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134 IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL, !"#. 58, $". 1, JANUARY 2011

Abstract—Doppler-based flow analysis methods require ac-quisition of ultrasound data at high spatio-temporal sampling rates. These rates represent a major technical challenge for ultrasound systems because a compromise between spatial and temporal resolution must be made in conventional approach-es. Consequently, ultrasound scanners can either provide full quantitative Doppler information on a limited sample volume (spectral Doppler), or averaged Doppler velocity and/or power estimation on a large region of interest (Doppler flow imaging). In this work, we investigate a di!erent strategy for acquiring Doppler information that can overcome the limitations of the existing Doppler modes by significantly reducing the required acquisition time. This technique is called ultrafast compound Doppler imaging and is based on the following concept: instead of successively insonifying the medium with focused beams, several tilted plane waves are sent into the medium and the backscattered signals are coherently summed to produce high-resolution ultrasound images. We demonstrate that this strat-egy allows reduction of the acquisition time by a factor of up to of 16 while keeping the same Doppler performance. Depend-ing on the application, di!erent directions to increase perfor-mance of Doppler analysis are proposed and the improvement is quantified: the ultrafast compound Doppler method allows faster acquisition frame rates for high-velocity flow imaging, or very high sensitivity for low-flow applications. Full quantitative Doppler flow analysis can be performed on a large region of interest, leading to much more information and improved func-tionality for the physician. By leveraging the recent emergence of ultrafast parallel beamforming systems, this paper demon-strates that breakthrough performances in flow analysis can be reached using this concept of ultrafast compound Doppler.

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D"++#,&--./,' imaging methods are well-established tools on ultrasound systems for flow analysis and

quantification, and have become mandatory in the con-text of cardiovascular disease assessment as well as cancer diagnosis. There are two di0erent kinds of Doppler modes available: spectral analysis (continuous or pulsed) and col-or-coded flow velocity and/or power imaging [1], [2].

Spectral analysis Doppler o0ers excellent temporal res-olution and provides in-depth quantification of flow char-acteristics by means of quantities such as the peak flow

velocity as a function of time, the mean flow velocity as a function of time, the resistance and pulsatility indices within a cardiac cycle, the spectral broadening index, etc. [1]. Spectral Doppler analysis requires continuous acquisi-tions or very high sampling rates (several thousand hertz). Flow quantification is then typically available only at a single location (sample volume) or multiple locations along the same line (multigating). Color flow imaging overcomes the limited spatial sampling of the spectral analysis by partly sacrificing the quantitative analysis, by reducing the observation time at any given location and spreading the ultrasound firings over a 2-D region of interest. The information displayed is the mean flow velocity and/or Doppler power estimated over an extended area. Those modes are displayed in real time at frame rates that are usually around a few hertz.

The big challenge of Doppler modes arises from the fact that physicians ideally require simultaneous real time dis-play of B-mode (gray scale) and PW-mode (duplex mode), or even B-, color- and PW-modes (triplex mode). Duplex and triplex simultaneous modes have become standard on ultrasound systems, but su0er from frame rate limitations in deep organs such as the liver or heart. Duplex and tri-plex modes represent major technical challenges because they require complex sequencing, high-energy ultrasound transmission, and high processing power. Severe tradeo0s on imaging mode quality and/or frame rate are necessary. Consequently, there is a crucial need to significantly re-duce the number of ultrasound firings required to perform Doppler analysis (i.e., reduce the acquisition time) while keeping constant or increasing performance.

Academic research into overcoming this issue has been, and continues to be, extensive. Many directions have been considered. The simplest solution consists of reducing the number of transmit beams per color flow image by widen-ing them [3]. Such an approach is currently implemented on ultrasound systems but requires tradeo0s between sen-sitivity and resolution to obtain a significant reduction of the acquisition time. Other approaches have been pro-posed, such as performing simultaneous transmissions [4] and parallel beamforming, using synthetic aperture imag-ing [5], [6], or reducing the number of samples required to perform the Doppler estimation (ensemble length) while introducing higher-performance processing methods [7], designing pulses able to perform B- and Doppler-mode imaging simultaneously [8]. Such approaches often require the use of open and fully programmable electronic plat-forms [9], [10]. Although proposed solutions show prom-ising results, they add complexity to the Doppler-mode

Ultrafast Compound Doppler Imaging: Providing Full Blood Flow CharacterizationJeremy Berco0, Gabriel Montaldo, Thanasis Loupas, David Savery, Fabien Mézière, Mathias Fink,

and Mickael Tanter

Manuscript received June 19, 2010; accepted October 8, 2010. J. Berco0, T. Loupas, D. Savery, and F. Mézière are with Super-

Sonic Imagine, R&D, Aix en Provence, France (e-mail: jeremy.berco0@ supersonicimagine.fr).

G. Montaldo, M. Fink, and M. Tanter are with Institut Langevin, École Supérieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI) ParisTech, Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (IN-SERM), Paris, France.

Digital Object Identifier 10.1109/TUFFC.2011.1780

sequence and processing paths. As a consequence, most of them have not yet become standards in current ultra-sound systems.

In previous work, we proposed the use of plane-wave insonifications to perform Doppler-based tissue motion analysis [11]. Plane-wave transmission represents the most e1cient solution in terms of number of firings because the whole medium is insonified in one shot. Ultrafast frame rates (several thousands of hertz) can therefore be achieved, and this has led to the introduction of a new quantitative elasticity imaging mode [12]. The plane wave technique implies compromises among resolution, contrast, and sen-sitivity that are not significant for tissue motion analysis but may become important when dealing with weak blood flow scatterers. Udesen et al. [13] recently tested the plane wave technique for color flow imaging. Coded excitations were used to improve the signal-to-noise ratio, but, as stated by the authors, the poor contrast of the technique limits its application to flow analysis in large arteries.

A way to improve the performance of ultrafast plane wave imaging is to use several tilted plane waves [14]. These waves are sent into the medium and the backscat-tered signals are coherently summed to produce a fully dynamically focused image (in transmit and receive). Re-cently, we introduced a new imaging method based on this approach called the ultrafast plane wave compound tech-nique [15]. We demonstrated that this technique allows the realization of a B-mode of equivalent quality to the standard focused approach with one-third the number of insonifications. We also successfully applied this concept to shear-wave-based elastography, allowing improvement of this mode in terms of resolution, contrast, and sensitiv-ity.

This paper investigates the ultrafast plane wave com-pound technique in the framework of Doppler-based flow analysis methods. The new technique is called ultrafast compound Doppler imaging and is described in Section II. Section III evaluates and quantifies the performance of the new color flow imaging mode in phantoms and compares it to conventional focused color flow imaging. It is shown that, for a given mode performance, the acquisition time can be reduced by a factor of up to 16. Based on these re-sults, Section IV demonstrates how color flow imaging can be enhanced using this insonification strategy through im-proved sequencing and processing schemes. In vivo results are presented. Section V proposes new tools for displaying and analyzing the flow data provided by ultrafast imaging (>500 Hz). Finally, Section VI discusses real-time imple-mentation of the new mode on an ultrasound system, and Section VII summarizes the conclusions of this study.

