaches for efficient estimation of the velocity spectrum of a fully transverse flow by a limited observation window length are described. The first proposed adaptive approach is based on the minimum variance adaptive spectral estimation in combination with the well-known TO technique (TO-MV). Then, by exploiting the eigenspace separation of the observed data to eliminate the contribution of the undesired components, the second technique (TO-EIBMV) is developed. The approaches are validated using Field II simulations for pulsating flow. The proposed methods are tested and compared to the conventional TO transverse spectral estimator by metrics of relative standard deviation (RSD) and relative bias (RB). One of the main achievements is the decrement of the required data samples for spectrogram estimation, which leads to a better temporal resolution. Moreover, for the analyzed adaptive techniques, the robustness of the estimation results for the beam to flow angles of 60–90° and vessel depths ranging from 20 mm to 60 mm are investigated.

Automatic Estimation of Coronary Blood Flow Velocity Step 1 for Developing a Tool to Diagnose Patients With Micro-Vascular Angina Pectoris.

Aim: Our aim was to automatically estimate the blood velocity in coronary arteries using cine X-ray angiographic sequence. Estimating the coronary blood velocity is a key approach in investigating patients with angina pectoris and no significant coronary artery disease. Blood velocity estimation is central in assessing coronary flow reserve.Methods and Results: A multi-step automatic method for blood flow velocity estimation based on the information extracted solely from the cine X-ray coronary angiography sequence obtained by invasive selective coronary catheterization was developed. The method includes (1) an iterative process of segmenting coronary arteries modeling and removing the heart motion using a non-rigid registration, (2) measuring the area of the segmented arteries in each frame, (3) fitting the measured sequence of areas with a 7° polynomial to find start and stop time of dye propagation, and (4) estimating the blood flow velocity based on the time of the dye propagation and the length of the artery-tree. To evaluate the method, coronary angiography recordings from 21 patients with no obstructive coronary artery disease were used. In addition, coronary flow velocity was measured in the same patients using a modified transthoracic Doppler assessment of the left anterior descending artery. We found a moderate but statistically significant correlation between flow velocity assessed by trans thoracic Doppler and the proposed method applying both Spearman and Pearson tests.Conclusion: Measures of coronary flow velocity using a novel fully automatic method that utilizes the information from the X-ray coronary angiographic sequence were statistically significantly correlated to measurements obtained with transthoracic Doppler recordings.

Simultaneous Vascular Strain and Blood Vector Velocity Imaging Using High-Frequency Versus Conventional-Frequency Plane Wave Ultrasound: A Phantom Study.

Plaque strain and blood vector velocity imaging of stenosed arteries are expected to aid in diagnosis and prevention of cerebrovascular disease. Ultrafast plane wave imaging enables simultaneous strain and velocity estimation. Multiple ultrasound vendors are introducing high-frequency ultrasound probes and systems. This paper investigates whether the use of high-frequency ultrafast ultrasound is beneficial for assessing blood velocities and strain in arteries. The performance of strain and blood flow velocity estimation was compared between a high-frequency transducer (MS250, fc = 21 MHz) and a clinically utilized transducer (L12-5, fc = 9 MHz). Quantitative analysis based on straight tube phantom experiments revealed that the MS250 outperformed the L12-5 in the superficial region: low velocities near the wall were more accurately estimated and wall strains were better resolved. At greater than 2-cm echo depth, the L12-5 performed better due to the high attenuation of the MS250 probe. Qualitative comparison using a perfused patient-specific carotid bifurcation phantom confirmed these findings. Thus, in conclusion, for strain and blood velocity estimation for depths up to ~2 cm, a high-frequency probe is recommended.

A dynamic ultrasound simulation of a pulsating three-layered CCA for validation of two-dimensional wall motion and blood velocity estimation algorithms.

