IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 62, NO. 8, AUGUST 2015 1969

Denoising of Contrast-Enhanced Ultrasound CineSequences Based on a Multiplicative ModelAvinoam David Bar-Zion∗, Charles Tremblay-Darveau, Melissa Yin, Dan Adam, Member, IEEE,

and F. Stuart Foster, Senior Member, IEEE

Abstract—Background: Speckle noise is an inherent characteris-tic of dynamic contrast-enhanced ultrasound (DCEUS) movies andultrasound images in general. Speckle noise considerably reducesthe quality of these images and limits their clinical use. Currently,temporal compounding and maximum intensity persistence (MIP)are among the most widely accepted processing methods enablingthe visualization of vasculature using DCEUS. Goal: A differentapproach has been used in this study, in order to improve the noiseremoval, while enabling the investigation of CEUS dynamics. Meth-ods: A multiplicative model for the formation of DCEUS speckledimages is adopted and the log-transformed cines are processed. Apreprocessing step was performed, locally removing low value out-liers. Due to the fast-changing spatial distribution of microbubblesinside the vasculature, the noise in consecutive DCEUS frames isindependent, facilitating its removal by temporal denoising. Noisereduction is efficiently achieved by wavelet denoising, in which thesignal’s wavelet coefficients are thresholded and small-value noise-related coefficients are discarded. The main advantage of usingwavelet denoising in the present context is its ability to estimateultrasound contrast agents’ (UCA) concentration over time adap-tively, without assuming a model or predefining the signal’s degreeof smoothness. The performance of wavelet denoising was com-pared against MIP, temporal compounding, and Log-normal modelfitting. Results: Phantom experiments showed improved SNR, us-ing wavelet denoising over a wide range of UCA concentrations(MicroMarker, 0.001–1%). In the in vivo tests, improved noise re-moval was achieved, reflected by a significantly lower coefficient ofvariation in homogeneous vascular regions (p < 0.01).

Index Terms—Contrast-enhanced ultrasound, denoising, multi-plicative noise, outlier-resistant estimation.

I. INTRODUCTION

I T is well known that blood–tissue contrast in ultrasoundscans can be improved by injecting the patient with ultra-

sound contrast agents (UCA) and using contrast-specific pulsesequences [1]. The increased echogenicity and typical radii

Manuscript received October 13, 2014; revised January 13, 2015; acceptedFebruary 14, 2015. Date of publication February 27, 2015; date of currentversion July 15, 2015. This work was supported by the Canadian Institutes ofHealth Research, the Terry Fox Foundation, the Ontario Research Fund, andVisualSonics Inc. Asterisk indicates corresponding author.

∗A. D. Bar-Zion is with Department of Biomedical Engineering,Technion—Israel Institute of Technology, Haifa 32000, Israel (e-mail:barz@tx.technion.ac.il).

C. Tremblay-Darveau is with the Department of Medical Biophysics, Uni-versity of Toronto.

M. Yin is with the Sunnybrook Health Sciences Center.D. Adam is with the Technion—Israel Institute of Technology.F. S. Foster is with the Sunnybrook Health Sciences Center, and also with the

University of Toronto.This paper has supplementary downloadable material available at http://

ieeexplore.ieee.org (File size: 7.17 MB).Digital Object Identifier 10.1109/TBME.2015.2407835

range makes UCAs ideal for the imaging of blood flow. Whenimaged at low intensities (mechanical index < 0.05), microbub-bles oscillate stably and circulate in the vasculature for longperiods of time, typically many minutes, before collapsing orbeing absorbed via endocytosis. The extended circulation ofcontrast agents inside the vasculature increases the importanceof the wash-out phase in scans incorporating a bolus injectionof UCA, and enables the use of targeted microbubbles thatbind to specific antibodies [2]. The use of DCEUS imagingfor the quantification of blood perfusion relies on the linearcorrelation between UCA concentration and the received har-monic ultrasound intensity. As presented in [3], with a suffi-cient dynamic range, this linearity holds over three orders ofdilution.

The ability to detect echoes of nonlinear bubble oscillationsand to suppress linear signals from tissue is essential for perfu-sion imaging. Several multipulse sequences were proposed forthat purpose [4]. In the pulse inversion technique, the polarity ofevery two consecutive pulses is altered, preserving the even har-monics. Alternatively, amplitude modulation can be used for theseparation of odd harmonics. A method that manipulates bothamplitudes and phases has been shown to improve UCA detec-tion sensitivity [4]. Using these multipulse sequences, UCA canbe imaged with sufficient contrast to tissue ratio.

Speckle noise is an inherent feature of DCEUS and of ultra-sound imaging in general, as it is a coherent imaging modality.This noise results from the presence of many small scattererswithin each resolution cell, defined by the −6 dB of the mainbeam profile in the axial, lateral, and out of plane orientations.The combination of these reflections creates complicated con-structive and destructive interference patterns in the echo signal,named speckle noise. Speckle noise further reduces the contrastand resolution of DCEUS and B-mode images and limits theirdiagnostic value.

Therefore, speckle reduction has been the focus of many stud-ies aiming to improve the quality of B-mode ultrasound imag-ing. Following the multiplicative model of speckle formationfirst presented in [5], the log transform can be used to convertthe multiplicative noise into additive noise. This additive noisecomponent can be removed using a variety of classical denoisingmethods, for example, wavelet denoising [6], [7]. The underly-ing assumption of this family of methods is that the resultinglog-compressed images contain an independent identically dis-tributed (i.i.d.) Gaussian noise. In [8], the authors emphasizedboth theoretically and experimentally that these assumptions aregenerally wrong for log-transformed B-mode images. They pre-sented a preprocessing step that reduced the spatial correlationof the noise and removed noise outliers. This step produced noise

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1970 IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 62, NO. 8, AUGUST 2015

that better resembled an i.i.d. Gaussian noise and significantlyimproved the performance of the speckle removal process.

Similar to the case of B-mode images, contrast-enhanced ul-trasound scans suffer from speckle noise. There are two maindifferences between the speckle patterns that characterize thesetwo imaging modes. First, due to the resonance phenomena,the signal received from UCA originates mainly from a sub-population of microbubbles with radii matching the excitationfrequency. The statistics of the signal received from these bub-bles depends on the number of relevant bubbles within eachresolution cell, at a given time. Second, the speckle patternin DCEUS images is influenced by the position of the contrastagents, and their movement with the blood flow over time, unlikespeckles in B-mode images. Moreover, considering typical val-ues of mean flow velocity in arterioles (5 mm/sec), frame rates(20 frames/s), and the dimension of the resolution cell (300 μmin the axial direction at 18 MHz, 3 cycles), one may assume thespatial distribution of contrast agents in consecutive frames tobe independent. Therefore, instead of attempting to remove thestructured noise in the spatial domain, the aim here is denoisingthe signal in the temporal domain—a much easier task.