II. B.)23&"($'

A. Ultrafast Compound Imaging

In conventional ultrasound imaging, the medium is se-quentially insonified using focused beams along di0erent

directions (or lines). Each image line is then computed by processing the backscattered echoes coming from the insonified direction. The maximal frame rate to produce a focused image is set by the following equation:

FcZ nfoc

Lines21

, (1)

where Z is the maximal depth of the image, c is the speed of sound, and nLines is the number of insonified lines. De-pending on the application and the depth of interrogation, frame rates varying from a few tens of hertz down to a few hertz are typically achieved.

Ultrafast imaging can be performed by insonifying the medium with a single plane wave transmit. Backscattered echoes are simultaneously recorded from the entire scan plane, and all imaging lines are simultaneously computed using parallel beamforming processes. In this case, the maximal frame rate is [12], [16]

FcZflat 2

. (2)

The plane wave imaging method is the most e1cient way to increase frame rate, at the expense of image contrast and spatial resolution [16], [17].

In the ultrafast compound imaging method, a set of plane waves (NAngles) are sent into the medium at dif-ferent angles at an ultrafast frame rate. The backscat-tered echoes are, in a first processing step, beamformed to produce NAngles ultrasound images. Each ultrasonic image is produced by applying a conventional dynamic receive focusing along each line of the image (conventional delay-and-sum technique, fixed aperture ratio F/D ~ 1). In a second step, these beamformed images are coherently summed to obtain a compounded image which is dynami-cally focused in transmit and receive. It is important to note that the summation is done coherently before any nonlinear process (envelope detection, etc.). The frame rate is, in this case:

FcZ Ncomp

Angles21

. (3)

Compared with a single flat insonification, the ultra-fast compound imaging frame rates are reduced by NAngles (number of plane wave angles) to improve image quality (contrast, resolution). In a previous article [15], it was demonstrated that when using NAngles 4 40, the ultrafast compound imaging method has resolution, contrast, and signal-to-noise ratio equivalent to the conventional focused method. The acquisition time is then reduced by a typical factor of 3 to 6, depending on the number of focused lines (128 to 256) used in the conventional method. The con-cept of plane wave compounding for increased image qual-ity has been successfully applied to the field of transient elastography [15]. This work investigates the performance

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of the ultrafast compound imaging method for Doppler-based flow analysis.

B. New Technological Implementation for Fully Parallel Beamforming

Ultrafast compound imaging requires the ability to ac-quire and process ultrasound images at very high frame rates, typically thousands of hertz. Conventional ultra-sound systems usually reach frame rates of a few tens of hertz as medium insonification and signal processing are serialized by image line. Implementation of ultrafast compound approaches is therefore not possible on such systems because they require full parallelization of the im-age formation process.

New platform architectures are needed, based on the com-bination of ultrafast raw RF data acquisition capabilities and full software-based and parallelized beamforming schemes. The platform used in this work (Aixplorer, SuperSonic Imag-ine, Aix en Provence, France) meets those requirements and enables implementation of such new schemes.

C. Conventional Color Flow Imaging

In color flow imaging, flow velocity estimation relies on the use of N narrowband (a few cycles) transmit pulses sent at a fixed pulse repetition frequency (PRF) to esti-mate the Doppler frequency (Fs). N is commonly referred as the ensemble length. Based on the Nyquist theorem, to avoid aliasing, the PRF must be at least two times the highest flow-related Doppler frequency of interest:

PRFflow 2Fs. (4)

The main steps of the processing are wall filtering to dis-criminate tissue echoes from the flow signal and veloc-ity estimation, most commonly based on autocorrelation methods [18]–[20].

In this conventional approach, the sequencing strat-egy is determined by the ratio between the maximum PRF achievable by the system at the considered depth (PRFmax) and the necessary PRF to detect the desired maximum flow velocity (PRFflow). This ratio gives the number of lines that can be sequentially insonified before going back to the first insonified line:

Nlines = PRFmax/PRFflow. (5)

To generate a color flow image that contains more lines than Nlines, the image is subdivided into several segments of Nlines and the color sequence and processing are done sequentially for all segments as illustrated in Fig. 1(a).

The number of firings necessary to compute a full color flow image is given by the following formula

NFiringsC = Nlines · NSegments · N, (6)

where NSegments is the number of segments needed to com-plete the full color image.

D. Ultrafast Compound Doppler Imaging

In the ultrafast compound approach, a set of NAngles tilted plane waves are transmitted at the ultrafast frame rate (PRFmax) to generate a compounded image, as il-lustrated in Fig. 1. The sequence is repeated N times at PRFflow to be able to perform wall filtering and flow veloc-ity estimation.

The maximum number of angles is determined by the same parameter that controls the number of segments in the conventional method: the ratio between PRFmax and PRFflow:

NAnglesMax = PRFmax/PRFflow. (7)

One obvious advantage of the ultrafast compound approach is that the concept of scanning the color image on a segment-by-segment basis disappears. Because the whole medium is insonified for each transmision, there are no more tradeo0s between frame rate and size of the color box caused by se-quence timing issues. Moreover, flow velocity estimation is performed simultaneously for all pixels and not at di0erent time instances, as in the conventional approach, leading to true 2-D real-time Doppler flow imaging.

The number of firings necessary for a full color flow im-age is no longer linked to the number of lines within the

136 IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL, !"#. 58, $". 1, JANUARY 2011

Fig. 1. (a) Conventional color mode and (b) ultrafast compound Doppler sequences.

image, but to the number of tilted plane waves transmit-ted, according to

NFiringsUltrafast = NAngles · N. (8)

The gain GAT in acquisition time between conventional and ultrafast acquisitions is then set by the following for-mula:

G N N

N N N NAT FiringsC FiringsUltrafast

lines Segments An

/

/( ) ( ggles N). (9)

According to (5) and (7), and considering NAngles = NAnglesMax = Nlines, this leads to the gain

GAT = NSegments. (10)

For high-speed flows, the typical number of segments in a conventional color image is quite large, reaching up to NSegments = 64 or even more (very high PRFFlow and large color box). Therefore, the acquisition time gain of the ultrafast compound method is potentially huge for such large color boxes and high PRFs.

For low-speed flows, the gain in acquisition time is not important because NSegment is small (typically NSegment = 1 to NSegment = 3). However, the gain in terms of sensitiv-ity is considerable, because each pixel is insonified NAngles times more when using ultrafast compound Doppler.

To evaluate the relevance of the ultrafast compound method from a practical point of view, its performance shall be quantified and compared with the conventional focused Doppler technique. The next section is dedicated to the in vitro assessment of the ultrafast compound Dop-pler mode performance using the conventional color flow mode as a reference.

