APPARATUS FOR ULTRASOUND FLOW VECTOR IMAGING AND METHODS THEREOF

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Abstract
Apparatus and methods of use are provided for complex flow imaging and analysis that is noninvasive, accurate, and timeresolved. It is particularly useful in imaging of vascular flow with spatiotemporal fluctuations. This apparatus is an ultrasoundbased framework called vector projectile imaging (VPI) that can dynamically render complex flow patterns over an imaging view at millisecond time resolution. The VPI apparatus and methods comprise: (i) highframerate broadview data acquisition (based on steered plane wave firings); (ii) flow vector estimation derived from multiangle Doppler analysis (coupled with data regularization and leastsquares fitting); and (iii) dynamic visualization of colorencoded vector projectiles (with flow speckles displayed as adjunct).
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23 Claims
 110. 10. (canceled)
 11. An ultrasound imaging system comprising:
a data acquisition unit, which transmits, via an ultrasonic array transducer, a series of plane waves into a tissue and receives waves reflected from the tissue to obtain beamformed data frames; a flow vector estimation unit which estimates flow vectors at pixels of the data frames using data of the data frames; and a display rendering circuit which displays, on a display, the flow vectors estimated as color coded moving projectiles having varying lengths or colors themselves across the moving projectiles based on velocity magnitudes of one or more flow vectors of the flow vectors corresponding to the moving projectiles, wherein positions of the projectiles are dynamically updated between data frames based on the flow vectors corresponding to the projectiles and one or more interframe periods between the data frames to depict movement of the projectiles.  View Dependent Claims (12, 13, 14, 15, 16, 17, 18)
 19. An ultrasound imaging system comprising:
a data acquisition unit, which transmits, via an ultrasonic array transducer, a series of unfocused waves into a tissue and receives waves reflected from the tissue to obtain beamformed data frames; a flow vector estimation unit which estimates flow vectors at pixels of the data frames using data of the data frames; and a display rendering circuit which displays, on a display, the flow vectors estimated as moving projectiles having varying lengths or colors themselves across the moving projectiles based on velocity magnitudes of one or more flow vectors of the flow vectors corresponding to the moving projectiles, wherein positions of the projectiles are dynamically updated between data frames based on the flow vectors corresponding to the projectiles and one or more interframe periods between the data frames to depict movement of the projectiles.  View Dependent Claims (20, 21, 22)
 23. An ultrasound imaging system comprising:
a data acquisition unit, which transmits, via an ultrasonic array transducer, a series of plane waves into a tissue and receives waves reflected from the tissue to obtain beamformed data frames; a flow vector estimation unit which estimates flow vectors at pixels of the data frames using data of the data frames; and a display rendering circuit which displays, on a display, the flow vectors estimated as moving projectiles having varying lengths or colors themselves across the moving projectiles based on velocity magnitudes of one or more flow vectors of the flow vectors corresponding to the moving projectiles, wherein positions of the projectiles are dynamically updated between data frames based on the flow vectors corresponding to the projectiles and one or more interframe periods between the data frames to depict movement of the projectiles.
1 Specification
The present application claims priority to U.S. Provisional Application No. 61/905,974 filed on Nov. 19, 2013, which is incorporated herein in its entirety.
The present invention is generally directed to an ultrasonic imaging system, and more particularly to an ultrasonic system for imaging vascular flow. This imaging system can be used for noninvasively visualizing the flow of blood in a human blood vessel.
Noninvasive visualization of flow dynamics in human arteries is widely considered to be of high diagnostic importance as it may foster clinical detection of abnormal vascular conditions (Steinman D A, Taylor Calif. Flow imaging and computing: large artery hemodynamics. Ann. Biomed. Eng., 2005; 33: 17041709). For instance, monitoring flow patterns in the carotid arteries has long been implicated in useful in stroke prognosis (Donnan G A, Fisher M, Macleod M, Davis S M. Stroke. Lancet, 2008; 371: 16121623; Shields R C. Medical management of carotid stenosis. Perspect. Vasc. Surg. Endovasc. Ther., 2010; 22: 1827). Over the years, a few noninvasive flow imaging modalities have been developed (Owen A R, Roditi G H. Peripheral arterial disease: the evolving role of noninvasive imaging. Postgrad. Med. J., 2011; 87: 189198; Wolbarst A B, Hendee W R. Evolving and experimental technologies in medical imaging. Radiology, 2006; 238: 1639), and among them, ultrasound has perhaps established itself as a unique bedside modality that can be readily applied to pointofcare diagnoses (Bierig S M, Jones A. Accuracy and cost comparison of ultrasound versus alternative imaging modalities, including CT, MR, PET, and angiography. J. Diagnost. Med. Sonography, 2009; 25: 138144; Moore C L, Copel J A. Pointofcare ultrasonography. New Eng. J. Med., 2011; 364: 749757). In most existing ultrasound scanners, flow information can be rendered in realtime in the form of color flow images, which provide 2D maps of axial flow velocity (or flow power) over an imaging view (Evans D H. Color flow and motion imaging. Proc. Inst. Mech. Eng. H, 2010; 224: 241253; Hoskins P R, McDicken W N. Colour ultrasound imaging of blood flow and tissue motion. Br. J. Radiol., 1997; 70: 878890). This flow imaging mode, when used together with the Doppler spectrogram mode that plots the temporal flow profile at a single range gate, can offer vast information about flow behavior in both spatial and temporal dimensions (Gaitini D, Soudack M. Diagnosing carotid stenosis by Doppler sonography: state of the art. J. Ultrasound Med., 2005; 24: 11271136; Hoskins P R. Haemodynamics and blood flow measured using ultrasound imaging. Proc. Inst. Mech. Eng. H, 2010; 224: 255271).