The work of Sureshkumar [9] was among the first studiesto report on the statistics of CEUS envelope and intensity sig-nals. It was found that for high enough UCA concentrations,the signals follow Rayleigh statistics. For lower concentra-tions, the variability of the measurements increased, indicatingdeviation from the Rayleigh model. These findings are inagreement with the results presented in [10] and [11]. TheK-distribution was proposed as a model that can describe the sig-nal and its variability over a wide range of concentrations. Thismodel was not yet validated. Moreover, since this research wasperformed with first harmonic signals, additional study is neededto examine the validity of the K-distribution model for harmonicCEUS measurements acquired using multipulse sequences.

Maximum intensity persistence (MIP) is a widely acceptedprocessing method that enables the visualization of the vascu-lature from noisy DCEUS ultrasound scans. MIP operates bycalculating the maximum of the intensity signal over time, foreach pixel. By doing so, the vasculature morphology is revealed[12]. MIP has been successfully used in the delineation of bloodvessels, facilitating the classification of prostate tumors [13],focal hepatic lesions [12], and breast tumors [14]. In addition,few papers tried to evaluate perfusion parameters from MIPmovies (for example, peak enhancement in [15]). Nevertheless,MIP suffers from a few well-known limitations. First, the accu-mulated maximum intensity over time includes a considerablestructured noise and artifacts. In addition, since MIP is exclu-sively based on the highest intensity values, it is very sensitiveto motion and every small movement smears the image. Finally,the wash-out phase, which is characterized by decreasing inten-sity values, cannot be displayed in MIP cines. As a result, MIPis still used as a qualitative method and no MIP-based automatictool has found its way to the clinic.

Another possible approach to the removal of the speckle noisefrom DCEUS scans is temporal compounding. In this context,temporal compounding is defined as the application of a linearfilter (e.g., FIR filter or an averaging window) to the DCEUSdata in the time domain. For example, averaging of several 3-D

CEUS scans taken during the wash-in phase of a bolus injection,[16] can be considered a temporal compounding procedure. Anobvious limitation of this approach is that a priori assumptionof the signal’s degree of smoothness is needed in order to setthe cut-off frequency. Cut-off frequency selection is not trivialsince the signal and the noise can have overlapping supports inthe frequency domain.

Considering the mentioned limitations of current methods,the aim of this study was to improve the noise removal and en-hance the appearance of vascular structures, while enabling theinvestigation of local CEUS dynamics. The proposed approachadopts a multiplicative model for speckle formation in DCEUSimages. It is then shown that the enhancement of vascular struc-tures can be redefined as a denoising problem. The structureof log-transformed DCEUS signals and the statistics of theirinherent speckle noise are studied to determine an appropriatedenoising method. Since the speckle noise in DCEUS imagesis characterized by a heavy tailed distribution, a preprocessingprocedure is proposed for removal of the noise outliers, result-ing in a semi-Gaussian noise. A variety of classic denoisingalgorithms, optimized for Gaussian noise, can then be used.

This study focuses on the use of wavelet denoising for thespeckle noise rejection in DCEUS cines. The general procedurefor an additive noise removal, by applying a nonlinear thresh-old to the coefficients of the wavelet transform of the measuredsignals, has been developed by Donoho and Johnstone [17],[18]. The application of wavelet denoising to signals contam-inated by the non-Gaussian noise was explored in [19]. Themain advantage of using wavelet shrinkage in our context is itsability to estimate signals adaptively without assuming a modelor predefining the signal’s degree of smoothness.

II. MULTIPLICATIVE NOISE MODEL FOR CEUS MOVIES

The main objective of this study is to improve upon cur-rent methods of DCEUS signal processing by representing thisproblem in the framework of signal denoising. To do so, a modeldescribing DCEUS signals in both the spatial domain and thetime domain and an understanding of the statistics of the noiseare required.

A. Statistical Models of Speckle Noise in Contrast-EnhanceUltrasound Scans

Considering the polydispersed physical characteristics ofcommercial UCA microbubbles and the variable number ofmicrobubbles imaged, it is useful to adopt a statistical modelfor DCEUS signals. First-order statistics of B-mode images’envelope signal depends on the acoustic homogeneity of the tis-sue. Similarly, the statistics of CEUS signals primarily dependson the concentration of relevant microbubbles’ subpopulationswithin the resolution cell and the relative scattering from each ofthese bubbles. Based on the analogy between speckle formationin CEUS and B-mode imaging, models for speckle statistics inB-mode images are examined in this study, and their validity forCEUS signals is assessed.

In order to understand the statistics of dephased echoes, thespatial distribution of the microbubbles should be evaluated.Commonly, a uniform spatial distribution of microbubbles is

BAR-ZION et al.: DENOISING OF CONTRAST-ENHANCED ULTRASOUND CINE SEQUENCES BASED ON A MULTIPLICATIVE MODEL 1971

assumed [9]. For cases of high concentrations of scatterersdistributed uniformly, the envelope signal is known to followthe Rayleigh distribution [20]. The speckle amplitude SNR isan indicator of the speckle contrast, defined as

r =μ

σ=

m1√m2 − m2

1

(1)

where mν is the υ moment of the corresponding distribu-tion. Inserting the moments of the Rayleigh distribution, weget r = 1.91 [20]. This constant ratio between the signal (ex-pected value) and the noise (standard deviation) indicates thatthe speckle noise is multiplicative for high UCA concentrations.

For the cases where low doses of UCA are injected, or poorlyperfused tissues are scanned, the microbubble concentrationscan be quite low. Many different statistical distributions wereused to describe cases in which the concentration of scattererswas low, or they could not be assumed to be independentlydistributed. The K-distribution will be used in this study, asproposed in [9], for the modeling of CEUS signals over a largerange of concentrations

p(A|α,b) =2b

Γ (α)

(bA

2

)α

Kα−1 (bA) , A ≥ 0 (2)

where α is the shape parameter of the K-distribution, E{A2} isthe second moment of the amplitude, Γ is the gamma function,and the function Kx(·) is the modified Bessel function of thesecond kind with order x. Parameter b = 2

√α/E{A2} serves

as a scale parameter. In [21], parameter α was shown to beassociated with the concentration of effective reflectors

α = (μ + 1) Ns, μ > −1 (3)

where Ns is the number of scatterers and μ is a parameter thatdepends on the geometry of the problem.

Convergence to the Rayleigh approximation can be assessedaccording to the amplitude SNR defined in (1). Following [22],the K-distribution moments are given by

mυ = E {Aυ} =(2σ2)υ/2 Γ

(1 + υ

2

)Γ

(α + υ

2

)

αυ/2Γ (α). (4)

Inserting (4) to (1), one obtains the amplitude SNR for theK-distribution

r =1

√(α · Γ (α)) /

(Γ

(1 + 1

2

)· Γ

(α + 1

2

))− 1

. (5)

It should be noted that according to (4), the expected valueof the intensity signal m2 is equal to 2σ2 . Thus, the linear cor-respondence between the ultrasound mean intensity and UCAconcentration, even at low concentrations, can be explained bythe K-distribution model, since σ2 is proportional to the numberof scatterers in the resolution cell [23].