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The performances obtained in terms of resolution and contrast using plane wave compounding are studied in this

section to provide insights for the next color flow section. All experiments were conducted on ultrasound phantoms using the Aixplorer ultrasound system (SuperSonic Imag-ine). The probe used is a standard linear probe (128 ele-ments, 0.3 mm pitch, 5 to 12 MHz) dedicated to small parts and vascular applications. The probe was driven by the system at ±50 V. To perform image comparisons, the following parameters were chosen: Transmit pulse for both methods: 3 cycles at 5 MHz, f-number for the focused method = 3, maximal angle values for the compounded approach: ±9°. The maximum angle a0ects the resolu-tion of the compounded image, and has been chosen to match the resolution of the focused method (f-number = 3). The number of angles used in the compound plane wave method is a varying parameter of our experiments. Signals received by the system were sampled at 20 MHz. Ultrasound images were computed with a wavelength res-olution of 0.3 mm.

Spatial resolution and contrast were calculated from the experimental point spread function (PSF) of each im-aging sequence on a single strong scatterer (50-µm wire immersed in water). The 2-D PSFs for both modes were calculated and compared with the so-called Flat mode, which corresponds to the transmission of a single, un-steered, plane wave. An example of 2-D PSFs for both methods is shown in Fig. 2. The lateral resolution is as-sessed by measuring the width of the PSF at the 76-dB level. The axial resolution corresponds to the dimension of the PSF at the 76-dB level in the axial (depth) direc-tion. Finally, the anechoic contrast is calculated as the ratio between energy outside a circle of 5% centered in the PSF and the energy of the complete PSF (% being the wavelength corresponding to the central frequency of the pulse).

A. Resolution

The lateral resolution obtained for the three acquisi-tion methods is shown in Table I. Although the single

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Fig. 2. Experimental 2-D PSFs of (a) focused, (b) flat, and (c) ultrafast compound (with 9 angles) methods. (d) A transverse cut of the PSFs at the scatterer depth.

flat mode presents a lower lateral resolution, the ultra-fast compound method has equivalent resolution to the focused mode. This was expected, because the lateral resolution depends on the value of the maximum angles chosen (not on the number of angles used) [15]. These angles (±9°) have been e0ectively chosen to match the focused mode apertures and resolution. The axial resolu-tion is also shown in Table I. Because it depends only on the bandwidth of the ultrasonic pulse, it is almost identi-cal for all methods.

B. Anechoic Contrast

The anechoic contrast versus the number of angles is shown in Fig. 3. The contrast decreases rapidly with the number of angles: using NAngles = 9, the contrast level is at 737 dB, only 5 dB higher than that for the focused method. For NAngles = 16, the contrast di0erence is only 2 dB. As we will see in the next section, those numbers of angles are an excellent choice for low-flow velocity imaging that requires high sensitivity and resolution.

C. Signal-to-Noise Ratio

Montaldo et al. studied the signal-to-noise ratio of the synthetic image obtained using compounded plane wave insonifications compared with conventional B-mode im-ages [15]. Assuming independent noise between insonifica-tions, it was shown that the SNRs of both imaging meth-ods (at the focal depth of the conventional focused image) are linked by the following relation

SNR

SNRComp

Foc

Angles Angles FNg

N ZD

, (11)

where NAngles is the number of angles, g is the antenna gain, ZF is the focal depth, % is the wavelength, and D is the array aperture. Considering a typical case in which % = 0.3 mm, ZF = 30 mm, and D = 9 mm, the SNR should be the same for both methods when the number of angles is equal to 9. If using more angles, the plane wave com-pounding method should provide better SNR than the conventional approach.

Experiments were conducted on a phantom to confirm these findings. The SNR is measured using a phantom containing a homogeneous distribution of scatterers. This phantom is placed on a vibration-free table and a large set of images is acquired. For each pixel of the image, the mean signal s(x, z) and its standard deviation is calculated using the complete set of acquisitions.

These values depend mainly on depth. To obtain a pre-cise profile of the SNR as a function of depth, the SNR values are averaged in the lateral direction and the final measured SNR is

SNR( )( , )

( , ).z

s x z

x zx

x

(12)

Fig. 4(a) shows the depth dependence of the SNR for the conventional focused method and an ultrafast compound sequence relying on 16 angles. The focused method has a lower SNR than the compound except at focal depth.

To compare both methods, we can define the SNR gain as SNRcomp/SNRfoc. In Fig. 4(b), one can see that this gain varies from 10 to 0 dB with a mean 5 dB gain over all depths. Using an ultrafast compound sequence of 9 angles, the mean gain across the whole image is reduced to approximately 2.5 dB.

D. Flow Analysis

Experiments were conducted on a calibrated Doppler phantom (ATS 523A, ATS Laboratories Inc., Bridgeport, CT) with blood mimicking fluid (Shelley Medical Imag-ing Technologies, London, Ontario, CA) circulating with a mean flow velocity of 4 cm/s in a 4-mm-diameter ves-sel. The linear probe and acquisition parameters were the same as those used previously. The acquisition parameters are summarized in Table II. For the focus method, the beam focus has been adjusted in the middle of the vessel for the reference central line of the box (40 mm absolute value)

Figs. 5(a) and 5(b) compare the power Doppler images for both methods. Figs. 5(c) and 5(d) compare the color

138 IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL, !"#. 58, $". 1, JANUARY 2011

TABLE I. S+.%*.# R,/"#(%*"$ 5"& %8, D*55,&,$% M"',/.

Resolution Focused FlatCompound 9 angles

Compound 16 angles

Axial (mm) 1.07 1.10 1.01 1.02Lateral (mm) 0.54 0.86 0.53 0.53

Fig. 3. Anechoic contrast for di0erent methods. Using 9 angles, the con-trast is only 5 dB higher than in the focused mode.

Doppler images in the same configuration. Qualitatively, the methods are very similar.

For a quantitative comparison of both Doppler ap-proaches, two quantities were assessed: the blood-to-tissue ratio (BTR) in the power Doppler imaging mode and the mean squared velocity error (MSE). The BTR is defined as the average power signal within the flow vessel divided by the average power in surrounding tissues. The mean squared velocity error (MSE) corresponds to the devia-tion of the experimental flow pattern from a theoretical Poiseuille flow pattern distribution.

The measured BTR values are very similar for both im-aging sequences: 17.7 and 17.8 dB for conventional color

flow imaging and ultrafast plane wave compounding, re-spectively. Hence, it is clearly demonstrated that the tis-sue clutter level is similar for both imaging methods.

