Two Frequencies Push-Pull Differential Imaging

10.1109/ULTSYM.2014.0175

Two Frequencies Push-Pull Differential Imaging Andrzej Nowicki, Michał Byra, Jerzy Litniewski, Janusz Wójcik Department of Ultrasound, Institute of Fundamental Technological Research - Polish Academy of Sciences, Warsaw, Poland [email protected] acoustic radiation force impulse (ARFI) imaging, is intensively investigated since early 2000. It was shown that after applying high intensity pulse generating radiation force on tissue resolution and contrast on par with and, in some cases, better than that in matched B-mode images, [4,5]. The possibility of remote generation of the shear wave was demonstrated by Sarvazyan et al [6]. Bercoff et al [7, 8] showed that propagation velocity of the shear wave can be determined from the 2D images acquired with a high Frame Rate (FR). The authors applied a wave with the frequency of 4MHz and length of 100μs that was adequately focused along the beam axis, at multiple depths (four to five). As a result, a shear wave front is moving at a supersonic speed. Subsequently, the organs are B-scanned with a very high frame rate, FR>5.000/s and a series of 50 or more US images are acquired for further processing and the process of shear wave generation and imaging starts anew. The shear wave velocity is determined by correlating subsequent images. When this value has been obtained, the Young’s modulus is calculated. It has been demonstrated in experimental studies that the shear wave produced by radiation force depends on the viscous and elastic properties of tissue. Professor Angelsen team published several papers on the novel Second order UltRasound Field (SURF) imaging system based on transmitted dual frequency band pulse complexes with at least partly overlapping high frequency HF and low frequency LF pulses. The advantage of this method in contrast enhancement in pig kidney was demonstrated in vivo after bolus injection of Sonovue [9, 10]. In their latest work [11] they have shown through computer simulations that SURF allows efficient reverberation suppression in synthetic transmit beam in inhomogeneous medium which emulates a strongly aberrating body wall.

Abstract—Nowadays there are new modalities in ultrasound imaging allowing better characterization of tissue regions with different stiffness. We are proposing an approach based on simultaneous propagation of two waves being a combination of two pulses differing in pressure and frequency: a low frequency pulse is expected to change the local scattering properties of the tissue due to compression/rarefaction while a high frequency pulse is used for imaging. Two transmissions are performed for each scanning line. First, with the imaging pulse that propagates on maximum compression caused by a low frequency wave. Next, the low frequency wave is inverted and the imaging pulse propagates over the maximum rarefaction. After the processing of the subtracted echoes from subsequent transmissions including wavelet transform and band-pass filtering, differential images were reconstructed. The low frequency wave has a visible impact on the scattering properties of the tissue which can be observed on a differential image. Keywords—tissue stiffness, ultrasonography, elastography

I. INTRODUCTION US imaging is based on the reflectivity of ultrasonic waves at the interfaces between tissue, differing in their acoustic impedances, being expressed by a product of density and longitudinal speed (depending on the modulus of elasticity). The elasticity of materials describes their property to return to their original shape after the material has been subjected to an external force or distorting stress. Tissue elasticity was, for a long time, estimated using manual palpation. In the process of palpation, physicians apply manual pressure to the patient's skin and in this way sense the location and stiffness of the organ in the body. Manual palpation, although widely used, has limitations. There is relatively easy access only to the superficial organs, while in more deeply located organs like the thyroid or liver the physician can only differentiate large masses with significantly different stiffness. An excellent general review of the methods applied in elastography and historical survey of palpation techniques was made by Wells and Liang [1]. Real-time compressional tissue elastography, firstly introduced by Ophir and al [2, 3] uses ultrasound to differentiate between hard and soft tissue. Freehand compression applies stress to tissue and the resulting tissue displacement, is displayed as a colour overlay on a conventional B-mode ultrasound image. The acoustic remote palpation, capable of imaging local variations in the mechanical properties of soft tissue using

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II. COMBINED TWO PULSES PROPAGATION All effects mentioned above are rather small for the ultrasonic intensities applied in diagnostic ultrasonography. We are proposing an approach combining the propagation of two waves, causing the enhanced compression and rarefaction of the tissue simultaneously with the probing/imaging pulse. The propagating wave is the sum of two individual pulses differing in frequency by a factor of 5 up to 10 times transmitted coherently in a sequence of two independent transmissions, as shown in Fig.1. In the first transmission the high frequency probing pulse coincides

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2014 IEEE International Ultrasonics Symposium Proceedings

alcohol) (PVA) tissue mimicking phantoms, having different stiffness, 0.14GPa (8% PVA) and 0.31GPa (12% PVA). Three different sets of RF echoes from the scanned phantoms were recorded. The first, direct set without activating the side low frequency transducers (the reference image). Next, two combined pulses, being the sum of the imaging pulse and positive (compression) and negative (rarefaction), respectively (as in Fig.1), were transmitted.

with the maximum of compression (displacement) caused by the low frequency wave. Secondly the low frequency wave is shifted by π radians, which results in the change of compression to rarefaction.