Despite its popular role in clinical screening, ultrasound color flow imaging is known to possess method flaws (Evans 2010). In particular, as its operating principle is typically based on axial Doppler estimation, it is prone to error if the beamflow angle (i.e. angle between the ultrasound propagation axis and the flow trajectory) varies over the vasculature (Evans D H, Jensen J A, Nielsen M B. Ultrasound color Doppler imaging. Interface Focus, 2011; 1: 490502). This issue represents a significant pitfall in diagnostic scenarios where the vasculature is not in straighttube form, such as the bifurcation geometry found in the carotid arteries (Ku D N. Blood flow in arteries. Annu. Rev. Fluid Mech., 1997; 29: 399434). In these cases, it can be challenging for sonographers to properly interpret color flow images (Arning C, Eckert B. The diagnostic relevance of colour Doppler artefacts in carotid artery examinations. Eur. J. Radiol., 2004; 51: 246251; Rubens D J, Bhatt S, Nedelka S, Cullinan J. Doppler artifacts and pitfalls. Radiol. Clin. N. Am., 2006; 44: 805835), especially when exacerbated by pulsatile flow conditions with considerable temporal variations in flow velocities.
For ultrasound to succeed in providing unambiguous mapping of flow dynamics in tortuous vasculature, it is imperative to resolve the beamflow angle dependence problem and in turn derive velocity estimates that reflect the actual flow characteristics (Dunmire B, Beach K W, Labs K H, Plett M, Strandness Jr D E. Crossbeam vector Doppler ultrasound for angleindependent velocity measurements, Ultrasound Med. Biol., 2000, 26: 12131235; Tortoli P, Bambi G, Ricci S. Accurate Doppler angle estimation for vector flow measurements. IEEE Trans. Ultrason. Ferroelec. Freq. Contr., 2006; 53: 14251431; Tortoli P, Dallai A, Boni E, Francalanci L, Ricci S., “An automatic angle tracking procedure for feasible vector Doppler blood velocity measurements,” Ultrasound Med. Biol. (2010), 36: 488496). To fulfill this task, flow estimation needs to be performed not only along the axial direction (as is the case in color flow imaging) but also the lateral direction of the imaging view, so that both the flow angle and the velocity magnitude can be determined without uncertainty (Evans et al. 2011). Motivated by such rationale, new imaging paradigms for flow vector estimation have been proposed. Often categorized as vector flow imaging methods, these paradigms are generally based on four types of estimation principles: (i) multiangle Doppler analysis (Capineri L, Scabia M, Masotti L. A Doppler system for dynamic vector velocity maps. Ultrasound Med. Biol., 2002; 28: 237248; Kripfgans O D, Rubin J M, Hall A L, Fowlkes J B. Vector Doppler imaging of a spinning disc ultrasound Doppler phantom. Ultrasound Med. Biol., 2006; 32: 10371046; Pastorelli A, Torricelli G, Scabia M, Biagi E, Masotti L. A realtime 2D vector Doppler system for clinical experimentation. IEEE Trans. Med. Imag., 2008; 27: 15151524; (ii) biaxial phase shift estimation from acoustic fields with transverse oscillations (Pedersen M M, Pihl M J, Haugaard P, Hansen J M, Hansen K L, Nielsen M B, Jensen J A. Comparison of realtime in vivo spectral and vector velocity estimation. Ultrasound Med. Biol., 2012; 39: 145151; Udesen J, Jensen J A. Investigation of transverse oscillation method. IEEE Trans. Ultrason. Ferroelec. Freq. Contr., 2006; 53: 959971; Udesen J, Nielsen M B, Nielsen K R, Jensen J A. Examples of in vivo blood vector velocity estimation. Ultrasound Med. Biol., 2007; 33: 541548); (iii) interframe blood speckle tracking (Bohs L N, Geiman B J, Anderson M E, Gebhart S C, Trahey G E. Speckle tracking for multidimensional flow estimation. Ultrasonics, 2000; 38: 369375; Ebbini E S. Phasecoupled twodimensional speckle tracking algorithm. IEEE Trans. Ultrason. Ferroelec. Freq. Contr., 2006; 53: 972990; Xu T, Bashford G R. Twodimensional blood flow velocity estimation using ultrasound speckle pattern dependence on scan direction and Aline acquisition velocity. IEEE Trans. Ultrason. Ferroelec. Freq. Contr., 2013; 60: 898908); and (iv) directional crosscorrelation analysis (Jensen J A. Directional velocity estimation using focusing along the flow direction I: theory and simulation. IEEE Trans. Ultrason. Ferroelec. Freq. Contr., 2003; 50: 857872; Jensen J A, Bjerngaard R. Directional velocity estimation using focusing along the flow direction II: experimental investigation. IEEE Trans. Ultrason. Ferroelec. Freq. Contr., 2003; 50: 873880; Kortbek J, Jensen J A. Estimation of velocity vector angles using the directional crosscorrelation method. IEEE Trans. Ultrason. Ferroelec Freq. Contr., 2006; 53: 20362049). While each of these approaches has its own merit, they are all known to yield erroneous flow vector estimates under certain scenarios. Notably, Doppler/phaseshift estimation is prone to aliasing artifacts when tracking fast flow, whereas speckle tracking and directional crosscorrelation have difficulty in following outofplane motion (Hansen L K, Udesen J, Oddershede N, Henze L, Thomsen C, Jensen J A, Nielsen M B. In vivo comparison of three ultrasound vector velocity techniques to MR phase contrast angiography. Ultrasonics, 2009; 49: 659667; Swillens A, Segers P, Torp H, Lovstakken L. Twodimensional blood velocity estimation with ultrasound: speckle tracking versus crossedbeam vector Doppler based on flow simulations in a carotid bifurcation model. IEEE Trans. Ultrason. Ferroelec. Freq. Contr., 2010a; 57: 327339). These frailties are particularly exposed when flow velocities vary significantly over different phases of a pulsatile flow cycle and when the imaging frame rate is inadequate (Swillens A, Segers P, Lovstakken L. Twodimensional flow imaging in the carotid bifurcation using a combined speckle tracking and phaseshift estimator: a study based on ultrasound simulations and in vivo analysis. Ultrasound Med. Biol., 2010b; 36: 17221735).