For the calculation of the MSE, the theoretical Poi-seuille profile v = vmax[1 7 (r/R)2] is defined, where R is the tube radius, r is the radial position within the tube, and vmax is the maximal velocity in the center of the tube. Because the Doppler angle between the flow and the beam direction is known (18°), angle correction is performed to derive the flow velocity from its projection along the direc-tion of the Doppler beam. The estimated MSE is found to be very similar for both methods: 0.25 and 0.27 cm/s for conventional color flow imaging and ultrafast plane wave compounding, respectively.

The performance of conventional method and plane wave compounding for color flow imaging is summarized in Table III. Quantitative values are provided for NAngles

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Fig. 4. (a) SNR versus depth: The ultrafast compound method with 16 angles has a higher SNR than the focused except at focal depth, where they are practically identical. (b) SNR gain versus depth. The mean gain is approximately 5 dB.

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Conventional focused

Compounded plane waves

Depth (mm) 50 50PRFmax (kHz) 14 14Lines in segments 9 —Angles — 9PRFflow (kHz) 1.55 1.55Lines to image 63 63Number of segments 7 —Ensemble length 11 11Number of firings 693 99Acquisition time (ms) 49 7

All parameters are identical except that the acquisition time is 7 times faster in the compound method.

Fig. 5. Comparison between focused and ultrafast compound Doppler images in a vessel phantom. (a) and (b) Power Doppler for focused and compound modes, respectively (scale in decibels). (c) and (d) Color Doppler for focused and compound method, respectively (scale in centi-meters/second).

= 1, NAngles = 9, and NAngles = 16 angles and compared with the conventional color flow imaging. One can observe that most values are similar, except the maximal PRF is 8 to 16 times faster for the ultrafast compounding approach depending on the number of angles used (9 or 16).

Reducing the plane wave compounding sequence to NAngles = 9 angles enables the generation of a very fast im-aging modality (16 times faster than the focused one) with a very moderate loss in contrast (5 dB) and equivalent res-olution and SNR compared with the conventional mode. Moving to the 16-angle sequence drops the maximum flow velocity detectable and the gain in acquisition time by a factor of 2 but allows contrast equivalent to conventional color flow. Trade-o0s are therefore possible depending on the type of flow analyzed. The next two sections illustrate how the optimization of such ultrafast sequences could pave the way to many potential improvements and new features of Doppler flow imaging. In Section IV, a method for the improvement of color flow imaging is presented and tested in vivo. In Section V, a new way to acquire, process, and analyze Doppler information with the use of ultrafast compounded sequences is introduced.

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In Section III, it was shown that the performance of conventional color flow imaging can be reached in much shorter acquisition time. Section IV investigates the pos-sibility of using the resulting available time to introduce additional color flow improvements. The sequences are optimized in a di0erent manner depending on the appli-cation considered: for fast-flow optimization, steep wall filtering and fast acquisition times are required, whereas for low-flow optimizations, very high sensitivity and reso-lution are pursued.

A. Fast Flow Optimization

To improve fast flow imaging, the number of angles must be limited to keep the PRF high enough. Thus, the ensemble length can be increased to fit the same acquisi-tion time as for the conventional focused method.

1) Experiment Setup: Experiments were performed in vivo on the common carotid of a healthy volunteer imaged using an 8-MHz linear probe (256 elements). To compare both methods at the same instant in the cardiac cycle, the ultrasonic sequence consists of 58 ms of conventional fo-cused imaging, immediately followed by 58 ms of ultrafast plane wave compounding. The parameters for the acquisi-tions are shown in Table IV. The number of firings, PRFs, and duration of the acquisition are exactly the same for both sequences. Only the total number of final images is di0erent: the ultrafast plane wave compounding sequence is able to acquire 176 frames, whereas the focused method generates only 11 frames because of the line-by-line ac-quisition.

To ensure patient safety, acoustic power and intensity (API) measurements were performed for both modes. The results are shown in Table V. Measurements have been performed following 60601–2-37 international guidance [21]. Values for both modes are below FDA safety limita-tions and plane wave compounding is found to outperform

140 IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL, !"#. 58, $". 1, JANUARY 2011

TABLE III. O!,&!*,9 "5 %8, I6.3*$3 P,&5"&6.$), "5 E.)8 M"',.

Conventional focused

Flat (NAngles = 1)

Compound (NAngles = 9)

Compound (NAngles = 16)

Axial res. (mm) 1.07 1.10 1.01 1.02Lateral res. (mm) 0.54 0.86 0.53 0.53Contrast (dB) 742 723 737 740Mean SNR gain (dB) 0 77 +2.5 +5BTR (dB) 17.7 12 17.8 19.1MSE 0.25 0.34 0.27 0.27Frame rate max (Hz) 100 10000 1600 800

The frame rate is calculated for a typical 4-cm-deep image comprising 128 lines. Compared with conventional color flow imaging, the plane wave compounding method reaches similar performances but exhibits a frame rate 8 to 16 times higher.

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Focused Compound

Depth (mm) 25 25PRFmax (kHz) 24 24Lines in segment 8 —Angles — 8PRFflow (kHz) 3 3Lines to image 128 128Number of segments 16 —Number of frames 11 176Total number of firings 1408 1408Acquisition time (ms) 58 58

All parameters are identical except for the number of images.

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Imaging method

Negative peak pressure

(MPa) MIIspta

(mW/cm2)

Conventional focused 2.46 1.1 < 1.9 220 < 720Ultrafast compound 1.79 0.8 < 1.9 120 < 720

conventional color flow in terms of mechanical index (MI) and spatial peak time average intensity (Ispta).

2) Improving Velocity Measurement Accuracy: Figs. 6(a) and 6(b) compares the images obtained with the two methods. These images are the direct output of the Dop-pler frequency calculated without any kind of spatial or temporal smoothing. As one can observe, the ultrafast plane wave compound Doppler image reaches a very high quality because of the long ensemble used (176 firings) whereas the conventional Doppler method generates an image with a high variance because of the limited tempo-ral averaging o0ered by the ensemble of just 11 firings.

Using the same set of data, one interesting experiment is to progressively reduce the number of acquisition frames in ultrafast plane wave compounding (i.e., the ensemble length) to increase the temporal resolution at the expense of image quality. Figs. 6(c) to 6(f) present results from such an experiment, with ultrafast Doppler images formed using ensemble lengths of 88, 44, 22, and 12 firings. Com-pared with the conventional Doppler method, the acqui-sition time is reduced respectively by a factor 2, 4, 8, and 14. Although the ultrafast Doppler image progres-sively degrades with the reduced number of acquisition frames, it remains better than conventional Doppler. Even

for the fastest sequence (corresponding to an ensemble length equal to 12) the ultrafast plane wave compounding outperforms the quality of the conventional method. It is important to note that the apparent change in the granu-larity between the fastest ultrafast compound image [Fig. 6(f)] and the focused image [Fig. 6(a)] comes from the fact that the conventional focused image is built gradually on a segment-by-segment basis (16 segments, each containing 8 lines). Thus, the spatio-temporal continuity is not ensured from one segment to the next, whereas the whole image is acquired simultaneously in the compound approach.