III. SIGNAL PROCESSING Phantom’s central region of 35mm height was cropped from the RF data to keep a ROI with only surrounding and removed phantom borders. Our goal was to find a suitable difference between the compression/rarefaction echoes from the ROI and the surrounding region. First, high-pass filtering was appointed to remove low frequency components from the RF data. As will be shown later, even a simple subtraction of the echoes from two subsequent transmissions modifies the ROI. To increase the difference two methods of signal analysis are applied. The first is related to the raw band-pass filtering of subtracted echoes that passes a specific frequency band chosen according to the Gabor transform. Secondly, the wavelet transform of subtracted echoes is performed. The Gabor transform is the short time Fourier transform with a Gaussian window:

Fig. 1, The actual sequence of transmitted pulses measured with a needle hydrophone. Propagating pulse with compression (left) and rarefaction (right).

It is assumed that during transmissions, tissue is modified in terms of its mechanical properties which influence local acoustic impedance and scattering characteristics. The examination is based on comparing the backscattered echoes from two subsequent transmissions. Although, as mentioned before, tissue displacements are considered small, the gradients of displacements are large enough. The difference of echoes measures the physical impact of the low frequency pressure wave on tissue and related to local stiffness. The imaging system shown in Fig.2 consists of three transducers. An imaging linear array, 5 MHz transducer (Philips, L4-7) and two symmetrically side mounted rectangular transducers, 0.5 MHz each.

Gf ( u, ω ) =

∞

∫

f ( t )e − iωt e

2

− π ( t − u ) dt

(1)

−∞

The idea is to analyze the subtraction of echoes from the two subsequent transmissions with the wavelet transform (CWT) and to perform filtration. It is expected that the wavelet coefficients change in the ROI where a beam from the low frequency/high pressure transducer is focused. Keeping significant wavelet transform coefficients will ensure better differentiating and emphasizes the subtracted signal in the ROI. Ultrasound imaging pulses are usually modeled as sine-Gaussian waves. The choice of such a wavelet which will efficiently capture sine-Gaussian wave variations, is indicated, [12]. The Morlet wavelet is a good choice since it is composed of a complex exponential and the Gaussian window.

Fig. 2, The imaging system

IV. RESULTS

All transducers were transmitting a two-cycle sine wave, but with a different pressure. Imaging pulse peak to peak pressure was 0.56MPa, measured in the focal zone. To ensure the “modification” of the media through the additional compression/rarefaction, the low frequency transducers emitted a higher peak to peak pressure of 2.4MPa. The pressure distribution of the combined pulse was measured in a water bath using a needle hydrophone. Because of the transducers' geometry a proper combination is possible only in the focal region over a certain length (around 8mm) which is our region of interest (ROI). The method was evaluated with two homogeneous poly(vinyl

As mentioned before, echoes (RF data) were cropped and high-pass filtering was performed in order to remove the low frequency content. Fig.3 shows an example of echoes for different transmissions. The ROI is situated at a depth between 15mm and 21mm. The low level signal’s amplitude was normalized to [-1, 1]. The difference is more visible if we look at the Gabor Transform of the above signal in Fig.4. One can notice that some Fourier coefficients are greater in the ROI (inside the black frame) and this is the key to successful signal processing. The Butterworth band-pass filter was

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used to maintain frequencies between 5.5MHz and 7.5MHz, which resulted in a ROI signal improvement. As would be expected, the subtracted signal is highly sensitive to noise, Fig.5. direct

compression

Fig.7, RF pulses measured using needle hydrophone positioned 3mm below the “soft” phantom. The estimated speed of sound: direct (black) -1520,9 m/s, compression (blue) - 1521,2 m/s and rarefaction (red) - 1520,8 m/s.

rerefaction

The recorded sound velocity changes between phantoms under compression or rarefaction are small, of the order of parts per thousand; however, they translate into a change in acoustic impedance within the heterogeneities of phantom material. Rigid tissue being less susceptible to deformation caused by the pressure wave is deformed to a lesser extent, thereby resulting not only in lower local speed of sound, but also the density. This translates to thereby lower the impedance change resulting in modification of the local reflection coefficient. The effectiveness of the algorithm was quantified by calculation of the contrast to noise ratio (CNR) and contrast to tissue ratio (CTR). Both coefficients express simply the fact that the detectability increases with increasing object contrast and decreasing acoustic noise (speckle variance).

depth

frequency [MHz]

Fig. 3, Echoes from a single transmission: only imaging pulse (upper), imaging pulse plus compression (middle), the imaging pulse plus rarefaction (bottom).

depth Fig. 4, Gabor transform modulus of subtracted echoes.