B. Model for Speckled CEUS Images

There is no unanimously accepted model for the formationof speckled CEUS images; therefore, the multiplicative specklenoise model, widely used for the denoising of B-mode images,was adopted here. A general multiplicative model for a specklenoise was proposed in [5]

g [n,m] = f [n,m] u [n,m] + ξ [n,m] (6)

where g and f are the measured and the original image, respec-tively. The multiplicative component of the noise is given by u,ξ represents the additive noise component. Here, n and m are theradial and angular coordinates, respectively, for a phased-arraytransducer, or axial and lateral coordinates for a linear trans-ducer. The multiplicative component of the noise relates to thephysics of coherent imaging, the additive noise component rep-resents electrical measurement noise and phenomena not fullydescribed by the model.

In [8], it was claimed that compared to the stronger speckle-related multiplicative noise component, the additive noise inB-mode ultrasound images is negligible. The same strategy isused here in the context of DCEUS scans despite the lower SNRof the harmonic signal. Applying the log transform to (6), themultiplicative speckle noise u is converted into an additive term.Defining g, f , and u as the logarithms of g, f, and u, respectively,one obtains

g [n,m, l] = f [n,m, l] + u [n,m, l] (7)

where l is used as the frame index. The resulting additive noiseu has very different correlation properties in the spatial andtemporal domains. In the time domain, noise in consecutiveframes is expected to be independent due to the movement of gasbubbles over time. In the spatial domain, the log-transformednoise was proven to have a significant structure [8]. Conse-quently, temporal denoising of DCEUS cines is easier to im-plement, without the need for preliminary decorrelation of thesignal.

In the search for a suitable denoising algorithm, the first-orderstatistics of the log-transformed noise should be considered. Forhigh enough concentrations of UCA, and assuming the ampli-tude of CEUS signals follows the Rayleigh distribution, thelog-transformed signal is known to obey the Fisher–Tippet dis-tribution [24]

pg (x) = 2 exp([

2x − ln 2σ2] − exp(2x − ln 2σ2)) . (8)

This distribution has a double exponent formation. It wasshown in [24] that if the first three terms of the Taylor seriesof the second exponential is inserted into (8), a Gaussian ap-proximation for (8) is obtained. This result is helpful since itpresents a Gaussian approximation to the Fisher–Tippet distri-bution, with a constant variance var{g} = 0.25 and an expectedvalue μ = ln(

√2σ), which is a function of the scatterers’ con-

centration. This independence between the values of the vari-ance and the expected value enables the representation of g as asum of f , a signal representing the UCA’s concentration, and anoise u, with constant variance and μ = 0. This approximationis fairly accurate when x is close to its expected value μ, whileat the tails of the Fisher–Tippet distribution the approximationerror increases considerably [24]. Due to the complexity of theK-distribution, a similar analytical approximation could not bederived for low UCA concentrations. However, after log trans-form, signals obeying the K-distribution display a heavy tailsimilar to those characterizing the Fisher–Tippet distribution. Inaccordance, the denoising procedure was developed to suit theFisher–Tippet distribution. The denoising procedure was latervalidated and shown to be effective (see below) over a wide rangeof UCA concentrations including low UCA concentrations

1972 IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 62, NO. 8, AUGUST 2015

for which the noise does not fit the Fisher–Tippet model andcan be described using the K-distribution.

C. Dynamics of CEUS Cines

Since this study focuses on DCEUS data processing in thetime domain, a model for the dynamics of CEUS cines mustbe derived. The degree to which this model can account for theunderlying physical phenomena will have a direct effect on thereliability and accuracy of the results.

In most quantitative DCEUS studies, an indicator-dilutionmodel is fitted to the CEUS signal over time, facilitatingestimation of physiological meaningful parameters from thetime-intensity curve (TIC). A review of five indicator-dilutionmodels and their properties can be found in [25]. The two mod-els that best fit the examined in vivo data were the log-normaland diffusion-with-drift models. In order to increase SNR andfacilitate the parameter estimation process, the signal is typicallyfirst averaged over a predefined spatial region of interest (ROI)around each pixel. However, both the initial spatial averagingand the use of a global temporal model limit the resolution of theanalysis, and can potentially mask important local information,in both time and space. In addition, although indicator-dilutionmodels were shown to fit DCEUS signals measured for homoge-neous, well perfused areas, they only match the first pass of theUCA and, thus, are problematic as a mean for signal denoising.

The log-normal model is the most widely used model forDCEUS data analysis. The log-normal distribution function isgiven by

I (t) =AUC√

2πσ (t − t0)e([ln(t−t0 )−μ ]2 /(2σ 2 )) + I0 , t > t0

(9)where AUC is the area under curve of the fitted model, t0 isthe time delay before the beginning of the wash-in phase, I0is the intensity offset. On a logarithmic time scale, μ and σare the location and scale parameters, respectively. Due to itspopularity, this model is an important reference used for theevaluation of new DCEUS processing methods.

The diffusion-with-drift model is based on a fluid-dynamicinterpretation of the indicator-dilution process. The combinationof diffusive and convective transport is described by

∂C (x, t)∂t

= D∂2C (x, t)

∂x2 − v∂C (x, t)

∂x(10)

where C(x, t) is the indicator’s concentration at a given timet and location x. Here, v is the blood velocity and D is the ef-fective diffusion constant. In the case of constant velocity, thediffusion term results in the indicator’s concentration becomingincreasingly smooth over time as the UCA bolus flow throughthe vasculature. This assumption holds for small systemic ves-sels and for the microvasculature. However, pulsatile flowin large vessels can result in sudden indicator concentrationchanges. In addition, tumor vasculature is highly heterogeneousand irregular, resulting in extremely long circulation times;thus, making the distinction between the first passage of UCAsand following recirculation difficult. Since a piecewise-smoothmodel for UCA concentration can cope with all mentioned phe-nomena, it will be used in this study. Specifically, with this

model UCA concentration signals are expected to be smooth,but can include isolated sharp transitions.

III. DENOISING OF DCEUS CINE SEQUENCES

A. Linear Versus Nonlinear Filtering

As the processing of the DCEUS measurements is performedfor each pixel separately, the spatial coordinates will be usedhere as indices. For reasons of clarity, these indices will beomitted for the rest of this section and the notation g[l] = f [l] +u[l] will be used, replacing the notation used in (7).