B. Low-Velocity Flow Optimization

For clinical applications where low-velocity flow must be detected in small vessels, the image contrast becomes a key parameter. Here, the number of angles used to com-pute a full color image is increased to 16 to obtain con-trast performance similar to the conventional approach (see Fig. 3) and a higher SNR (see Fig. 4). The spatial resolution is also explicitly increased by choosing large maximum tilting angles (±12° instead of ±9°). Both meth-ods (conventional and ultrafast compound) are evaluated on the thyroid of a healthy volunteer and presented in Fig. 7.

The ultrafast compound image exhibits higher flow sen-sitivity and less variance (14 times less) than the focused one; for example, small vessels deep in thyroid are clearly resolved, whereas they remain very di1cult to detect in the focused image. This is demonstrated in Figs. 7(c)–(f), where the Doppler intensity is plotted along two horizon-tal lines (2- and 2.2-cm depth) for both methods. Peaks of Doppler signals are clearly present on the ultrafast com-

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Fig. 6. Focused color Doppler image (a) and compound color Doppler (b) using the same acquisition time. The compound image has a very low variance and is of much better quality than the focused one. (c) to (f) are computed by reducing the number of frames to calculate the ultrafast Doppler image, the acquisition time is accelerated by a factor of 2 to 14. The image accelerated 14 times has a similar quality than the standard focused image. No spatial or averaging filter is used in all these images.

Fig. 7. Thyroid scans using (a) ultrafast compound Doppler imaging and (b) focused. Horizontal lines of the Doppler images are shown on (c) and (d) for the ultrafast compound Doppler image and (e) and (f) for the focused image.

pound image, whereas the same peaks are very close to the noise background on the focused one.

The ultrafast compound method o0ers much better flow detection and higher sensitivity and resolution than the conventional method. Moreover, the penetration is signifi-cantly higher in the ultrafast compound method, mainly because of a better SNR at greater depths.

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Standard color Doppler is limited to frame rates of up to 20 to 30 Hz. At such frame rates, many fast transient phenomena such as turbulence or short duration flow re-versals are invisible. Therefore, color flow imaging could tremendously benefit from higher acquisition rates.

Because the acquisition time of the ultrafast compound Doppler images is significantly shorter than for the con-ventional method, sequences can be designed to increase the temporal sampling rate of the Doppler data. In this section, we investigate the potential of the ultrafast com-pound Doppler method to acquire Doppler data at high spatial and temporal sampling rates.

A. Ultrafast Doppler

In this particular acquisition mode, Doppler data over the full ROI is acquired at high repetition frequency (typi-cally the PRF used in pulsed-wave Doppler mode) dur-ing a complete cardiac cycle. The highly sampled Doppler data are then stored into memory and are available for multiple parallel processing and analysis schemes in a ret-

rospective manner. To assess the performance of the new acquisition scheme, experiments were performed on the carotid of a healthy volunteer.

The implemented sequence is an ultrafast sequence comprising 3 angles (73°, 0 °, +3°) and an acquisition PRF between angles equal to 20 kHz. The acquisition contains gaps between sets of compounded images to obtain a flow acquisition frame rate of 3 kHz, instead of the maximum flow PRF of 6.66 kHz (20 kHz/3 angles). Doppler data are acquired at this frame rate during a complete cardiac cycle (3000 images in total).

Fig. 8(a) shows an example of the raw in-phase/quadra-ture-phase (IQ) signal (The IQ signal corresponds to the beamformed and demodulated ultrasound signal) at one given location inside the arterial blood flow. A wall filter is applied to this raw signal to extract the blood flow sig-nal, which is displayed on Fig. 8(b). The mean Doppler frequency is then calculated using 15 samples in a slid-ing window (with a 10-sample step between adjacent win-dows). The resulting Doppler frequency signal is shown in Fig. 8(c) and has a temporal resolution of 150 Hz, which is significantly higher than for the standard focused color Doppler imaging (typically 10 to 15 Hz).

The complete movie comprises as much as 300 images. Fig. 9 presents some interesting frames from the acquisi-tion. To identify the temporal location within the cardiac cycle, Fig. 9(a) shows the mean Doppler frequency of the flow in the artery. In Fig. 9(b), the flow velocity is at its minimal value. In Figs. 9(c) and 9(d), the aortic valve opens and the flow accelerates. Between time steps (d) and (f), the flow dynamics become more complex and the Reynolds number can reach the critical value where turbulence can appear [22]. In Figs. 9(e.1) to (e.5), a sequence of 5 frames shows the rapid inversion of the parabolic laminar flow into a profile where the speed is temporarily almost zero in the center of the artery. In Fig. 9(f), local turbulent phenomena start to develop. The spatio-temporal trajectory and evolution of this lo-cally turbulent flow can be followed during a few frames. Finally, in Figs. 9(g) and 9(h), the profile becomes lami-nar again.

The two cineloops corresponding to Figs. 9(e.1) to (e.5) and Figs. 9 (f.1) to (f.5) have a total duration of 40 ms, which is the required time to perform only a single Dop-pler image in conventional color flow. This shows that such temporal close-ups as the ones allowed by the ultrafast compound imaging method could be of great interest to fully analyze complex flow dynamics in arteries.

B. Full Spectra Analysis

The continuous acquisition of ultrafast compound im-aging over a 2-D area of interest o0ers additional possi-bilities for advanced flow quantification. For example, the acquired data are perfectly suitable for generating full-spectral analysis Doppler sonograms as those obtained by the standard PW Doppler mode simultaneously for each pixel of the 2-D color flow image. Therefore, we can per-

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Fig. 8. Example of the signals in an ultrafast acquisition. (a) Signal I at a selected point in the artery in arbitrary units. (b) After applying the wall filter, the raw signal corresponding to blood flow is extracted. (c) The mean Doppler frequency is calculated using a 15-point sliding window for Fourier analysis.

form retrospective full-spectral analysis at arbitrary mul-tiple points throughout the whole area of interest, unlike conventional PW Doppler which is restricted to a given location. The obtained Doppler spectra for each desired pixel exhibit perfect time alignment, because they are based on data acquired at the same time. Fig. 10 shows two such Doppler spectra, obtained from sample volumes denoted as b and c in the ultrafast Doppler image. Note that the vertical elongation which is visible in some parts of the spectra in Fig. 10 is a direct consequence of ap-plying a rectangular windowing function to the Doppler time sequence before the fast Fourier transform. Also note

that the acquisition parameters corresponding to the in-put data of this figure are the same as those specified in the previous paragraph.