A dynamic ultrasound simulation model for the common carotid artery (CCA) with three arterial layers for validation of two-dimensional wall motion and blood velocity estimation algorithms is proposed in the present study. This model describes layers with not only characteristics of echo distributions conforming to clinical ones but also varying thicknesses, axial, and radial displacements with pulsatile blood pressure during a cardiac cycle. The modeling process is as follows: first, a geometrical model according with the clinical structure size of a CCA is built based on the preset layer thicknesses and the diameter of lumen. Second, a three-dimensional scatterer model is constructed by a mapping with a Hilbert space-filling curve from the one-dimensional scatterer distribution with the position and amplitude following Gamma and Gaussian distributions, respectively. The characteristics of three layers and blood are depicted by smoothly adjusting the scatterer density, the scale, and shape parameters of the Gamma distribution as well as the mean and standard deviation of the Gaussian distribution. To obtain the values of parameters of scatterer distributions, including the shape parameter, density, and intensity, for arterial layers and blood, the envelope signals simulated from different configurations of scatterer distribution are compared with those from different kinds of tissue of CCAs in vivo through a statistic analysis. Finally, the dynamic scatterer model is realized based on the blood pressure, elasticity modulus of intima-media (IM) and adventitia, varying IM thickness, axial displacement of IM as well as blood flow velocity at central axis during a cardiac cycle. Then, the corresponding radiofrequency (RF) signals, envelope signals, and B-mode images of the pulsatile CCA are generated in a dynamic scanning mode using Field II platform. The three arterial layers, blood, and surrounding tissue in simulated B-mode ultrasound images are clearly legible. The results based on a statistical analysis for the envelope signals from 30 simulations indicate that the echo characteristics of blood, intima, media, and adventitia are in accordant with clinical ones. The maximum relative errors between the preset geometrical sizes and the measured ones from the simulated images for the diameter of the lumen and the thicknesses of the intima, media, and adventitia are 0.13%, 3.89%, 1.35%, and 0.06%, respectively. For the dynamic parameters, the variation in IM thickness, the radial displacements of lumen and adventitia as well as the axial displacement of IM and blood flow velocity are measured with the mean relative errors of 68.03%, 9.27%, 2.10%, 4.93%, and 17.34%, respectively. The simulated results present static sizes and dynamical variations according with preset values; echo distributions conforming to clinical versions. Therefore, the presented simulation model could be useful as a data source to evaluate the performance of studies on measurements of ultrasound-based tissue structures and dynamic parameters for the CCA layers.

A Comparison Between Compounding Techniques Using Large Beam-Steered Plane Wave Imaging for Blood Vector Velocity Imaging in a Carotid Artery Model.

Conventional color Doppler imaging is limited, since it only provides velocity estimates along the ultrasound beam direction for a restricted field of view at a limited frame rate. High-frame-rate speckle tracking, using plane wave transmits, has shown potential for 2-D blood velocity estimation. However, due to the lack of focusing in transmit, image quality gets reduced, which hampers speckle tracking. Although ultrafast imaging facilitates improved clutter filtering, it still remains a major challenge in blood velocity estimation. Signal dropouts and poor velocity estimates are still present for high beam-to-flow angles and low blood flow velocities. In this paper, ultrafast plane wave imaging was combined with multiscale speckle tracking to assess the 2-D blood velocity vector in a common carotid artery (CCA) flow field. A multiangled plane wave imaging sequence was used to compare the performance of displacement compounding, coherent compounding, and compound speckle tracking. Zero-degree plane wave imaging was also evaluated. The performance of the methods was evaluated before and after clutter filtering for the large range of velocities (0-1.5 m/s) that are normally present in a healthy CCA during the cardiac cycle. An extensive simulation study was performed, based on a sophisticated model of the CCA, to investigate and evaluate the performance of the methods at different pulse repetition frequencies and signal-to-noise levels. In vivo data were acquired of a healthy carotid artery bifurcation to support the simulation results. In general, methods utilizing compounding after speckle tracking, i.e., displacement compounding and compound speckle tracking, were least affected by clutter filtering and provided the most robust and accurate estimates for the entire velocity range. Displacement compounding, which uses solely axial information to estimate the velocity vector, provided most accurate velocity estimates, although it required sufficiently high pulse repetition frequencies in high blood velocity phases and reliable estimates for all acquisition angles. When this latter requirement was not met, compound speckle tracking was most accurate, because it uses the possibility to discard angular velocity estimates corrupted by clutter filtering. Similar effects were observed for in vivo data obtained at the carotid artery bifurcation. Investigating a combination of these two compounding techniques is recommended for future research.