Temporal compounding is equivalent to the application of aspecific FIR filter. The definition of this filter depends on thenumber of temporal samples being compounded

f [l] =1M

(M −1)/2∑

i=−(M −1)/2

g [l − i]

=N −1∑

k=0

G [k] H [k] · exp(

i2πkl

N

)(11)

where M is the number of samples of the moving window, f isthe estimation of f , G[k] is the Fourier transform of g[l]. TheFourier transform of the rectangular moving average filter isgiven by H[k]. Different finite-impulse response filters may bedesigned according to the desired tradeoff between the cut-offfrequency, roll-off, ripple level, and the order of the filter. Theapplication of a linear filter requires the setting of a single cut-off frequency separating the slow-changing UCA concentrationdynamics and the wide-band noise. The value of this cut-offfrequency is assumed to be valid for the whole dataset andshould be set a priori. In practice, such a priori knowledge ofthe cut-off frequency is generally unavailable.

Linear estimation of the temporal UCA concentration signalmay be performed on the signal’s representation in any givenbasis. Given, a basis ϕ a general linear estimation of the con-centration signal is described by

f =N −1∑

k=0

a [k] · 〈g, ϕ [k]〉 · ϕ [k] (12)

where ϕ is the biorthogonal basis. Here again, the values of thecoefficients a[k] have to be determined in advance. Piecewise-smooth signals are known to have sparse representations inwavelet bases. Since the smoothness level of DCEUS signalsis generally unknown and sharp transitions can be found inmany cases during the wash-in phase, a wavelet basis is usedhere for the representation of DCEUS signals in the temporaldomain. The sparse representation of UCA concentration curvesin wavelet bases is used here as a prior for signal denoising.

Wavelet denoising by soft thresholding is a nonlinear esti-mation method [17], which filters out the noise by adaptivelyselecting the relevant subset of the basis according to their rel-ative value. Following [26], the formulation of this denoisingoperation is given by

f =N −1∑

k=0

ak (〈g, ϕ [k]〉) · 〈g, ϕ [k]〉 · ϕ [k]. (13)

BAR-ZION et al.: DENOISING OF CONTRAST-ENHANCED ULTRASOUND CINE SEQUENCES BASED ON A MULTIPLICATIVE MODEL 1973

When given the threshold T, the soft attenuation rule isdefined by

0 ≤ ak (x) = max(

1 − T

|x| , 0)

≤ 1. (14)

The threshold proposed in [17] was the universal thresholdrule T = σ

√(2ln(N)), where N is the length of the signal

and σ is estimated as the median of the absolute value of thewavelet coefficients in the first decomposition level. Althoughlower thresholds were presented, for example, by [18], the uni-versal threshold rule was used in this study, due to its robustnessto high levels of noise. When this value of T is selected, thewavelet thresholding algorithm produces a nearly minimax riskfor piecewise regular signals, which means that this estimator isclose to the optimal mean-square error for every signal in thisclass [17].

The overall scheme proposed in [17] can be summarized bythe three-step wavelet shrinkage algorithm. First, the empiri-cal wavelet coefficients are calculated from the measurements,using the wavelet transform. Then, the soft threshold rule is ap-plied to the wavelet coefficients, decreasing the amplitudes ofall coefficients and excluding those associated with the noise.Finally, the inverse wavelet transform is applied, producing anestimation of the clean DCEUS signal. Even though this thresh-old rule excludes coefficients produced by the Gaussian noisewith high probability, it is very sensitive to noise outliers. Theoutlier-resistant wavelet denoising algorithm and the consider-ation for selecting a specific wavelet basis are described below.

B. Outlier Resistant Wavelet Denoising

The wavelet denoising algorithm presented in [17] and [18]was optimized for the removal of the Gaussian white noise.While the noise in consecutive DCEUS frames can be assumedto be independent, its distribution is heavy tailed as described inprevious sections. The three-step wavelet denoising algorithm issensitive to noise outliers, since their projections on the waveletbasis produce large coefficients that are preserved as featuresof the signal. The outlier resistant wavelet denoising schemecopes with the non-Gaussian noise distributions by introducingan outlier detection step and removing these outliers from thesignal before each step of the wavelet decomposition [19].

Following the notations of [24], for every level of decom-position (k), a smooth version of the detail coefficients gk , iscalculated. This smooth signal is calculated using a moving me-dian filter with a window of seven samples. Subsequently, therobust residuals of level k, Rk are calculated according to thefollowing rule:

Rk [l] =

{0, |gk | < λk

sign (gk ) (|gk | − λk ) , |gk | ≥ λk

gk ≡ gk [l] − gk [l] (15)

where λk is the threshold for level k, defining a small per-centage p of the coefficient gk as outliers. Next, the detectedresiduals can be subtracted from gk and the next level of thewavelet decomposition can be performed. As discussed in [19],short decomposition wavelet filters reduce the leakage of outlier

patches into the smooth coefficients, isolating them and facilitat-ing their removal. On the other hand, long reconstruction filtersare desirable in order to ensure a sufficient level of smoothness.Following [19] the “b-spline” biorthogonal wavelets with threeand nine vanishing moments were used in this study.

In this study, the noise contaminating the DCEUS moviescontains isolated outliers in the time domain. In [24], the Fisher–Tippet noise containing isolated outliers was processed by asingle use of the outlier removal procedure. It was shown thatthis outlier removal step successfully removed the “heavy tail”of the distribution, producing the semi-Gaussian noise. At thesame time, the short moving median had a negligible effecton the signal. Similarly, a single use of the outlier removalprocedure was used in the current study. The resulting semi-Gaussian noise can then be removed using the standard three-step algorithm with the universal threshold rule. This processingscheme is robust to the noise outliers and also incorporates thenumerical efficiency of wavelet denoising.

IV. VALIDATION OF THE PROPOSED MODEL

All the phantom and in vivo experiments were performed us-ing the Vevo 2100 (VisualSonics Inc., Toronto, Canada) scannerand the linear array MS250 transducer in contrast mode. Am-plitude modulation is used in the Vevo2100 for the separationof CEUS signal. The cines were exported from the scanner aslinear raw data files (without log compression).

A. Phantom Experiments

In order to evaluate the performance of the proposed denois-ing algorithm when compared to established methods, a seriesof flow phantom experiments were performed.

1) Ultrasound Contrast Agents: MicroMarker UCA was usedin all the phantom and in vivo experiments. Before each exper-iment, a new vial was activated according to the company’sprotocol. Following the activation, the size distribution of themicrobubbles was measured using a Beckman Coulter Multi-sizer 3 system. In order to obtain reliable quantification, 10 μLof UCA was diluted in 10 mL of Isoton II diluent [9]. In eachtest, 100 μL of this solution was drawn by the Multisizer andeach measurement was repeated three times. The aperture useddetects bubbles with radii as small as 0.7 μm.