By o0ering the possibility of performing full spectral analysis throughout the color image, plane wave com-pound sequences enable generation of Doppler sonograms of higher dimensionality (Doppler frequency + time + depth + lateral position) which can be exploited in a va-riety of ways. For example, Fig. 11 shows a new type of flow analysis by defining longitudinal and transverse lines within the ultrafast color flow image, and generating Dop-pler spectrum sonograms versus spatial position at mul-

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Fig. 9. Some selected frames of a complete cardiac cycle obtained with the ultrafast compound. (a) Average flow in the artery indicating the selected frames. (b) Before the opening of the aortic valve, there is a minimal laminar flow. (c) and (d) Acceleration of the flow. (e) Inversion of the parabolic profile in the deceleration. (f) Local turbulence is present and propagates in the artery. (g) and (h) Laminar flows in diastole.

tiple times. Negative flows are clearly quantified at the time of the turbulence, Fig. 11(b).

This kind of spatial analysis could eventually be done by allowing a user to manually draw a line to analyze a particular trajectory of the flow. Thanks to the ultrafast plane wave compounding technique, automation tools can

also be envisioned to improve the e1ciency of the Doppler exam workflow. For example, the location of maximum peak velocity over the cardiac cycle could be automatical-ly detected and the full Doppler spectrum corresponding to this specific location can be calculated and displayed. More generally, this new Doppler sequencing opens the possibility of complete o=ine quantitative analysis of the blood flow from a single ultrafast compound acquisition.

VI. D*/)(//*"$

This paper introduces a new approach of Doppler blood flow imaging that significantly outperforms conventional Doppler imaging. This approach is based on the successive transmissions of compounded plane waves with di0erent tilting angles. The backscattered echoes corresponding to these compounded plane waves transmissions are recom-bined coherently to resynthesize ultrasonic images that ex-hibit excellent contrast and highly improved frame rates. Thanks to plane wave insonifications, each pixel of the imaged area can be formed using many more time samples than those used in conventional Doppler imaging.

As an immediate consequence, several key improve-ments can be obtained relative to conventional Doppler imaging. First of all, the acquisition time can be great-ly reduced, typically by a factor of up to 16 (with the 9-angle sequence). Therefore, plane wave compounding is extremely convenient for fast-flow imaging (arteries, large veins) where transitory and turbulent flows could be imaged with a much better temporal resolution. We

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Fig. 10. (a) Two sample volumes plus (b) and (c) the corresponding spectrograms using IQ data acquired at the same time.

Fig. 11. Doppler frequency versus spatial position spectrograms at three di0erent times (a), (b), and (c). Spatial analysis is done longitudinally (x) and transversely (z) through the common carotid artery.

demonstrated higher-quality flow estimation for fast-speed flows, as shown in Section IV, without the need for post-processing operators (spatial smoothing, temporal smoothing, etc.) which are typically used in conventional Doppler flow imaging to improve the quality of the raw flow estimates. On the other hand, the technique is also extremely interesting for slow-flow imaging, where resolu-tion, sensitivity, and contrast are important. In that case, the angle range and the number of angles are increased (from 9 to 16 as illustrated in Section IV) at the expense of a lower gain in acquisition time. Flow detection and definition can be strongly improved for low-speed flows, as shown in Fig. 7: because each pixel is derived from many more time samples, much better sensitivity is enabled. Ul-trafast compound Doppler imaging could provide a way to image very-low-speed flows in small vessels; for example, for evaluating tumor recurrence after chemotherapy or ra-diotherapy treatments (prostate or breast cancer) [23].

Complex flows are di1cult to analyze because of the inability of conventional approaches to obtain Doppler spectra simultaneously at several locations. To partly overcome these limits, Tortoli et al. proposed multigat-ed Doppler spectrum analysis to increase the amount of quantitative information available compared with conven-tional Doppler [24], [25]. A highly-relevant feature of ul-trafast Doppler in this context is its ability to fully over-come the fundamental limitation of conventional Doppler, which is able to assess blood flow simultaneously only in a very limited area of interest (this refers both to color flow imaging and PW color modes). In ultrafast Doppler, blood flow can be estimated simultaneously at each pixel location in a wide 2-D region of interest. Because blood flow is evaluated simultaneously for all pixels, it enables a much better understanding of complex flows. Assessing the full spatial and temporal distribution of flow enables, for example, tracking pf turbulent flows, or the study of viscosity or Reynold’s number thanks to the dynamics of velocity distribution profiles in the artery. It also enables the analysis of complex flow trajectories and dynamics because the complete Doppler spectrum becomes available for each pixel. One should notice here that the maximum detectable frequency/velocity is NAngles times lower than in classic PW single mode. NAngles should therefore be set as low as possible for fast-flow analysis. The concept of plane wave compounding can also easily be extended to transverse Doppler measurements by using independent sub-apertures of the array [26].

Finally, plane wave compounding provides a much better use of the color and PW Doppler modes, because they are fully integrated within the same real-time mode. Perhaps more importantly, this new concept of ultrasonic sequences also provides the possibility of obtaining quanti-fiable Doppler spectra at all image locations in a retrospec-tive manner, by using previously stored ultrafast Doppler data. More generally, ultrafast compound Doppler imaging opens up the possibility of exploring advanced flow imag-ing and quantification techniques by taking advantage of the simultaneous acquisition of Doppler information over

a large region of interest with excellent spatio-temporal continuity.

Of course, the possibility of implementing such a mode in real time on an ultrasound system is also a real chal-lenge, because it requires a complete redefinition of the ul-trasonic system architecture. The two main requirements are:

Highly parallelized acquisition and processing ca-pabilities to perform ultrafast imaging sequences, i.e., imaging of the medium at ultrafast frame rates (>1000 Hz),Very high processing performance to be able to gener-ate ultrafast color images in real time. Overall, pro-cessing capabilities of the ultrasound device need to be increased at least by a factor of NAngles compared with conventional techniques (each pixel is beam-formed NAngles times) to provide real-time Doppler information.

The requirements of such an ultrasonic platform can only be provided by a highly flexible software architecture, in which the system is able to acquire and process ul-trasound images at ultrafast frame rates. A first clinical system based on a fully software-based architecture was developed in the framework of shear wave imaging (Aix-plorer ultrasound system, SuperSonic Imagine). Ultrafast data are stored in a digital memory and transferred at 3.5 Gbytes/s via a PCI express link to a software-based processing block that leverages GPU processing power (1 CPU with 6 cores, 3.33 GHz sampling, and 1 GPU board). All image lines can therefore be processed in parallel, en-abling generation of a few thousands of ultrasound images per second. The Aixplorer system is currently leveraged for integrating real-time ultrafast Doppler imaging. Tech-nical and clinical performances will be presented in a fu-ture work.