Robust angle-independent blood velocity estimation based on dual-angle plane wave imaging.

Two-dimensional blood velocity estimation has shown potential to solve the angle-dependency of conventional ultrasound flow imaging. Clutter filtering, however, remains a major challenge for large beam-to-flow angles, leading to signal drop-outs and corrupted velocity estimates. This work presents and evaluates a compounding speckle tracking (ST) algorithm to obtain robust angle-independent 2-D blood velocity estimates for all beam-to-flow angles. A dual-angle plane wave imaging setup with full parallel receive beamforming is utilized to achieve high-frame-rate speckle tracking estimates from two scan angles, which may be compounded to obtain velocity estimates of increased robustness. The acquisition also allows direct comparison with vector Doppler (VD) imaging. Absolute velocity bias and root-mean-square (RMS) error of the compounding ST estimations were investigated using simulations of a rotating flow phantom with low velocities ranging from 0 to 20 cm/s. In a challenging region where the estimates were influenced by clutter filtering, the bias and RMS error for the compounding ST estimates were 11% and 2 cm/s, a significant reduction compared with conventional single-angle ST (22% and 4 cm/s) and VD (36% and 6 cm/s). The method was also tested in vivo for vascular and neonatal cardiac imaging. In a carotid artery bifurcation, the obtained blood velocity estimates showed that the compounded ST method was less influenced by clutter filtering than conventional ST and VD methods. In the cardiac case, it was observed that ST velocity estimation is more affected by low signal-to-noise (SNR) than VD. However, with sufficient SNR the in vivo results indicated that a more robust angle-independent blood velocity estimator is obtained using compounded speckle tracking compared with conventional ST and VD methods.

Effects of reverberations and clutter filtering in pulsed Doppler using sparse sequences.

Duplex ultrasound is a modality in which an ultrasound system is used for simultaneous acquisition of both B-mode images and velocity (Doppler) data. Conventional duplex sequences interleave packets of B-mode and Doppler transmissions, producing undesirable gaps during B-mode interruptions. In recent years, several techniques have been proposed for avoiding such gaps by using sparse sequences, in which velocity spectra are generated from nonuniformly sampled Doppler data containing frequent B-mode interruptions. In this work, two negative effects are discussed that may influence velocity estimation when using nonuniformly sampled sequences. First, it is shown that long reverberation times lead to discontinuities in the signal from stationary clutter after each B-mode interruption. Second, using frequency analysis, it is shown that clutter filtering of nonuniformly sampled data may introduce artifacts in the velocity spectrum, and also lead to significant bias in mean velocity estimates. Methods are presented for quantification of these effects, and utilized to analyze three types of sparse duplex sequences for blood velocity estimation. In particular, it is argued that the use of such sequences in cardiac applications is not recommended because of long reverberation time. Additionally, it is found that the use of regression filters to filter nonuniformly sampled data may produce significant artifacts in pulsed wave Doppler spectra, but is less significant for color Doppler imaging applications. In vitro and in vivo examples are included showing the presence and magnitude of these problems in clinically relevant applications.

3P5-11 Improvement of accuracy and computational efficiency in intracardiac blood velocity estimation

3P5-11 Improvement of accuracy and computational efficiency in intracardiac blood velocity estimation

