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Contrast-to-tissue ratio improvement by transmitted optimized binary signal in ultrasound pulse inversion
imaging
Sébastien Ménigot, Jean-Marc Girault
To cite this version:
Sébastien Ménigot, Jean-Marc Girault. Contrast-to-tissue ratio improvement by transmitted opti- mized binary signal in ultrasound pulse inversion imaging. 2013 IEEE 10th International Sympo- sium on Biomedical Imaging (ISBI), Apr 2013, San Francisco, United States. pp.776 - 779, 2013,
�10.1109/ISBI.2013.6556590�. �hal-01075518�
Contrast-to-Tissue Ratio Improvement by Transmitted Optimized Binary Signal in Ultrasound Pulse Inversion Imaging
Sébastien Ménigot
1,2,†and Jean-Marc Girault
2,‡1 IUT Ville d’Avray, université Paris Ouest Nanterre La Défense, Ville d’Avray, France
2 UMR-S930 “Imaging and Brain”, université François Rabelais de Tours, Inserm U930, Tours, France
† [email protected], ‡ [email protected]
April 7-11, 2013
1. Introduction & Background A. Ultrasound Contrast Imaging
◮ Injection of encapsulated microbubbles
◮ High nonlinear behaviour
◮ Contrast-to-Tissue Ratio CTR =
Emicrobubbles
Etissue
0 2 4 6 8 10
−80
−60
−40
Frequency (MHz)
Amplitude (dB)
0 2 4 6 8 10
−80
−60
−40
Frequency (MHz)
Amplitude (dB)
B. Simulation
◮ Marmottant model for a 2.5 µm microbubble
◮ Nonlinear propagation in tissue by Anderson’s model
◮ Transducer: 4 MHz - 75%
2. Pulse Inversion Ultrasound Imaging with Feedback
Increasing CTR by odd harmonic extraction
Simpson et al., IEEE T UFFC, 46(2), 1999
3. Usual Transmitted Pulse 4. Optimal Binary Command
2 6 10 14 18 22 26 30 34 38
−1
−0.5 0 0.5 1
Samples
Input Sample
Input Signal with a Fixed Transmit Frequency
2/3 f
c Optimal frequency
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2
−150
−100
−50 0 50 100 150
Time (µs) Driving Pressure (kPa)
p(t) at the transducer output
Horizontal Position (mm)
Depth (mm)
Synthetic image at 2/3 f
c
0 1 2 3 4 5 6 7 5
6 7 8 9 10 11 12 13 14 15 16
Grey Level (dB)
15 17.5 20 22.5 25 27.5 30 32.5 35 37.5 40 42.5 45 47.5 50 52.5 55
Horizontal Position (mm)
Depth (mm)
Synthetic image at f
opt
0 1 2 3 4 5 6 7 5
6 7 8 9 10 11 12 13 14 15 16
Grey Level (dB)
15 17.5 20 22.5 25 27.5 30 32.5 35 37.5 40 42.5 45 47.5 50 52.5 55
CTR = 13.6 dB CTR = 16.8 dB
2 6 10 14 18 22 26 30 34 38
−1 0 1
Samples
Binary Value
Optimal Binary Command
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2
−150
−100
−50 0 50 100 150
Time (µs) Driving Pressure (kPa)
p(t) at the transducer output
Horizontal Position (mm)
Depth (mm)
Synthetic image with the optimal binary command
0 1 2 3 4 5 6 7 5
6 7 8 9 10 11 12 13 14 15 16
Grey Level (dB)
15 17.5 20 22.5 25 27.5 30 32.5 35 37.5 40 42.5 45 47.5 50 52.5 55
CTR = 17.6 dB 5. Optimization Process by Binary Genetic Algorithm
20 40 60 80 100 120 140 160 180 200
12 12.5 13 13.5 14 14.5 15 15.5 16 16.5 17 17.5 18
Generation
CTR (dB)
Optimization
Genetic Algorithm Optimal frequency f
opt
2/3 f
c
6. Discussion
◮ Automatic optimization of CTR
◮ Gain
◮ 4 dB in comparison with an excitation at 2/3fc
◮ 0.8 dB in comparison with an excitation at the optimal frequency
Ménigot et al., Optimization of Contrast to Tissue Ratio by Frequency Adaptation in Pulse Inversion Imaging, IEEE T UFFC, 59(11), 2012
◮ Stochastic excitation combined with genetic algorithm → Nonlinear
◮ Nonlinear backscattered components only due to medium nonlinearities
◮ Prospects
◮ Implementation on ultrasound scanner
◮ Using programmable analogue transmitter