The present invention is directed to an apparatus and methods for imaging and complex analysis of vascular flow with spatiotemporal fluctuations that is noninvasive, accurate, and timeresolved. In particular, the invention is a new ultrasoundbased framework that can be called “Vector Projectile Imaging” (VPI), which dynamically renders complex flow patterns over an imaging view at millisecond time resolution. VPI is founded upon three principles: (i) highframerate broadview data acquisition (based on steered plane wave firings); (ii) flow vector estimation derived from multiangle Doppler analysis (coupled with data regularization and leastsquares fitting); and (iii) dynamic visualization of colorencoded vector projectiles (with flow speckles displayed as an adjunct).
VPI can enable quantitative and consistent tracking of spatiotemporally varying flow trajectories in curvy vascular geometries like the carotid bifurcation. The present invention is more advanced than current methods. In particular, the present invention has duplex mode flow information, including velocityencoded flow projectiles (instead of just a particle) and flow speckles. The present invention utilizes new methods for both data processing and rendering to support consistent flow estimation. In designing VPI, consistent flow vector estimation performance is achieved through the integrative use of: (i) broadview insonation schemes that can readily offer high data acquisition frame rates well beyond the video display range; and (ii) multiangle Doppler analysis coupled with posthoc regularization strategies and leastsquares estimation principles. Furthermore, provided that high imaging frame rates and consistent flow vector estimates are available, it is estimated that dynamic visualization of flow vectors can be made possible through rendering them in a duplex form that depicts: (i) particle projectiles, which quantitatively highlight the local flow speed, orientation, and trajectory; and (ii) flow speckles, which serve as a qualitative adjunct to enhance visualization effect.
The present invention is readily distinguished from earlier efforts that showed the feasibility of achieving highframerate vector flow imaging through spherical wave firings (Nikolov S I, Jensen J A. Invivo synthetic aperture flow imaging in medical ultrasound. IEEE Trans. Ultrason. Ferroelec. Freq. Contr., 2003; 50: 848856; Oddershede N, Jensen J A. Effects influencing focusing in synthetic aperture vector flow imaging. IEEE Trans. Ultrason. Ferroelec. Freq. Contr., 2007; 54: 18111825) or plane wave excitation (Ekroll I K, Swillens A, Segers P, Dahl T, Torp H, Lovstakken L. Simultaneous quantification of flow and tissue velocities based on multiangle plane wave imaging. IEEE Trans. Ultrason. Ferroelec. Freq. Contr., 2013; 60: 727738; Flynn J, Daigle R, Pflugrath L, Linkhart K, Kaczkowski P. Estimation and display for vector Doppler imaging using plane wave transmissions. Proc. IEEE Ultrason. Symp., 2011; 413418, Flynn J, Daigle R, Pflugrath L, Kaczkowski P. High frame rate vector velocity blood flow imaging using a single plane wave transmission angle. Proc. IEEE Ultrason. Symp., 2012; 323325; Lu J Y, Wang Z, Kwon S J. Blood flow velocity vector imaging with high frame rate imaging methods. Proc. IEEE Ultrason. Symp., 2006; 963967; Udesen J, Gran F, Hensen K L, Jensen J A, Thomsen C, Nielsen M B. High framerate blood vector velocity imaging using plane waves: simulations and preliminary experiments. IEEE Trans. Ultrason. Ferroelec. Freq. Contr., 2008; 55: 17291743). As will be demonstrated through an anthropomorphic flow phantom validation study, VPI represents an imaging innovation that uniquely couples highframerate data acquisition, regularized flow vector estimation, and novelties in dynamic visualization.
VPI has the following key features that distinguish it from conventional color flow imaging (CFI): 1) Can readily offer imaging frame rates well beyond video display range (e.g. >1,000 fps); 2) Provides estimates of flow velocity vectors to account for multidirectional flow components; 3) Projectilebased rendering of flow vectors to highlight their spatiotemporal dynamics. These features are made possible through a unique marriage of broadview data acquisition principles, flow vector analysis algorithms, and novelties in computer vision.
The steps of the technique of the present invention include data acquisition, regularized flow vector estimation, and dynamic visualization. Each of the steps can be briefly summarized as follows.
1. Data Acquisition: VPI is based on the use of M steered plane waves^{1 }during ultrasonic transmission (Tx). For each Tx angle, parallel beamforming for a 2D image grid is performed over N dynamic receive (Rx) steering angles. The MTx pulsing scheme is looped repeatedly as analogous to slowtime sampling in Doppler. The effective frame rate for VPI is then simply equal to FR=f_{PW}/M, where f_{PW }is the rate of each plane wave firing event (i.e. equal to the pulse repetition frequency, or PRF). By simply adjusting PRF, the FR can be boosted to >1,000 fps as needed to track fastchanging flow. Note that, for each pixel P_{o }within the 2D image grid, there are MN beamformed ensembles along slowtime (i.e. MN number of 1D slowtime signal arrays; each corresponding to one combination of TxRx angle).