VII. C"$)#(/*"$

By moving beyond the concept of conventional ultrason-ic imaging acquisitions, ultrafast plane wave compounding enables revision of Doppler imaging and paves the way to new perspectives in Doppler flow analysis. First, a signifi-cant gain in acquisition time (up to 16 times faster) can be achieved while keeping the Doppler mode performance similar to today standards, o0ering significant frame rate improvements and the opportunity for improved visualiza-tion and advanced analysis of transient flow phenomena such as turbulence and jets. Second, the ultrafast Doppler sequence can be optimized so that it o0ers comparable frame rates to conventional Doppler flow imaging but with much longer ensembles, to enhance flow imaging perfor-mance by increasing resolution, sensitivity, and introduc-ing very fine tissue/flow discrimination. Finally, ultrafast Doppler imaging can be used to acquire Doppler informa-tion at very high spatial and temporal sampling rates,

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allowing full spectral analysis on a large 2-D ROI in real-time as well as using previously stored data. Therefore this approach has the potential to strongly improve all aspects of currently used flow imaging and analysis appli-cations, as well as to expand the clinical applications and diagnostic capabilities of Doppler ultrasound far beyond what is currently available.

R,5,&,$),/

[1] D. H. Evans, W. N. McDicken, R. Skidmore, and J. P. Woodcock, Doppler Ultrasound, Physics, Instrumentation, and Clinical Applica-tions. New York, NY: Wiley, 1989.

[2] J. A. Jensen, Estimation of Blood Velocities Using Ultrasound: A Signal Processing Approach. New York, NY: Cambridge University Press, 1996.

[3] L. Y. L. Mo, T. L. Ji, C. H. Chou, D. Napolitano, G. W. McLaugh-lin, and D. DeBusschere, “Zone-based color flow imaging,” in Proc. IEEE Ultrasonics Symp., 2003, pp. 29–32.

[4] N. Oddershede, F. Gran, and J. A. Jensen, “Multi-frequency encod-ing for fast color flow or quadroplex imaging,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 55, no. 4, pp. 778–786, Apr. 2008.

[5] T. X. Misaridis and J. A Jensen “Space-time encoding for high frame rate ultrasound imaging,” Ultrasonics, vol. 40, no. 1–8, pp. 593–597, May 2002.

[6] K. L. Gammelmark and J. A. Jensen, “Multielement synthetic transmit aperture imaging using temporal encoding,” IEEE Trans. Med. Imaging, vol. 22, no. 4, pp. 552–563, 2003.

[7] L. Germont-Rouet, T. Loupas, and O. Bonnefous, “Clutter filtering with small ensemble length in ultrasound imaging,” U.S. Patent ap-plication, US 2007112269, May 17, 2007.

[8] D. N. Roundhill, T. Loupas, A. Criton, and D. Rust, “Coincident tissue and motion ultrasonic diagnostic imaging,” U.S. Patent 6 139 501, Dec. 14, 2000.

[9] T. K. Holfort, F. Gran, and J. A. Jensen, “Minimum variance beam-forming for high frame-rate ultrasound imaging,” in Proc. IEEE Ultrasonics Symp., 2007, pp. 1541–1544.

[10] P. Tortoli, L. Bassi, E. Boni, A. Dallai, F. Guidi, and S. Ricci, “ULA-OP: An advanced open platform for ultrasound research,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 56, no. 10, pp. 2207–2216, 2009.

[11] M. Tanter, J. Berco0, L. Sandrin, and M. Fink, “Ultrafast com-pound imaging for 2-D motion vector estimation: Application to transient elastography,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 49, no. 10, pp. 1363–1374, 2002.

[12] J. Berco0, M. Tanter, and M. Fink, “Supersonic shear imaging: A new technique for soft tissues elasticity mapping,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 51, no. 4, pp. 396–409, Apr. 2004.

[13] J. Udesen, F. Gran, K. Lindskov Hansen, and J. A. Jensen, “High frame-rate blood vector velocity imaging using plane waves: simula-tions and preliminary experiments,” IEEE Trans. Ultrason. Ferro-electr. Freq. Control, vol. 55, no. 8, pp. 1729–1743, Aug. 2008.

[14] J. Y. Lu, “2D and 3D high frame rate imaging with limited di0rac-tion beams,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 44, no. 4, pp. 839–855, 1997.

[15] G. Montaldo, M. Tanter, J. Berco0, N. Benech, and M. Fink, “Coherent plane-wave compounding for very high frame rate ul-trasonography and transient elastography,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 56, no. 3, pp. 489–506, Mar. 2009.

[16] D. Shattuck, M. Weinshenker, S. Smith, and O. Von Ramm, “Ex-plososcan: A parallel processing technique for high speed ultrasound imaging with linear phased arrays,” J. Acoust. Soc. Am., vol. 75, no. 4, pp. 1273–1282, Apr. 1984.

[17] S. Park, S. R. Aglyamov, and S. Y. Emelianov, “Elasticity imaging using conventional and high-frame rate,” IEEE Trans. Ultrason. Fer-roelectr. Freq. Control, vol. 54, no. 11, pp. 2246–2256, 2007.

[18] C. Kasai, K. Namekawa, A. Koyano, and R. Omoto, “Real-time two-dimensional blood flow imaging using an autocorrelation technique,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 32, no. 3, pp. 458–463, 1985.

[19] O. Bonnefous and P. Pesque, “Time domain formulation of pulse-Doppler ultrasound and blood velocity estimation by cross-correla-tion,” Ultrason. Imaging, vol. 8, no. 2, pp. 73–85, 1986.

[20] T. Loupas, J. T. Powers, and R. W. Gill, “An axial velocity estima-tor for ultrasound blood flow imaging, based on a full evaluation of the Doppler equation by means of a two-dimensional autocorrela-tion approach,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 42, no. 4, pp. 672–688, 1995.

[21] Particular Requirements for the Safety of Ultrasound medical Di-agnostic and Monitoring Equipment, international standard IEC 60601-2-37 2nd ed., 2007.

[22] Y. C. Fung, Biomechanics, Circulation, 2nd ed., New York, NY: Springer Science, 1997, pp. 136–138.

[23] O. Rouviere, T. Vitry, and D. Lyonnet, “Imaging of prostate cancer local recurrences: Why and how?” Eur. Radiol., vol. 20, no. 5, pp. 1254–1266, May 2010.

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[25] W. Secomski, A. Nowicki, P. Tortoli, and R. Olszewski, “Multigate Doppler measurements of ultrasonic attenuation and blood hemat-ocrit in human arteries,” Ultrasound Med. Biol., vol. 35, no. 2, pp. 230–236, Feb 2009.

[26] R. Daigle, L. Pflugrath, J. Flynn, K. Linkhart, and P. Kaczkowski, “High frame rate quantitative Doppler imaging,” presented at IEEE Ultrasonics Symp., Rome, Italy, 2009.