2) Continuous-Flow Phantom Setup: The purpose of thecontinuous-flow phantom experiments is threefold: to detect thelinear region of the relationship between the UCA concentrationand the ultrasound intensity, to validate the K-distributionmodel, and to compare the effectiveness of the MIP, temporalcompounding, and wavelet denoising algorithms using moviescontaining homogenous concentrations of UCA. The setup ofthis phantom is described in Fig. 1(a). Before each injection, asolution of deionized water and UCA was mixed using a mag-netic stirrer. Then, the solution was loaded into a 3-mL syringeand injected using a syringe pump (model NE-4000, New EraPump Systems, Inc., Farmingdale, NY, USA) into a polyethy-lene tube (internal diameter of 1.4 mm) submerged inside a smallwater tank. The injection rate was set to 7.7 μL/s. Cines of 300frames at 25 frames/s were recorded. The scans were repeatedusing four different vials of MicroMarker. The transducer was

1974 IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 62, NO. 8, AUGUST 2015

Fig. 1. Phantom setup. (a) Continuous-flow setup during the scanning phase.(b) Indicator-dilution setup.

aligned to the center of the flow channel with an in-plane an-gle of about 30° to minimize reverberations. A syringe pumpwas used in this experiment in order to reduce flow velocityfluctuations.

Several measures were made to improve the quality of themeasurements. Before mixing each solution, the vial of thecontrast agents was shaken to achieve stable microbubble con-centration over time. Before each injection, deionized water waspumped through the tubing using a peristaltic pump (Minipuls 3,Gilson Inc., Middleton, WI, USA) until no traces of UCAwere seen by the scanner. In addition, increasing concentra-tions of UCA were injected in order to reduce the influenceof residual microbubbles between consecutive injections. Fi-nally, great care was taken to remove static air bubbles from thetubing.

For each vial, a range of contrast agent dilutions spanningfour orders of magnitude were scanned (see Table I). This rangeof dilutions includes both tens of microbubbles in each resolu-tion cell, complying with the Rayleigh distribution approxima-tion, and resolution cells containing single bubbles. Assumingthat the average blood volume of a laboratory mouse is about1.5–2.5 mL; a 50 μL bolus injection of UCA would results in apreclinical concentration of around 2.5%.

The recorded cines were loaded into a personal computerand analyzed using dedicated MATLAB code (The Mathworks,Natic, MA, USA). A ROI was selected in each cine, containinga cross section of the tube with a negligible signal originatingfrom the walls of the tube. Since three tests were performedusing this phantom setup, three different processing methodswere applied to the data. First, in order to detect the linear re-gion of the UCA concentration versus ultrasound intensity, theintensity of the pixels inside the ROIs was averaged through-out the movie. Next, in order to validate the K-distributionmodel, the amplitude SNR was calculated inside the ROI forevery frame and averaged throughout the cine. Then, the SNRvalues were fitted to the K-distribution SNR model of (5).

TABLE ICONTRAST AGENTS CONCENTRATIONS USED IN CONTINUOUS-FLOW

PHANTOM EXPERIMENT

Relativeconcentration

Contrast agentconcentration

(%)

Scatterers perultrasound resolution

cell∗

Bubbles per unitvolume

(bubbles/μL)

A 5% – 67500 ± 3500A/2 2.5% – 33750 ± 1750A/5 1% 743.5 13500 ± 700A/10 0.5% 371.7 6750 ± 350A/20 0.25% 185.9 3375 ± 175A/50 0.1% 74.35 1350 ± 70A/100 0.05% 37.17 675 ± 35A/200 0.025% 18.59 337.5 ± 17.5A/500 0.01% 7.44 135 ± 7A/1000 0.005% 3.72 67.5 ± 3.5A/2000 0.0025% 1.86 33.75 ± 1.75A/5000 0.001% 0.74 13.5 ± 0.7

∗The number of effective Scatterers per ultrasound resolution cell was estimated by fittingthe measured amplitude SNR to the K-distribution model (see Fig. 2).

The K-distribution model enabled the normalization of the con-centration axis so that the SNR can be presented as a function ofthe number of effective scatterers per resolution cell. Finally, thelog-transformed cines which were processed using the differentalgorithms were compared in order to evaluate the effectivenessof the algorithms in estimating the TIC and the noise removal.The moving average filter of the temporal compounding algo-rithm was chosen to be 21 samples long, to achieve a reasonablenoise reduction, while avoiding oversmoothing the data duringthe wash-in phase. Following the considerations described in theprevious section, wavelet denoising was performed using the 3,9 biorthogonal wavelets. The robust residual removal procedurewas set so that the length of the moving-median window was 7and p was set to 7%.

The spatial coefficient of variation was used to evaluate theability of the different algorithms to remove the speckle noise.The underlying assumption was that the intensity within a smallROI should be uniform. The spatial coefficient of variation (CV )was defined as the ratio of the standard deviation σ to the meanμ of log-intensity value in each processed movie within theROI.

3) Indicator-Dilution Phantom Setup: An indicator-dilutionphantom was used to test the effectiveness of the different al-gorithms on real scans containing changing concentrations ofUCAs. The setup of this phantom, based on [27], is described inFig. 1(b). The three-way stopcock was used to connect both thereservoir and the syringe pump to the phantom. The peristalticpump was used to maintain a steady flow of 36 μL/s within thesystem. The syringe pump, filled with undiluted UCA, was usedto inject a bolus of 50 μL into the phantom. Cines of 920 framesat 25 frames/s were recorded using the Vevo 2100 scanner incontrast mode. The alignment of the MS250 probe was carriedout as described in the previous section.

The recorded cine was loaded into a personal computer andanalyzed using MATLAB. A ROI was selected as describedabove. Regional intensity measurements were calculated by av-eraging the intensity of the pixels included inside the entire5 × 5 pixel ROI. Local TIC were estimated from single pixels.

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Fig. 2. Continuous-flow phantom results. (a) Linearity of the relationship between the raw data intensity and UCA concentration. (b) Measured amplitude SNRand its compliance to the K-distribution model. (c) Spatial coefficient of variation for different processing durations (0.1% UCA). (d) Spatial coefficient of variationfor different concentrations of UCA.

The MSE and correlation coefficient (R) criteria were used toevaluate the discrepancy between the regional measurementsand local TIC estimations.

B. Processing of In Vivo Data

1) Ex-Utero Mouse Embryos: In order to evaluate how theproposed algorithm functions when a healthy well-organizedvasculature is imaged, a subset of the mouse embryo scansstudied in [28] was reprocessed. Specifically, the scans usedwere of embryos imaged at the embryonic day (E) 17.5 usinguntargeted MicroMarker at a frame rate of 1 frames/s. This lowframe rate was used in order to capture the full duration of thewash-out phase. Full details about the experimental preparation,microbubble reconstruction, injection procedure, and ultrasoundimaging can be found in [28].

2) Human LS174T and PC3 Xenografts in Mice: Xenografttumors were induced in mice using either LS174T human

colorectal cancer cells or PC3 human prostate cancer cells. Bothcell lines were cultured in Gibco DMEM (Life TechnologiesInc., ON, Canada) supplemented with 10% fetal bovine serum(Hyclone, UT, USA). One million cells were injected subcuta-neously into the left hind limb of each female eight-week oldSCID nude mouse (Charles River, CA, USA). The tumors werescanned after they reached a target size of 4–6-mm depth. Allprocedures were completed with the animal anesthetized underisoflurane and in accordance with Sunnybrook Health ScienceCentre’s approved protocol for animal care and use.