CNRenv =

IQ direct − IQ differ

,

Var IQ direct + Var IQ differ

(2)

where and Var|IQ| are the mean and variance for the envelope |IQ|. In dB scale the coefficient is equal dB

CNRenv _ dB =

Fig 5, Subtracted echoes (along an arbitrary line) after band-pass filtering.

Filtration in the Gabor transform (or Wavelet transform) domain shows the ability of signal enhancement in the ROI. All in all, band-pass filtering and wavelet transform were used on each RF line and differential images were reconstructed. After envelope detection, standard and differential images were reconstructed, Fig.6.

where IQ

dB

dB

IQ direct − IQ differ dB

dB

Var IQ direct + Var IQ differ

(3)

= 20log ( IQ ) .

The quality of the bitmap output was calculated using the formula

CNRimage =

I direct − I differ Var ( I direct ) + Var ( I differ )

.

(4)

Finally the CTR was calculated according to two simple formulas: dB

dB

CTRdB = IQ direct − IQ differ

(5)

dB dB CTRimage = I direct − I differ .

(6)

and Fig. 6, Comparison of the standard imaging technique and the differential one, ROI in white frame. Dynamic range is 50dB.

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The results are given in Table 1.

[6]

TABLE 1: CNR AND CTR VALUES FOR THE PHANTOM SCANS FROM FIG.6

CNRenv CNRenv_dB CNRimage CTRenv CTRimage

Phantom 1 stiff 1,7 3 1,7 25,8 138,7

Phantom 2 soft 1,2 2,6 -0,2 23,7 -11,6

Phantom 3 stiff 1,8 3,4 2 27,4 136,8

Phantom 4 soft 1,4 2,4 -0,5 20,6 -15,5

[7]

[8]

[9]

V. CONCLUSIONS

[10]

A low frequency wave has a different effect on the scattering of ultrasound within the phantom, depending on its rigidity. Apart from the signal processing we can observe in Fig.6 that the differential images of soft phantoms soft are brighter than those for rigid ones. As expected, the lowfrequency wave has less impact on a rigid structure, which translates to smaller differences between compression (C) and rarefaction (R) images resulting in darker final differential images. Similar conclusion can be drawn from the analysis of CNR and CTR ratios of Table 1, comparing the direct and the differential images. We see that in general the soft phantoms are characterized by a lower value of the coefficients. Originally CNR and CTR ratios were used to estimate the contrast to background noise in the tissue mimicking phantoms. In our case, where the noise part is replaced by the differential image (or its amplitude envelope) these ratios are still to be a measure of contrast, but rather between two “competing” images created by two methods. Based on the data from the table, we conclude that the differential images of soft phantoms have greater contrast comparing to the corresponding direct. Differential signals for rigid phantoms are lower. Fig. 6 shows a standard US images (upper four scans) and corresponding differential images (lower four images) obtained applying method described.

[11]

[12]

Sarvazyan A.P., Rudenko O.V., Swanson S.D., Fowlkes J.B., Emelianov S.Y., Shear wave elasticity imaging: a new ultrasonic technology in medical diagnosis. Ultrasound Med. Biol., Vol. 24, 1419-1435, 1998. Bercoff J, Tanter M, Fink M. Supersonic shear imaging: a new technique for soft tissue elasticity mapping. IEEE Trans. Ultrason.. erroelect. Freq. Contr., 51, 396 – 409. 2004a. Bercoff J, Tanter M, Muller M, Fink M. The role of viscosity in the impulse diffraction field of elastic waves induced by acoustic radiation force. IEEE Trans. Ultrason. Ferroelect. Freq. Contr., 51, 1523 – 1536, 2004b. Hansen R, Måsøy S-E, Johansen T.F, Angelsen B.A, Utilizing dual frequency band transmit pulse complexes in medical ultrasound imaging, J. Acoust. Soc. Am. 127, 1, 2010 . Måsøy S-E, Standal A, Näsholm S.P, Tonni F. Johansen T.F., Angelsen B.A., Rune Hansen R., SURF Imaging: In Vivo, Demonstration of an Ultrasound Contrast Agent Detection Technique, IEEE Transactions on Utrasonics, Ferroelectrics and Frequency Control, vol. 55, no. 5, 2008. Näsholm S.P. and Angelsen B.A., ,SURF Imaging Beams in an Aberrative Medium: Generation and Postprocessing Enhancement, IEEE Transactions on Ultrasonics, Ferroelectrics, and Fr equency Control, vol. 59, no. 11, November 2012. S. Mallat, Wavelet tour of signal processing, Elsevier Science, 1999.

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