Jeremy Berco! was born 1977 in Paris, France. In 2001, he received an engineering degree from the Ecole Supérieure de Physique et de Chimie de Par-is (ESPCI, ParisTech) with a specialization in Physics. In 2004, Jeremy received a Ph.D. degree in physics (acoustics) from the University of Paris VII for his work on ultrafast imaging and shear-wave-based elastography in soft tissues for cancer detec-tion. In 2005, he co-founded SuperSonic Imagine, a French medical ultrasound imaging and therapy company in Aix en Provence, for which he led the

introduction in 2007 of a new real time elasticity imaging mode (shear wave elastography) on the company’ first marketed product. He is the author of 8 patents and more than 15 peer reviewed papers in the field of medical imaging. His current research activities include ultrafast imaging, new ultrasound beam-forming strategies, and functional ultrasound imag-ing, such as color Doppler and elasticity imaging.

Gabriel Montaldo was born 1972 in Montevi-deo, Uruguay. He received the Ph.D. degree in physics from the Universidad de la Republica, Uruguay, in 2001. Since 2002, he has worked in the Laboratory Ondes et Acoustique at the Ecole Supérieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI). His principal re-search includes applications of the time-reversal process to adaptive focusing in heterogeneous me-dia, biomedical applications of ultrasound, and elastography. He has published more than 30 re-viewed articles in the ultrasound domain.

Thanasis Loupas received the B.E. degree in electrical engineering from the National Technical University of Athens, Greece, in 1983. He was a post-graduate research student at the National Research Center “Democritos” in Athens, Greece (1983–1984), and then moved to the Department of Medical Physics and Medical Engineering, Uni-versity of Edinburgh, Scotland, as a Ph.D. student working on adaptive image processing algorithms for speckle reduction in medical ultrasonic imag-ing. After receiving the Ph.D. degree in 1988 from

the University of Edinburgh, he became a Research Fellow at the same university working on real-time image processing and Doppler signal processing techniques. In 1992, he joined the Ultrasonics Laboratory of the Commonwealth Scientific Industrial & Research Organization, Syd-

146 IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL, !"#. 58, $". 1, JANUARY 2011

ney Australia, where he worked as a Research Scientist, Senior and Prin-cipal Research Scientist in novel flow and tissue-motion estimation tech-niques, and quantitative analysis of cardiac and obstetrical ultrasound data. In 1996, he became a member of the Corporate Technical Sta0 at Advanced Technology Laboratories, Bothell, WA, (subsequently acquired by and integrated within Philips Medical Systems) where he worked on a variety of R&D projects in the areas of Color/PW Doppler system architecture and processing, as well as algorithms for computer-assisted quantification and system automation, implemented in the HDI5000 and iU22 ultrasound systems and the QLAB quantification software. He joined SuperSonic Imagine, Aix-en-Provence, France as a Principal Sci-entist in 2007, where he continues to work in the design and development of signal and image processing algorithms for all imaging modes of the Aixplorer ultrasound system, plus automatic optimization and quantifi-cation techniques.

David Savéry earned the M.S. degree in 1998 from Mines Paris Tech, France, majoring in image analysis and image processing.He then joined the Laboratory of Biorheology and Medical Ultrason-ics, University of Montréal Hospital, Montréal, PQ, where he received the Ph.D. degree in bio-medical engineering in 2003. From 2004 through 2005, Dr. Savéry was a postdoctoral fellow and then senior member of the research sta0 at Philips Research North America, Briarcli0 Manor, NY. David Savéry is currently R&D ultrasound engi-

neer at SuperSonic Imagine, Aix-en-Provence, France. He has contrib-uted to the development of the Aixplorer ultrasound system since 2006. His research interests are in statistical signal processing, medical imag-ing, ultrasound tissue characterization, and biomechanics.

Fabien Meziere was born in December 1987 in Bernay, France. In 2007 he entered the Ecole Su-périeure de Physique et de Chimie Industrielle de Paris (ESPCI ParisTech) and in 2011 obtained an engineer degree with a specialization in physics. In 2009, he worked at SuperSonic Imagine on ultra-fast compound Doppler imaging. He is now achiev-ing his master’s degree in acoustics at the Univer-sity of Paris VII.

Mathias A. Fink received the M.S. degree in mathematics from Paris University, France, in 1967, and the Ph.D. degree in solid-state physics in 1970. Then he moved to medical imaging and received the Doctorat es-Sciences degree in 1978 from Paris University. His Doctorat es-Sciences research was in the area of ultrasonic focusing with transducer arrays for real-time medical imag-ing.

Dr. Fink is a professor of physics at the Ecole Superieure de Physique et de Chimie Industrielles

de la Ville de Paris (ESPCI), Paris, France, and at Paris 7 University (Denis Diderot), France. In 1990, he founded the Laboratory Ondes et Acoustique at ESPCI. In 2002, he was elected to the French Academy of Engineering, in 2003 to the French Academy of Science, and in 2008 to the Chair of Technological Innovation for the College de France.

His current research interests include medical ultrasonic imaging, ul-trasonic therapy; nondestructive testing; underwater acoustics; telecom-munications; seismology; active control of sound and vibration; analogies between optics, quantum mechanics, and acoustics; wave coherence in multiply scattering media; and time-reversal in physics.

He has developed di0erent techniques in acoustic imaging (transient elastography, supersonic shear imaging), wave focusing in inhomoge-neous media (time-reversal mirrors), speckle reduction, and in ultrasonic laser generation. He holds more than 50 patents, and he has published more than 300 articles. 4 start-up companies have been created from his research (Echosens, Sensitive Object, Supersonic Imagine, and Time Reversal Communications).

Mickaël Tanter is a Research Professor of the French National Institute for Health and Medical Research (INSERM). For five years, he has been heading the team Inserm ERL U979 “Wave Phys-ics for Medicine” at Langevin Institute, ESPCI ParisTech, France. In 1999, he was awarded the Ph.D. degree from Paris VII University in phys-ics.

His main activities are centered on the devel-opment of new approaches in wave physics for medical imaging and therapy. His current research

interests cover a wide range of topics: elastography using shear wave imaging, high intensity focused ultrasound, ultrasonic imaging using ul-trafast ultrasound scanners, adaptive beamforming, and the combination of ultrasound with optics and MRI. In 2009, he received the Frederic Lizzi Early Career Award of the International Society of Therapeutic Ultrasound and the Montgolfier Prize of the National Society for Indus-try Valorization (SEIN) in 2010. Mickael Tanter is the recipient of 17 patents in the field of ultrasound imaging and the author of more than 80 technical peer reviewed papers and book chapters. He is an Associate Editor and TPC member of IEEE Ultrasonics and member of the Brain Advisory board of the Focused Ultrasound Surgery Foundation. In 2005, he, along with M. Fink, J. Souquet, C. Cohen-Bacrie and J. Berco0 founded SuperSonic Imagine, an innovative French company positioned in the field of medical ultrasound imaging and therapy; in 2009 they launched a new-generation ultrasound imaging platform called Aixplorer with a unique shear wave imaging modality.

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