The transducer’s field of view was aligned to the center ofthe tumor with the largest cross section. The acoustic focus wasplaced around the level of the tumor’s center. The contrast gainwas set to 32 dB and the frame rate to 25 frames/sec.

3) Processing of DCEUS Cines: The recorded cineswere loaded into a personal computer and analyzed usingMATLAB. In order to evaluate the effectiveness of the differ-ent processing algorithms, the log-transformed processed cines

1976 IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 62, NO. 8, AUGUST 2015

were compared. The parameters used were those described inthe phantom experiments section.

For quantitative assessment of the different processing meth-ods, two measures were used: spatial coefficient of variationand spatial autocorrelation. In order to evaluate the ability ofthe different algorithms to remove the speckle noise from theex-utero scans, the spatial coefficient of variation was measuredwithin a ROI placed inside the carotid arteries of the mouseembryos. The carotid arteries are big vessels in which homo-geneous distribution of UCA may be assumed. Differences inspatial coefficient of variation values across the different meth-ods were tested using two-sample t-tests. A two-sided p-value<0.05 was considered to be statistically significant. The othermeasure was the support of the autocorrelation function exceed-ing 90% of the maximal value, in pixels. This measure was usedin ultrasound image processing studies to compare the resolu-tion of different images [8].

V. RESULTS

A. Phantom Experiments

Fig. 2 presents the results of the experiments with thecontinuous-flow phantom. The results are separated into threecategories: a calibration plot, evaluating the linear correlationbetween the mean intensity of the raw data and the concen-tration of microbubbles; validation of the K-distribution modelaccording to the SNR measured at different concentrations; andcharacterization of the spatial coefficient of variation over time,and over a wide range of concentrations. The two highest con-centrations were not included in the analysis since a significantshadowing effect was observed in all their scans.

The mean intensity measured as a function of UCA con-centration in the phantom, is presented in Fig. 2(a). The val-ues represent the mean intensity averaged over all experiments,and the error bars represent the standard deviation measuredwhen using UCA from the different vials. The results showa linear relationship between the UCA concentrations and themean intensity measures which holds for 2 orders of magni-tude. The linearity tails off for both the highest and the lowestconcentrations.

Fig. 2(b) shows a side-by-side comparison between the calcu-lated SNR value in the ROI and that from the fitted K-distributionSNR model. The SNR model was used to normalize the UCAconcentrations so that the measurements are presented as a func-tion of the effective scatterers per resolution cell. The SNR in-creases monotonically until reaching a plateau at a value of∼1.91. At the lowest concentration, an average of 0.7 effectivescatterers may be found in a resolution cell in each frame.

The spatial coefficient of variation (Cv ) is presented inFig. 2(c) when measured inside the ROI as a function of time, ata concentration of 0.1%. While TC and WD produce fairly lowCv over the whole scan, the Cv produced by MIP is markedlyhigher at the beginning of the cine. The Cv value of the MIP at t0is 0.52, the value expected from a single speckled CEUS frame.This value drops after a few seconds to a level comparable withTC. WD shows the lowest Cv throughout the cine. Similar re-sults were recorded for different UCA concentrations. Fig. 2(d)shows Cv averaged over time, as a function of concentration.The Cv values were averaged over the first 300 frames. The Cv

Fig. 3. Continuous-flow phantom results. Single frames taken from the dif-ferent processed cines (t = 1 s).

produced by WD is the lowest between the three algorithms,while that produced by MIP is the highest. A reduction of Cv

values at low UCA concentrations was recorded for both TCand WD.

The noise reduction performances of the different algorithmsare visualized in Fig. 3. This figure includes a single frame fromeach processed movie, taken one second after the beginning ofthe scan. These images show how well the homogenous UCAconcentration within the center of the phantom correlates withthe intensity in the processed CEUS movies. The frame takenfrom the WD movie is less noisy and more homogenous bothwithin the lumen of the phantom and in the background.

Fig. 4 presents the results of experiments performed withthe indicator-dilution flow phantom. The temporal denoisingperformance of TC and WD are compared against the MIPand the log-normal model fitting. Both local TIC estimation[single pixel, Fig. 4(b)] and regional TIC estimation [5 × 5 pixelaveraged, Fig. 4(a)] are presented. While TC, MIP, and themodel fitting results change when local estimation is attempted,regional and local WD results are very similar.

Numerical analysis of local TIC estimation is presented inTable II. The local TICs were estimated from single pixels withina 5 × 5 ROI. The averaged intensity within this ROI in eachframe was used as a point of reference, when evaluating thequality of local TIC estimation. The quality of the estimation wasquantified by the MSE and correlation coefficient (R) betweenthese estimations and the averaged regional measurements. Theproposed method produced both the highest R values and lowestMSE values of all the compared methods.

B. In Vivo Data

An example of a nonprocessed DCEUS scan of a mouse em-bryo is shown on the left row of Fig. 5. Three frames taken

BAR-ZION et al.: DENOISING OF CONTRAST-ENHANCED ULTRASOUND CINE SEQUENCES BASED ON A MULTIPLICATIVE MODEL 1977

Fig. 4. Indictor-dilution phantom results. (a) Regional TIC (5 × 5 averagedpixels). (b) Local TIC from a single pixel.

during the wash-in phase (50 s), the peak-enhancement (100 s),and the wash-out phase (250 s) are presented on the top, mid-dle, and bottom column. To the right, the corresponding framesfrom the processed MIP, TC, and WD movies are presented,allowing us to compare the three methods. Since MIP holds onto the maximum intensity value over time, there is no significantchange between the MIP image at the peak-enhancement timeand wash-out time. Moreover, the high concentration of UCAentering the liver during the first few frames of the wash-in isretained by MIP throughout the movie. In contrast, the UCAsignal decrease can be appreciated in both TC and WD images(for example, inside the brain). The ability of the WD to re-ject noise from DCEUS images is noticeable when observinghomogeneous areas such as the carotid arteries.

The shapes of single blood vessels in the core of a hind-limbtumor depicted by WD are presented in Fig. 6. Fig. 1ex (sup-plementary material) compares the WD results with MIP andTC. An interesting feature observed in Fig. 1ex is the robustness

TABLE IIMSE AND CORRELATION COEFFICIENTS OF SINGLE PIXEL TIC ESTIMATIONS

R MSE [a.u.]

Fitting 0.707 ± 0.004 9.70 ± 2.00MIP 0.641 ± 0.011 172.34 ± 21.44TC 0.719 ± 0.009 7.46 ± 1.73WD 0.742 ± 0.003 6.93 ± 1.48

of TC and WD to movement, compared to MIP. Although thehind-limb of the mouse is held in place, the cine contains smallmotion artifacts that are accumulated by the MIP and create ablurring effect. This effect can be seen clearly by comparingthe size of the air bubble trapped above the tumor. The bub-ble in the MIP frames appears considerably larger than in thecorresponding TC and WD frames.