2. Regularized Flow Vector Estimation: This process is performed independently for individual pixels. In every realization, the process can be divided into two stages: (i) velocity estimation for each of the MN slowtime ensembles for that pixel; (ii) vector computation via leastsquares fitting of MN velocity estimates derived from all TxRx angle pairs. In Stage 1, for each of the MN slowtime ensembles (matrix notation x_{mn}), multilevel subsampling is first performed to attain finer slowtime resolution. For this set of subsampled ensembles constructed for x_{mn}, three steps are performed: (i) clutter filter (to suppress tissue echoes); (ii) velocity estimation (based on lagone autocorrelation: the classical CFI estimator); (iii) aliasing correction. After that, the subsampled ensembles are averaged to arrive at the mean flow estimate for that TxRx angle pair. After doing the same for all angle pairs, MN raw velocity values (matrix notation u) would become available. In Stage 2, a flow vector v=(v_{x}, v_{z}) is derived from u as follows. First, based on crossbeam Doppler principles^{2 }an MN×2 angle matrix A is formed from all MN TxRx angle combinations. It is known that u=Av. Hence, v is computed by carrying out the leastsquares fitting operation Au.
3. Dynamic Visualization: For the v estimate of each pixel, a singlehue, colorcoded arrow is formed. For high velocity magnitude, its corresponding arrow would be brighter in color and longer in length. Arrows for different VPI frames are compiled, and they are displayed as moving projectiles to enable quantitative visualization.
This patent or application contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.
An arrangement of apparatus for carrying out the vector projectile imaging (VPI) of the present invention is illustrated in
To achieve data acquisition an ultrasonic array transducer 10 is positioned on the outside of the patient'"'"'s body adjacent the vasculature in which the fluid (e.g., blood) is to be imaged. The array 10 transmits a series of ultrasonic unfocused, steered plane waves 11 into the tissue at a high rate. The transmission angle with respect to the transducer surface 13 is changed after each wave. The transducer 10 also receives the waves reflected from the tissue at different receive steering angles and stores it in PrebeamFormed Data Acquisition device 12. This data is in the form of frames for each angle.
Each received frame of data is applied to a separate Beam Former 30 (enclosed in dotted lines), which includes BeamForming circuits 14_{1 }to 14_{N}. The output of each beam forming circuit is subsampled in SubSampling circuits 16_{1 }to 16_{K }for levels 1 through K. In turn, the output of each subsampling circuit is processed in a Regularized Flow Estimation circuit 18. Then the outputs of the flow estimation circuits for a frame are averaged in circuits 20.
A Least Squares Vector Estimation circuit 22 performs two major processing stages, i.e., (i) regularizing of the frequency shift estimation on each frame and; (ii) axiallateral vector component estimation based on leastsquares fitting. This circuit uses a customized estimation algorithm with posthoc data regularization. In Display Rendering circuit 24 a duplex visualization method is used in which the primary channel shows colorencoded particle projectiles (little arrows) whose color code and length are both related to the velocity magnitude at a particular location in the imaged vasculature. The direction of the projectiles shows flow direction. The position of these projectiles is dynamically updated between frames to quantitatively highlight flow paths together with changes in magnitude and orientation. The secondary visualization channel depicts grayscale flow speckles that are derived based on the slowtime filter power. This supplementary flow information serves as an adjunct depiction of flow trajectories. The output of circuit 24 is the VPI image 26
Data Acquisition
Data acquisition in VPI is based upon the use of plane wave transmissions and parallel beamforming (both performed from multiple angles). As shown in
Using a VPI framework, acquisition of flow vector information at high frame rates is facilitated by performing steered plane wave transmissions whose operating principles have recently matured for ultrasound imaging applications. See, Montaldo G, Tanter M, Bercoff J, Benech N, Fink M., “Coherent planewave compounding for very high frame rate ultrasonography and transient elastography,” IEEE Trans. Ultrason. Ferroelec. Freq. Contr., (2009), 56: 489506, which is incorporated herein in its entirety. As shown in
For each plane wave transmission at a given Tx angle, a set of N beamformed data frames is generated in parallel based on the corresponding array of channeldomain pulse echoes (waves reflected from the tissue) which are acquired from the transducer 10 at the PreBeam Forming Data Acquisition Device 12. As illustrated in
In order to monitor temporal changes in flow dynamics, plane wave transmission and receive beamforming are carried out multiple times for all MN TxRx angle pairs (as indicated in
Flow Vector Estimation
At each slowtime instant, the VPI method of the present invention performs flow vector estimation independently at all pixel positions based on their corresponding set of MN slowtime ensembles.
Flow vector estimation in VPI works by processing, for every pixel position, its corresponding set of slowtime signals from all TxRx angle pairs.
As illustrated in
To obtain consistent slowtime frequency shift estimates as necessary for accurate vector computation, a customized estimation algorithm with posthoc data regularization is devised to individually process every filtered slowtime ensemble. As shown in
Following the estimation steps, a twostage posthoc processing strategy is performed to regularize entries of the slowtime frequency estimate ensembles. First, to remove spurious estimates at time instants where flow is not detected by a TxRx angle pair, entries in the slowtime frequency ensembles are set to zero if their respective slowtime power estimate at that instant is below a predefined threshold (i.e. a flow classification mask 38 is applied similar to the color gain mask in color flow imaging). Second, akin to previous efforts in color flow signal processing, phase unwrapping is applied to the sifted slowtime frequency estimates to account for possible aliasing artifacts that may well occur when performing the lagone autocorrelation algorithm. See, Lai X, Torp H, Kristoffersen K, “An extended autocorrelation method for estimation of blood velocity,” IEEE Trans. Ultrason. Ferroelec. Freq. Contr., (1997), 44: 13321342, which is incorporated herein in its entirety. This step effectively extends the dynamic range of the slowtime frequency estimates beyond the Nyquist sampling limit. Note that both regularization steps are performed individually on all MN ensembles of slowtime frequency estimates.
