Improvement of the power response in contrast imaging with transmit frequency optimization

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Improvement of the power response in contrast imaging with transmit frequency optimization

Sébastien Ménigot, Anthony Novell, Ayache Bouakaz, Jean-Marc Girault

To cite this version:

Sébastien Ménigot, Anthony Novell, Ayache Bouakaz, Jean-Marc Girault. Improvement of the power response in contrast imaging with transmit frequency optimization. IEEE. IEEE Interna-tional Ultrasonics Symposium (IUS), Sep 2009, Rome, Italy. IEEE, pp.1 - 4, 2009, �10.1109/ULT-SYM.2009.5441554�. �hal-01075511�

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Improvement of the power response in contrast imaging with transmit

frequency optimization

Sébastien Ménigot, Anthony Novell, Ayache Bouakaz and Jean-Marc Girault

Université François Rabelais de Tours, Tours, France INSERM, U930, Tours, France

CNRS, ERL 3106, Tours, France _{September 20-23, 2009}

1. Introduction

Ultrasound (US) contrast imaging is being investigated for

tissue function and for targeted therapeutic applications with microbubble ultrasound contrast agents (UCA). Currently

technical challenge consists in seeking for US excitations

which should make possible the optimization of the contrast tissue ratio (CTR). This ratio can be maximized if the

microbubbles backscattered power is maximized with the transmit signal. CTR = Ebubbles E_BMF , with ⎧ ⎨ ⎩

E_bubbles the microbubbles backscattered

power

E_BMF the tissue backscattered power

We tackled the problem in a simple way by proposing an adaptive imaging technique which seeks the transmit

frequency that maximizes the backscattered power. That is to disregard these unknown factors, it seemed more judicious to propose an US excitation whose frequency is selected in an adaptive way.

2. Methods

Emission signal _{Transducer 1} _Microbubbles _{Transducer 2}

Optimization

1. Transmit pulse with the frequency f_old and a 4 cycles →

Constant transmit power

2. Repeat (1) twenty times

3. Compensation of bandwidth transducers

4. Linear combination of twenty signals by principal

component analysis

5. Backscattered power of the new signal

6. Optimal frequency f_new computed by the optimization

algorithm → maximizing backscattered power

Two different optimization algorithms

▶ Golden section search (GSS)

Choose two frequencies (f₁ and f₂) around the maximum

power.

Reduce the intervals with a third frequency f₃

▶ Gradient ascent (GA)

steps proportional to the gradient of energy

→ f_k₊₁ ₌ f_k _{+ 𝛼}_k∇E₍f_k₎

7. f_old = f_new and return to (1)

3. Materials

Microbubbles

▶ Microbulles SonoVueTM: mean diameter of 4.5 𝜇m with shell thickness of

1 nm ; Resonance frequency : f_R = 2.1 MHz

▶ Concentration: 1/2000 diluted solution of SonovueTM

▶ Immersed in a blood mimicking fluid (BMF)

0 1 2 3 4 5 6 7 −40 −35 −30 −25 −20 −15 −10 −5 0 Transducers 2.25 MHz & 3.5 MHz Frequency (MHz) Gain (dB) 3.5 MHz 2.25 MHz 0 2 4 6 8 10 12 14 16 18 20 −150 −100 −50 0 50 100 150 Pulse (4 cycles) Time (µs) Pressure (kPa) Acoustical Measurements

▶ Arbitrary function generator piloted by Matlab⃝R

▶ 2 perpendicular transducers : 1. Emission : 2.25 MHz - BW 74% 2. Reception : 3.5 MHz - BW 63% Emission Reception 4. Results : simulation

BUBBLESIM (Model based on modified Rayleigh-Plesset equation)

0.5 1 1.5 2 2.5 3 3.5 4 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1

Energy according to the frequency for a microbubble of 2 µm−radius

Frequency (MHz) Energy A = 80 kPa A = 127 kPa A = 244 kPa A = 280 kPa A = 370 kPa A = 420 kPa Backscattered Power Pressure Frequency -17.4 dB 244 kPa 2.25 MHz 222 kPa 1.65 MHz -13.6 dB 370 kPa 2.25 MHz 308 kPa 1.48 MHz 2 4 6 8 10 12 14 16 18 20 1 1.5 2 2.5 3 3.5 4 4.5

Golden section search

Iterations Frequency (MHz) A = 127 kPa A = 244 kPa A = 370 kPa 2 4 6 8 10 12 14 16 18 20 −40 −30 −20 −10 0 Iterations Power (dB) A = 127 kPa A = 244 kPa A = 370 kPa 2 4 6 8 10 12 14 16 18 20 1 1.5 2 2.5 3 3.5 4 4.5 Gradient algorithm Iterations Frequency (MHz) A = 127 kPa A = 244 kPa A = 370 kPa 2 4 6 8 10 12 14 16 18 20 −40 −30 −20 −10 0 Iterations Gain (dB) A = 127 kPa A = 244 kPa A = 370 kPa 5. Results : experiments 2 4 6 8 10 12 14 16 18 20 1 1.5 2 2.5 3 Iterations Frequency (MHz)

Transmit Power Optimization − Golden section search

A = 244 kPa A = 370 kPa 2 4 6 8 10 12 14 16 18 20 −2 0 2 4 6 8 Iterations Power (dB) 2 4 6 8 10 12 14 16 18 20 0 5 10 15 20 Iterations CTR (dB) 2 4 6 8 10 12 14 16 18 20 1 1.5 2 2.5 3 Iterations Frequency (MHz)

Transmit Power Optimization − Gradient ascent

A = 244 kPa A = 370 kPa 2 4 6 8 10 12 14 16 18 20 −2 0 2 4 6 8 Iterations Power (dB) 2 4 6 8 10 12 14 16 18 20 0 5 10 15 20 Iterations CTR (dB)

Backscattered Power Without GSO GA

5 dB pressure (kPa) 244 222 222 frequency (MHz) 2.25 1.45 1.18 duration gain 21% 37% 6.6 dB pressure (kPa) 370 305 305 frequency (MHz) 2.25 1.61 1.27 duration gain 13% 17%

The optimal frequencies depend on the transmitted pressure. The maximal power shifts forward the low frequency when the pressure increases.

The CTR is optimized with the maximization of backscattered power in around 10 iterations.

6. Discussion

In vitro, the power is computed for a cloud of microbubbles. To cancel the movements of the cloud, we repeat the experiment. A high number of repetition and a high number of iterations could destroy the

microbubbles and thus the power could decrease. A trade-off must be found to avoid the destruction of the microbubbles.

By proposing a close loop system whose frequency adapt itself with the perfused media, throughout the examination, the optimization

system adapt itself to the remaining bubbles population thus allowing an increase of the 37% examination duration.

7. Conclusion & future prospects

▶ The optimization permits to increase the backscattering energy. This

gain increases the CTR.

▶ Increase of the 37% examination duration

▶ Simultaneous tissue echo minimization and microbubbles echo

maximization

▶ Simultaneous adaptation in frequency and in amplitude

E-Mail: sebastien.menigot@etu.univ-tours.fr jean-marc.girault@univ-tours.fr

Acknowledgement: This work was supported by Agence Nationale de la Recherche.