An improved Doppler model for obtaining accurate maximum blood velocities

Maximum blood velocity estimates are frequently required in diagnostic applications, including carotid stenosis evaluation, arteriovenous fistula inspection, and maternal-fetal examinations. However, the currently used methods for ultrasound measurements are inaccurate and often rely on applying heuristic thresholds to a Doppler power spectrum. A new method that uses a mathematical model to predict the correct threshold that should be used for maximum velocity measurements has recently been introduced. Although it is a valuable and deterministic tool, this method is limited to parabolic flows insonated by uniform pressure fields. In this work, a more generalized technique that overcomes such limitations is presented. The new approach, which uses an extended Doppler spectrum model, has been implemented in an experimental set-up based on a linear array probe that transmits defocused steered waves. The improved model has been validated by Field II simulations and phantom experiments on tubes with diameters between 2mm and 8mm. Using the spectral threshold suggested by the new model significantly higher accuracy estimates of the peak velocity can be achieved than are now clinically attained, including for narrow beams and non-parabolic velocity profiles. In particular, an accuracy of +1.2±2.5cm/s has been obtained in phantom measurements for velocities ranging from 20 to 80cm/s. This result represents an improvement that can significantly affect the way maximum blood velocity is investigated today.

Intraoperative Cardiac Ultrasound Examination Using Vector Flow Imaging

Conventional ultrasound (US) methods for blood velocity estimation only provide one-dimensional and angle-dependent velocity estimates; thus, the complexity of cardiac flow has been difficult to measure. To circumvent these limitations, the Transverse Oscillation (TO) vector flow method has been proposed. The vector flow method implemented on a commercial scanner provided real-time, angle-independent estimates of cardiac blood flow. Epicardiac and epiaortic, intraoperative US examinations were performed on three patients with stenosed coronary arteries scheduled for bypass surgery. Repeating cyclic beat-to-beat flow patterns were seen in the ascending aorta and pulmonary artery of each patient, but these patterns varied between patients. Early systolic retrograde flow filling the aortic sinuses was seen in the ascending aorta as well as early systolic retrograde flow in the pulmonary artery. In diastole, stable vortices in aortic sinuses of the ascending aorta created central antegrade flow. A stable vortex in the right atrium was seen during the entire heart cycle. The measurements were compared with estimates obtained intraoperatively with conventional spectral Doppler US using a transesophageal and an epiaortic approach. Mean differences in peak systole velocity of 11% and 26% were observed when TO was compared with transesophageal echocardiography and epiaortic US, respectively. In one patient, the cardiac output derived from vector velocities was compared with pulmonary artery catheter thermodilution technique and showed a difference of 16%. Vector flow provides real-time, angle-independent vector velocities of cardiac blood flow. The technique can potentially reveal new information of cardiovascular physiology and give insight into blood flow dynamics.

Blood Velocity Estimation Using Compressive Sensing

Duplex ultrasonography is a mode of medical ultrasonography that allows one to visualize, at the same time, the inner structure of the body (B-mode) and the blood flow at a particular point in the body (Doppler mode). This mode requires a strategy for alternating B-mode and flow emissions. Traditional strategies either halve the maximum measurable velocity or introduce gaps in the flow data. The objective of this article is to propose a completely original method based on compressive sensing for reconstructing the Doppler signal segment by segment. Our approach is based on randomly alternating B-mode and flow emissions. The influence of the different parameters on the reconstruction quality is studied in detail. The technique is evaluated and its feasability is validated in simulation and from experimental in vivo data. It is also compared to the only method from the literature, proposed by Jensen, that reconstructs blood velocity estimates from sparse data sets.

Comparison of Kasai Autocorrelation and Maximum Likelihood Estimators for Doppler Optical Coherence Tomography

In optical coherence tomography (OCT) and ultrasound, unbiased Doppler frequency estimators with low variance are desirable for blood velocity estimation. Hardware improvements in OCT mean that ever higher acquisition rates are possible, which should also, in principle, improve estimation performance. Paradoxically, however, the widely used Kasai autocorrelation estimator's performance worsens with increasing acquisition rate. We propose that parametric estimators based on accurate models of noise statistics can offer better performance. We derive a maximum likelihood estimator (MLE) based on a simple additive white Gaussian noise model, and show that it can outperform the Kasai autocorrelation estimator. In addition, we also derive the Cramer Rao lower bound (CRLB), and show that the variance of the MLE approaches the CRLB for moderate data lengths and noise levels. We note that the MLE performance improves with longer acquisition time, and remains constant or improves with higher acquisition rates. These qualities may make it a preferred technique as OCT imaging speed continues to improve. Finally, our work motivates the development of more general parametric estimators based on statistical models of decorrelation noise.