LeastSquares Vector Computation Algorithm
Once the MN slowtime frequency shifts are estimated from all TxRx angle pairs, the axial and lateral components of the flow vector are derived using multiangle Doppler analysis principles. See, Dunmire et al. 2000 cited above, which is incorporated in its entirety. This computational task is equivalent to solving an overdetermined system of equations whereby MN data values (i.e. frequency shift estimates from all the TxRx angle pairs at one slowtime instant) are used as inputs to solve for two unknowns (i.e. axial velocity and lateral velocity). Specifically, the flow vector v=(v_{z}, v_{x}) can be estimated through a leastsquares fitting approach. The advantage of adopting this algebraic framework is that each resulting flow vector estimate would be optimized in the sense that its mean squared error is minimized for a given input. In turn, in cases with noisy data input, consistent estimation performance can still be maintained compared with the twoequation, twounknown formulation that corresponds to the conventional twoangle vector Doppler method (Ekroll et al. 2013; Kripfgans et al. 2006 cited above).
In the leastsquares vector computation method, v can be calculated by carrying out a matrix operation with each MN×1 measurement vector u (consisting of individual frequency shift values).
The leastsquares flow vector estimator can be considered a generalized form of the crossbeam Doppler estimation method in which multiple TxRx angle pairs are used in lieu of the twoangle approach that has been reported previously. See, Kripfgans et al. 2006; Tortoli et al. 2006, 2010; and Ekroll et al. 2013, all cited above and incorporated herein in their entirety. In the case with MN combinations of TxRx angle pairs, the m^{th }Tx angle can be denoted as θ_{m }and the n^{th }Rx angle can be denoted as φ_{n}. Note that, since plane wave transmissions and dynamic receive focusing are used, θ_{m }and φ_{n }would remain the same for all pixels within the imaging view. It is wellknown from the Doppler equation that, with this angle pair configuration, the slowtime frequency shift ϕ_{mn }for an object moving at velocity magnitude v and angle α is equal to:
where c_{o }is the acoustic speed and f_{o }is the ultrasound center frequency. See, Dunmire et al. 2000, cited above and incorporated herein in its entirety. Following similar derivations, the mathematical form expressed in (A1) can be modified by noting two points: (i) there exists a trigonometry relation [cos(A−B)=cos(A)cos(B)+sin(A)sin(B)]; and (ii) the axial and lateral velocity components are respectively equal to v_{z}=v cos(α) and v_{x}=v sin(α). See, Tsang I K H, Yiu B Y S, Yu A C H, “A leastsquares vector flow estimator for synthetic aperture imaging,” Proc. IEEE Ultrason. Symp., (2009), 13871390, which is incorporated herein in its entirety. Substituting these relations into (A1), the following revised form of the Doppler equation can be obtained:
The flow vector estimator seeks to solve for v_{x }and v_{z}, the two unknowns in (A2), by forming an overdetermined system of equations from MN realizations of (A2) as made available through the use of different TxRx angle pairs. In matrix notation, this system of equations can be expressed in the following form for a given flow vector v=(v_{x}, v_{z}):
where A is the anglepair matrix (MN×2 in size) and u as the measurement vector (MN×1 in size). Note that u_{mn }is essentially equal to the right hand side of (A2) for a given TxRx angle pair (i.e. u_{mn}=c_{o}ϕ_{mn}/f_{o}).
From linear algebra principles, it is well known that v in Equation (3) can be found by multiplying the pseudoinverse of A with v: a solution that is often referred to as the leastsquares fitting solution. See, Moon T K, Stirling W C, “Mathematical Methods and Algorithms for Signal Processing,” Upper Saddle River: PrenticeHall, (2000), which is incorporated herein in its entirety. Thus, with each MN×1 measurement vector u (consisting of individual frequency shift values), v can be calculated by carrying out the following matrix operation:
where the T superscript denotes a matrix transpose operation and entity (A^{T}A)^{−1}A^{T }is wellknown in linear algebra as the pseudoinverse of matrix A. See Moon T K, Stirling W C, “Mathematical Methods and Algorithms for Signal Processing,” Upper Saddle River: PrenticeHall, (2000), which is incorporated herein in its entirety. Note that the pseudoinverse (A^{T}A)^{−1}A^{T }is essentially a 2×MN matrix of constant values (as long as the TxRx angle pairs remain the same). Thus, the same pseudoinverse is applicable to different pixel positions. It is also worth pointing out that, for the leastsquares estimator given in Equation (4), the resulting flow vector estimate can be considered as an optimal solution in the sense that its meansquared error is minimized for a given input. It is worth emphasizing that Equation (4) is carried out individually at every pixel position and at each slowtime instant.
Dynamic Visualization Procedure
Using the computation protocol according to the present invention, frames of flow vector information can be generated at a rate of f_{VPI}, which equals to f_{DAQ}/K [i.e. f_{PRF}/(MK)] for a step size of K slowtime samples when executing the sliding window implementation. To facilitate dynamic rendering of these flow vector estimates, a novel duplex visualization method is used. Its primary visualization channel shows colorencoded particle projectiles (small arrows) whose color code and projectile length are both related to the velocity magnitude (on a scale from zero to a tunable maximum value). The direction of the projectiles shows flow direction. The position of these projectiles is dynamically updated between frames to quantitatively highlight flow paths together with changes in magnitude and orientation. The secondary visualization channel depicts grayscale flow speckles that are derived based on the slowtime filter power. This supplementary flow information serves as an adjunct depiction of flow trajectories in ways similar to that offered by the Bflow imaging technique. See, Chiao R Y, Mo L Y, Hall A L, Miller S C, Thomenius K E, “Bmode blood flow (Bflow) imaging,” In: Proceedings, IEEE Ultrasonics Symposium, San Juan, Puerto Rico, 2225 October. New York: IEEE; (2000), p. 14691472; and Lovstakken L, Bjaerum S, Martens D, Torp H, “Blood flow imaging—A new realtime, 2D flow imaging technique,” IEEE Trans Ultrason Ferroelectr Freq Control (2006),53:289299. Note that the graphical representation of multidirectional flow dynamics rendered by our duplex visualization approach is essentially different from the dotbased particle visualization algorithm that has been reported recently in ultrasound flow imaging (Flynn et al. 2011).