The quantitative results acquired from the in vivo data arepresented in Fig. 7 and Table III. The proposed method achievedsignificant improvement in the spatial coefficient of variation,measured within homogeneous regions (9 × 9 ROI inside thecarotid arteries of mouse embryos, Fig. 7) in comparison to bothMIP and TC. In addition, comparing the support of the axial andlateral autocorrelation function, a considerable improvement inresolution was measured in the processed WD scans, comparedto the corresponding MIP frames (see Table III).

VI. DISCUSSION

While in most studies isotropic spatial filtering is performedbefore TIC estimation, it was shown here that using the proposeddenoising method, TIC could be estimated locally from sin-gle pixels. When tested on the indicator-dilution phantom data,the single pixel estimations provided good agreement with theregional TIC estimation and correlated well with the instanta-neous intensity measurements averaged over the ROI. The mainlimitation of the standard isotropic averaging is the resultingreduced spatial resolution. In addition, speckle noise containssignificant spatial structure, thus independence between noisemeasurements in adjacent pixels cannot be assumed. The de-noising was performed here only in the temporal domain, andwas able to remove the speckle noise and produce clear imagesthat describe the change in UCA concentration over time. Asdepicted in Fig. 3, the WD algorithm provides the homogenousconcentration of UCA within the phantom, while removing mea-surement noise from the surrounding regions. Fig. 5 presents aclear depiction of the UCA concentration in regular vasculature,while Fig. 6 shows the vasculature in a tumor. The tumor scansare characterized by lower UCA concentrations and demonstratethe ability of WD to produce clear depictions of isolated bloodvessels in the tumor’s core.

The linear correspondence between the UCA concentrationand the intensity of the ultrasound echoes is the basis of quan-titative contrast-enhanced ultrasound imaging. Fig. 2(a) showslinear correlation between UCA concentration and Intensity,over 2 orders of magnitude. Deviation from linearity observed atconcentration extremes can be attributed to acoustic shadowingeffects at high concentrations, and dominant tissue (phantom)harmonics at low concentrations.

1978 IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 62, NO. 8, AUGUST 2015

Fig. 5. Single frames from different processed cines during wash-in, peak enhancement, and wash-out phases of a mouse embryo scan. The noise levels areconsiderably lower in WD images especially during the wash-in phase (see arrows), while MIP presents partial filling of blood vessels. False structures in theMIP scans and their locations in WD images are marked by stealth arrows. The ability of WD to present the wash-out phase is easily seen in the brain tissue(arrowheads).

Fig. 6. Single frame from the wash-in phase in WD cine of LS174T tumor.

The models proposed in this study were supported by theexperimental results. The SNR values measured using thecontinuous-flow phantom corroborate the K-distribution SNRmodel [see Fig. 2(b)]. The favorable results achieved by bothTC and WD in this paper support the validity of the multiplica-tive noise model for CEUS images. Therefore, the proposedmodels can serve as a basis for applying different signal pro-cessing methods to DCEUS data. These models are general andshould hold for both clinical and preclinical scans.

Denoising of DCEUS signals enables estimation of UCAconcentrations during both wash-in and wash-out phases. Somevariants of MIP enable the depiction of the wash-out phase byattaching a decaying weight to the measurements. However, thisis an artificial modification that does not produce a direct rela-tionship between the UCA concentration during the wash-outphase and the MIP values. The growing research in the area oftargeted drug/gene delivery may benefit most from quantitativeimaging of targeted CEUS and, thus, from the denoised wash-out data, where possible new applications may arise once thiskind of data is available.

Fig. 7. Spatial coefficient of variation measured inside the carotids of themouse embryos in different processed cines.

It was also found that in MIP movies the Cv values are initiallyhigh, even when the concentration of UCA is constant. The Cv

values slowly decrease only after few seconds [see Fig. 2(c)]to a level comparable to TC and WD. This means that the MIPcines are very noisy at first and gradually become clearer. Thisdelayed response is problematic when the dynamics of DCEUScines is studied.

The proposed method has two main theoretical advantagesover classical TC. The first is that TC relies on the implicit as-sumption of the Gaussian noise. However, it is well known thatlog-transformed CEUS signals suffer from low-value outliers.These outliers motivated the introduction of a preprocessingstep for their removal. The second advantage of WD is that un-like TC, it does not require the definition of a cutoff frequencya priori. Moreover, a low-pass filter cannot separate the signaland the noise efficiently since they have an overlapping sup-ports in the frequency domain. Therefore, the optimal cut-offfrequency depends on the local flow characteristics and is diffi-cult to estimate in advance. In contrast, WD relies on the sparse

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TABLE IIIRESOLUTION RATIO GAIN OF WD IN COMPARISON TO MIP

Axial Resolution Ratio Lateral Resolution Ratio

LS174T tumors 1.66 ± 0.38 1.45 ± 0.1PC3 tumors 1.96 ± 0.26 1.74 ± 0.05Mouse Embryos 2.27 ± 0.19 1.62 ± 0.13All scans (n = 20) 1.99 ± 0.37 1.60 ± 0.15

representation of piecewise-smooth functions in wavelet basesand adaptively removes the noise from the data. WD retains thedominant fine details of the signal, while efficiently smoothingout other regions in which the signal changes slowly. This can beconsidered as setting different cut-off frequencies for differenttemporal segments. Thus, while both methods are parameter de-pendent, TC requires adjusting the upper frequency limit, whilefor WD the noise level is estimated from the measured signals.These theoretical advantages correspond to the improved de-noising capabilities of WD, for a wide range of UCA concentra-tions in the in vitro experiments [see Fig. 2(d)]. Correspondingquantitative in vivo results were presented in Fig. 7.

Indicator-dilution models enable the reliable analysis ofCEUS signals only when the model fits the dynamics of the un-derlying UCA concentrations and the noise level is sufficientlylow to enable the estimation of the model’s parameters. Whenthe underlying signals do not correspond to the fitted modelor the noise level is high, the estimation is unsatisfactory (seeFig. 4). Denoising is an important preprocessing procedure forindicator-dilution model fitting, enabling the noise removal androbust TIC estimation, while including a high level of flexibility.Denoising is also useful if the effects of UCA recirculation aredominant and the first passage of UCA should be detected andanalyzed separately.

Denoising of DCEUS signals using WD and TC improved thespatial resolution in comparison to MIP analysis; MIP retainsthe maximal intensity over time, thus movement blurs the image(see Table III). Processing schemes of scans containing signifi-cant motion artefacts should incorporate motion-compensationalgorithms, but the residual movements are expected to be betterhandled using denoising methods.

There are two main limitations of the proposed methods incomparison to MIP. The first is the ability of MIP to capturethe flow trajectories of extremely sparse microbubbles. MIP isnonlinear and is very sensitive to single bubbles. However, theoutlier removal rule can be adjusted to discriminate betweenlow-value noise-related outliers and positive outliers producedby single bubbles. This is a subject of future research. The sec-ond limitation is that MIP retains the positive outliers of theintensity distribution and by that improves the contrast to tissueratio. This characteristic can be important in preclinical sys-tems, having low contrast to tissue ratio. Adjusting the contrastspecific pulse sequences and the dynamic range of the scannerto minimize tissue harmonics is important in these cases.

VII. CONCLUSION

To conclude, temporal denoising of log-transformed DCEUSdata was shown to be an effective framework for processing

DCEUS signals: WD produces superior results in reducing thevariability of DCEUS signals and removing speckle noise fromthese cines. WD enabled the estimation of local TIC without theneed for spatial isotropic filtering. A substantial improvementin noise reduction was demonstrated over a wide range of UCAconcentrations. The increased SNR resulted in a significant im-provement in the depiction of homogenous regions (p < 0.01).Denoising of DCEUS signals enables the estimation of UCAconcentration maps through the duration of the scan, reveal-ing new observations on CEUS dynamics. Finally, denoising ofDCEUS signals was proved to be relatively resistant to motionartifacts, having higher resolution by a factor of 2 in the axialdirection and 1.6 in the lateral direction, as compared to MIP.

ACKNOWLEDGMENT

The authors would like to thank J. Denbeigh from the De-partment of Medical Biophysics at the University of Toronto forproviding mouse embryo scans used in this study. F. S. Fosterdiscloses that he is a Consultant to VisualSonics.

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Avinoam David Bar-Zion was born in Haifa, Israel,in 1983. He received the B.Sc. degree in biomedicalengineering from the Technion, Haifa, in 2010, wherehe is currently working toward the Ph.D. degree inbiomedical engineering.

He completed a year of research as a part of a col-laboration between the Technion and with the Medi-cal Biophysics Department, University of Toronto,Toronto, ON, Canada. His research interests in-clude contrast-enhanced ultrasound imaging, medi-cal signal and image processing, and computer-aided

diagnosis.

Charles Tremblay-Darveau was born in St-Charles-Borromee, QC, Canada, in 1987. He received theB.Sc. degree in physics from McGill University,Montreal, QC, in 2009, and the M.S. degree inmedical biophysics from the University of Toronto,Toronto, ON, Canada, in 2011 and is currently work-ing toward the Ph.D. degree in medical biophysics atthe University of Toronto.

His research interests include nanofluidics,Doppler contrast-enhanced ultrasound imaging, andnoninvasive blood pressure measurement with

microbubbles.

Melissa Yin was born in Nanjing, China, in 1988.She received the B.Sc. degree in biology from Mc-Master University, Hamilton, ON, Canada, in 2010.

Since graduating, she has been working at Sunny-brook Research Institute, Toronto, ON, as a ResearchBiologist in Imaging Research. Her research interestsinclude the use of contrast-enhanced ultrasound andphotoacoustic imaging to monitor disease progres-sion and therapeutic efficacy in preclinical models ofcancer and other pathologies.

Dan Adam (M’64) was born in Israel. He receivedthe B.Sc. and M.Sc. degrees in electrical engineeringand the D.Sc. degree in biomedical engineering fromthe Technion, Haifa, Israel, in 1968, 1973, and 1977,respectively.

He was an R&D Engineer in Biomedical Indus-try (Elscint Ltd.). He joined the Technion Faculty in1977. He was with Tufts University, Boston, MA,USA, from 1978 to 1980. He joined MIT in 1980,teaching with the Harvard-MIT HST Program. Herejoined the Technion in 1983, and is currently a Pro-

fessor in Biomedical Engineering and was the Dean from 2009 to 2012. Hehad a sabbatical at NIH, MD, from 1992 to 1993. He serves on the Board ofthe Israel Society for Medical and Biological Engineering since 1988, and asthe President till 2007, and it’s a Delegate to the International Federation forMedical and Biological Engineering, and the Past-Chair, Academic Divisionand Council Member, European Alliance for Medical and Biological Engineer-ing and Science. His research interests include ultrasound processing—designof multifrequency-phased array probes for the development of multifrequency(spectral) imaging; ultrasound RF processing; echocardiography strain imag-ing, including layer-specific 2-D strain measurements as a diagnostic tool ofmyocardial pathologies; ultrasound RF processing; multitransducer-phased ar-ray design; beam-forming design—coded excitation; perfusion measurementsusing contrast agents; pressure estimation using contrast Agents; high-intensityfocused ultrasound; ultrasound control of thermotherapy—monitoring ofthermal-cavitational therapy; cellular and subcellular processes generating ar-rhythmias and alternans; detection and manipulations of contrast agents; func-tional imaging; multimodality cardiac imaging; cardiac-MRI and echocardio-graphy image fusion, and functional data fusion; quantification of neovascula-ture within plaques for estimating its vulnerability; monitoring neovasculaturewithin cancerous lesions; targeted drug or gene delivery using ultrasound andmicrobubbles (contrast agents); noninvasive thrombolysis by focused ultrasoundin acute ischemic stroke; cardiac pacing by noninvasive high-intensity focusedultrasound; neural activation by noninvasive high-intensity focused ultrasound.

Dr. Adam elected as the IEEE-EMBS AdCom in 1999, and to the Board ofComputers from Cardiology 1990 to 1999.

F. Stuart Foster (M’90–SM’95) received the B.A.Sc.degree in engineering physics from the University ofBritish Columbia, Vancouver, BC, Canada, in 1974,and M.Sc. and Ph.D. degrees in medical biophysicsfrom the University of Toronto, Toronto, ON, Canada,in 1977 and 1980, respectively.

He is the Founder and the former Chairman ofVisualSonics Inc., a company dedicated to preclini-cal imaging. Dr. Foster co-founded the Mouse Imag-ing Centre (MICe) now at the Toronto Centre forPhenogenomics. He has served on the Board of Di-

rectors of the National Cancer Institute of Canada and as the Chairman of itsCommittee on Research. He is currently a Senior Scientist at the SunnybrookHealth Sciences Centre, Toronto, ON, Canada, and a Professor and the CanadaResearch Chair in Ultrasound Imaging, Department of Medical Biophysics,University of Toronto, Toronto. His current research interests include the de-velopment of high-frequency clinical and preclinical imaging systems, arraytechnology, intravascular imaging, photoacoustics, and molecular imaging. Heserves on numerous advisory bodies and is currently on the Editorial Boards ofUltrasonic Imaging and Ultrasound in Medicine and Biology.

Dr. Foster is a Fellow of the American Institute of Ultrasound in Medicine.He received the 2010 Rayleigh Award from the IEEE. He has received the EadieMedal for major contributions to engineering in Canada and also received theQueen’s Golden Jubilee Medal, the Manning Award of Distinction for CanadianInnovation, and the Ontario Premier’s Discovery Award.