VPI provides quantitative flow visualization through dynamic rendering of colorencoded particle projectiles. In
The dynamic projectile method of the VPI visualization protocol works as follows: First, as shown in
Hardware and Parameters
The VPI invention has been implemented using a researchpurpose, channeldomain imaging platform that allows the transmission and reception operations of each array element to be configured individually. This platform is a composite system in which the frontend of a SonixTouch research scanner (Ultrasonix, Richmond, BC, Canada) was coupled to a prebeamformed data acquisition tool, and data was streamed to a backend computing workstation through a universal serial bus link (specifications listed in Table 1a). An L145 linear array (Ultrasonix) was used as the operating transducer. See, Cheung C C P, Yu A C H, Salimi N, Yiu B Y S, Tsang I K H, Kirby B, Azar R Z, Dickie K, “Multichannel prebeamform data acquisition system for research on advanced ultrasound imaging methods,” IEEE Trans. Ultrason. Ferroelec. Freq. Contr., (2012) 59: 243253, which is incorporated herein in its entirety.
Pressure field recordings of this ultrasound transmission hardware were taken using a membrane hydrophone (HMB0500, Onda, Sunnyvale, Calif., USA) that was mounted on a threeaxis micropositioner (ASTS01, Onda). When operating in plane wave excitation mode, our scanner hardware generated a derated peak negative pressure of 0.72 MPa (located at 2cm depth). This pressure value, for our given pulsing parameters, corresponded to a mechanical index of 0.32; spatialpeak, temporalaverage intensity of 0.16 W/cm2; and spatialpeak, temporalpeak intensity of 27 W/cm2 (assuming operation in 37_C degassed water). These numbers were well within the safety limits defined by the U.S. Food and Drugs Administration. See Duck F A, “Medical and nonmedical protection standards for ultrasound and infrasound,” Prog Biophys Mol Biol (2007) 93: 176191.
A vector estimation configuration with three Tx angles (−10°, 0°, +10°) and three Rx angles (−10°, 0°, +10°) was implemented (i.e. M=3, N=3, MN=9). To realize such a configuration, a steered plane wave pulsing sequence was programmed on the platform by executing relevant functions in the TEXO software development kit (Ultrasonix) to define array channel delays that only generate angle steering without focusing. Typical pulseecho imaging parameters were used as summarized in Table 1b, and prebeamformed channeldomain data was acquired on reception.
A 3Tx, 3Rx VPI configuration was implemented. Also, for each TxRx angle pair, a 3level subsampling was imposed during flow estimation. VPI was tested on anatomically realistic flow models that resembled healthy and stenosed carotid bifurcation. These are suitable geometries because flow dynamics within them are known to be multidirectional and significantly timevarying. The phantoms are wallless designs based on lostcore casting with polyvinyl alcohol gel. Pulsatile flow is supplied through the use of a gear pump with programmable flow rates. The resulting images are shown in
After streaming the acquired data offline to the backend processor, various image formation and visualization operations were carried out as required for VPI. First, to improve the channeldomain signaltonoise ratio, a finiteimpulseresponse band pass filter (minimum order; parameters listed in Table 1c) was applied to the prebeamformed data of each channel using Matlab (R2012a; Mathworks, Natick, Mass., USA). After that, delayandsum beamforming from the three Rx angles were executed using a graphical processing unit (GPU) based parallel computing approach like that disclosed in Yiu et al. 2011, which was previously cited and is incorporated by reference in its entirety. Note that an array of two GTX590 GPUs (NVidia, Santa Clara, Calif., USA) was used for this operation to facilitate processing at realtime throughput. Subsequently, an implementation for speckle imaging was used as disclosed in Yiu B Y S, Tsang I K H, Yu A C H, “GPUbased beam former: fast realization of synthetic aperture imaging and plane wave compounding,” IEEE Trans. Ultrason. Ferroelec. Freq. Contr., (2011), 58: 16981705. In addition, clutter filtering (in the form of a minimum order highpass finiteimpulseresponse filter; see Table 1d for parameters) and lagone autocorrelation were performed individually on the nine TxRx angle pairs at each pixel position. Other downstream operations related to vector estimation (posthoc regularization and leastsquares fitting) were then conducted in Matlab. Ad hoc persistence and median filtering were also performed. At last, VPI image sets were obtained and rendered using the duplex dynamic visualization algorithm of the present invention and the parameters listed in Table 1d. Note that the flow information in our VPI image sets was overlaid on top of a background Bmode image frame that was formed from spatial compounding of the beamformed data frames formed from the nine TxRx angle pairs.
In order to evaluate the accuracy of the flow vector estimation algorithm used in our VPI technique, steadyflow calibration experiments were first conducted using a multivessel flow phantom that we fabricated inhouse. The phantom comprised three wallless straight tubes whose long axes were aligned along the same plane; each tube had a different diameter (2, 4 and 6 mm) and flow angle (−10°, 0° and +10°), and they were positioned at different depths (1.5, 4 and 6 cm). By use of an investment casting protocol similar to that described in our previous work (Yiu and Yu 2013, previously cited), the phantom was fabricated with polyvinyl alcohol cryogel as the tissuemimicking material, whose acoustic attenuation coefficient and acoustic speed were respectively measured to be 0.24 dB/cm, MHz and 1518 m/s.
The phantom was connected to a gear pump (AccuFlowQ, Shelley Medical Imaging, London, ON, Canada) that supplied continuous circulation of bloodmimicking fluid (Shelley; 1037 kg/m3 density, 3.95 3 106 m2/s viscosity) at a flow rate of 2.5 mL/s. The transducer scan plane was aligned to the long axis of the three vessels (which were expanded to diameters of 2.2, 4.4 and 6.3, respectively, because of flowmediated dilation). Raw data were then acquired for the threeTx, threeRx configuration based on the parameters described above, and the lumen velocity profiles were estimated using VPI'"'"'s flow vector computation algorithm. Results were correlated with the theoretical parabolic profiles, whose centerline velocities were 131, 31 and 16 cm/s, respectively, for the three dilated vessels
To assess the practical efficacy of the VPI technique, this framework was used to image complex flow dynamics inside anatomically realistic carotid bifurcation phantoms. In particular, efficacy of VPI was evaluated through an anthropomorphic carotid bifurcation phantom study.
Note that the carotid bifurcation vasculature is rather suitable for this investigation because it possessed curved vessel geometries in the vicinity of the junction between three branches: the common carotid artery (CCA), the internal carotid artery (ICA), and the external carotid artery (ECA). In other words, it effectively allowed testing of the ability of the threeTx, threeRx VPI configuration to track flow patterns with significant multidirectional and spatiotemporal variations. Another point of merit in using these geometries is that their flow dynamics have already been extensively characterized by others using optical particle image velocimetry (Kefayati S, Poepping T L “Transitional flow analysis in the carotid artery bifurcation by proper orthogonal decomposition and particle image velocimetry.” Med. Eng. Phys., 2013; 35: 898909.; Poepping et al. (2010)) and computational fluid dynamics (Steinman D A, Poepping T L, Tambasco M, Rankin R N, Holdsworth D W, “Flow patterns at the stenosed carotid bifurcation: effect of concentric versus eccentric stenosis,” Ann. Biomed. Eng., 2000; 28: 415423. This information effectively provides an established reference for comparison with the flow patterns rendered by VPI.
Two different carotid bifurcation phantom models were used for experimentation (i) healthy coplanar geometry (
A steadyflow calibration experiment was first conducted by connecting the healthy bifurcation phantom to a gear pump (AccuFlowQ; Shelley Medical Imaging, London, ON, Canada) that supplied continuous circulation of blood mimicking fluid (Shelley; 1037 kg/m^{3 }density, 3.95×10^{6 }m^{2}/s viscosity) at 5 ml/s flow rate. The transducer scan plane was aligned to the CCA long axis (expanded to 6.4 mm diameter due to flowmediated dilation), and raw data was acquired for the threeTx, threeRx configuration based on the parameters described earlier. The lumen velocity profile was then estimated using VPI'"'"'s flow vector computation algorithm. Results were compared to the theoretical parabolic profile (with 31 cm/s centreline velocity).
Next, pulsatile flow experiments were performed using the bifurcation phantoms. The flow pulse, with a 72 bpm pulse rate (i.e. 1.2 Hz) and a 5 ml/s systolic flow rate, resembled a carotid pulse pattern that featured a primary systolic upstroke and a secondary dicrotic wave (predefined in the pump system). VPI cineloops were then generated by processing raw data acquired under such flow settings to determine the ability of VPI in visualizing complex flow features. To facilitate comparison, Doppler spectrograms were computed at representative pixel positions in the imaging view by reprocessing the raw slowtime ensembles at those places.
The vector computation algorithm of VPI was found to be capable of deriving flow vector estimates at high accuracy. Corresponding results obtained from the steadyflow calibration experiment are shown in
The plots are the flow vector profiles in the CCA of a healthy bifurcation phantom (with 5 ml/s constant flow rate) with the transducer placed in parallel with the CCA vessel. As can be observed in
The estimated flow speed magnitude across the lumen of the three vessels was generally found to resemble a parabolic shape that matched well with the theoretical prediction. As illustrated in
Calibration results showed that, using three transmit angles and three receive angles (−10°, 0°, +10° for both), VPI can accurately compute flow vectors even when the transducer was placed in parallel to the vessel (6.4 mm dilated diameter; 5 ml/s steady flow rate). The practical merit of VPI was further demonstrated through an anthropomorphic flow phantom investigation that considered both healthy and stenosed carotid bifurcation geometries. For the healthy bifurcation with 1.2 Hz carotid flow pulses, VPI was able to render multidirectional and spatiotemporally varying flow patterns (using 416 fps nominal frame rate, or 2.4 ms time resolution). In the case of stenosed bifurcation (50% eccentric narrowing), VPI enabled dynamic visualization of highspeed flow jet and recirculation zones.
Using the VPI technique, timeresolved quantitative visualization of multidirectional and spatiotemporally varying flow patterns that emerge within curvy vasculature under pulsatile flow conditions were achieved for a healthy carotid bifurcation with 72 bpm pulse rate. As an illustration,
The nominal VPI frame rate (f_{VPI}) was 416 fps, and it was played back at 50 fps (f_{DAQ }was 3,333 Hz). The rendered flow dynamics were found to be consistent with wellestablished findings obtained from computational predictions See, Berger S A, Jou L D, “Flows in stenotic vessels,” Annu. Rev. Fluid Mech., (2000) 32: 347382). In particular, it can be readily observed that the temporal evolution of flow speed and flow direction rendered by VPI in different parts of the vasculature are, as expected, synchronized with the stroke of the flow pulse. Also, in the ECA branch (lower branch), streamlined forward flow (without reversal) along the vasculature was evident throughout the pulse cycle. This latter observation effectively demonstrates that VPI'"'"'s flow vector estimation procedure is robust against flow angle variations, which do arise in the ECA as its inlet segment is inherently tortuous.
The technical merit of VPI is perhaps more notably demonstrated by its rendering of flow disturbances in the ICA branch of the healthy carotid bifurcation. This branch corresponds to the upper branch in
The millisecond time resolution (2.4 ms for 416 fps nominal frame rate) of VPI effectively enabled tracking of when a flow disturbance emerged in the carotid bulb of the healthy bifurcation vasculature.
To further demonstrate VPI'"'"'s efficacy in accurately visualizing highly complex flow dynamics,
One striking observation to be noted is that, during systolic upstroke, the formation of a highvelocity flow jet (red arrows) can be dynamically visualized at the site of stenosis. Indeed, the flow jet continued to propagate along the inner wall side of the ICA sinus (i.e. the unstenosed side) and spurted across the ICA lumen. It then collided against the outer ICA wall near the distal end of the carotid bulb where the ICA vessel started to become straightened. Upon hitting the outer wall, the jet direction was reoriented tangentially against the wall and eventually ramped off before it dissipated further downstream.
In
As illustrated in
Using ultrasound to visualize complex flow dynamics is inherently not a straightforward task. In developing a prospective solution, two practical vascular flow conditions must be taken into account: (i) at a given time instant, flow speed and direction (i.e. the flow vector) may vary spatially because of the tortuous nature of vascular geometry; and (ii) over a cardiac pulse cycle, flow components would deviate temporally due to pulsatile behaviour. VPI has been designed to capture and render these spatiotemporal dynamics in blood flow.
From a technical standpoint, VPI is equipped with three key features that enable timeresolved visualization of flow vectors over an imaging view. First, it performs highframerate broadview data acquisition via multiangle plane wave imaging principles, so as to achieve the high time resolution required to monitor flow pulsations and their spatial variations over an imaging view (
The practical merit of VPI in visualizing complex flow dynamics is demonstrated through a carotid bifurcation phantom study with controlled flow conditions that are otherwise not possible invivo (
As an integrative insight into VPI'"'"'s application potential in delineating specific details of complex flow patterns,
In the case of healthy bifurcation, it should be noted that the recirculation zone in the carotid bulb region is larger at the end of postsystolic down stroke (
For the stenosed carotid bifurcation, the spatial extent of its two flow recirculation zones shows substantial differences. While VPI did not detect significant changes in the size of the carotid bulb recirculation zone over the pulse cycle (
As mentioned above, the adjunct display of grayscale flow speckles can enhance the flow visualization performance of the algorithm because interframe flow speckle displacements can serve to highlight the flow trajectory path.
Being a newly developed technique with fine temporal resolution and flow vector estimation capabilities, VPI can be leveraged to investigate various forms of complex flow dynamics. For instance, besides using VPI to study flow patterns in the carotid bifurcation as demonstrated here, this technique can be used to examine multidirectional flow dynamics inside diseased vascular features such as aneurysms. Also, VPI can be applied to visualize flow turbulence with fluttering features that require fine temporal resolution to render coherently. Realizing these applications would effectively substantiate the diagnostic value of VPI in complex flow analysis.
As the fine temporal resolution offered by VPI hinges on the use of broadview data acquisition sequences in which the ultrasound firings are unfocused in nature, the flow signals returned from deeper vasculatures would inevitably be weaker as a consequence. This issue, which would be physically worsened by depth dependent attenuation, may pose a challenge when diagnosing certain patients whose vasculature tends to be positioned farther away from the skin surface. Hence, flow signal enhancement techniques may be used to reinforce the efficacy of the VPI framework when used in different invivo scan settings. One particular strategy that can be used is the incorporation of coded excitation principles into the transmission pulse sequence design. See Zhao H, Mo L Y L, Gao S, “Barkercoded ultrasound color flow imaging: Theoretical and practical design considerations,” IEEE Trans Ultrason Ferroelectr Freq Control (2007), 54:319331, which is incorporated herein in its entirety. Alternatively, microbubble contrast agents may be introduced to boost the flow signal level when performing VPI. See, TremblayDarveau C, Williams R, Milot L, Bruce M, Burns P N, “Ultrafast Doppler imaging of microbubbles,” In Proceedings 2012 IEEE Ultrasonics Symposium, Dresden, Germany, 710 October. New York: IEEE; (2012), p. 13151318.
Another aspect of VPI to be further refined is its engineering considerations regarding the technique'"'"'s real time realization. In the arrangement of
Alternatively, VPI can be further used for echocardiography investigations where vector visualization of intracardiac flow fields currently relies on either postprocessing of color flow imaging data or the use of microbubble contrast agents to perform echo particle image velocimetry. To realize VPI for echocardiography applications, the flow vector estimation framework would need to be further refined to account for the nonstationary tissue clutter that arises due to myocardial contraction. For instance, when deriving the frequency shift estimates of each TxRx angle pair, advanced signal processing solutions that are resilient against tissue motion biases could be adapted for this purpose, such as maximum likelihood estimation and adaptiverank eigenestimation. See, Lovstakken L, Bjaerum S, Torp H, “Optimal velocity estimation in ultrasound color flow imaging in presence of clutter,” IEEE Trans. Ultrason. Ferroelec. Freq. Contr., (2007), 54: 539549; and Yu ACH, Cobbold RSC, “Singleensemblebased eigenprocessing methods for color flow imaging—Part II. The matrix pencil estimator,” IEEE Trans. Ultrason. Ferroelec. Freq. Contr., (2008), 55: 573587, both of which are incorporated herein in their entirety.
VPI can be considered a new approach in leveraging ultrasound for flow estimation purposes. In particular, it represents a drastic transformation in the way that flow information is acquired, estimated, and rendered in comparison to conventional color flow imaging. Since VPI is essentially noninvasive, this technique should hold promise in being introduced as a routine diagnostic tool to investigate complex flow dynamics in the human vasculature. For instance, in the context of carotid diagnostics, VPI can potentially be adopted as a more instinctive way to assess the severity of carotid stenosis compared with the conventional Doppler spectrogram mode that is routinely performed as part of the clinical practice for carotid disease management. If such clinical translation effort can be realized, the present role of ultrasound in vascular diagnostics can undoubtedly be expanded.
The invention is not to be limited in scope by the specific embodiments described herein. Indeed, various modifications of the invention in addition to those described will become apparent to those skilled in the art from the foregoing description and accompanying figures. Such modifications are intended to fall within the scope of the appended claims.