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M-sequence-coded excitation for magneto-acoustic imaging

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Abstract

Magneto-acoustic imaging is a novel functional imaging method to electrical characteristics of tissue. It provides valuable tools for diagnosing early stage tumor and monitoring bioelectrical current. Common single short-pulse excitation limits SNR due to the short-pulse duration and low power of magneto-acoustic signal. In this study, we propose M-sequence-coded excitation and pulse compression approach to improve SNR of magneto-acoustic imaging. Simulations on the magneto-acoustic signal under different bit lengths M-sequence-coded excitation are performed. Experiments on the samples made of pork and graphite slices are done to validate the proposed coded excitation method. The SNR and sidelobe levels were investigated. The results showed when 7, 15, 31, 63, 127 bits M-sequence-coded excitations were applied onto the samples, SNR was improved by 17.4 dB, 24.2 dB, 30.6 dB, 37.6 dB, and 40.1 dB, respectively. For a similar SNR improvement, the total used time under coded excitation can be shortened to 9.4% under the single pulse excitation. The result indicates the M-sequence-coded excitation approach is effective to improve the magneto-acoustic signal SNR and shorten the imaging time.

SNR of the magneto-acoustic signal is significantly improved by the coded excitation than the pulse excitation, the reconstructed image of the front and back boundary of the pork can be seen clearly under the 7, 15, 31, 63, 127 bit M-sequence-coded excitations.

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References

  1. Roth BJ (2011) The role of magnetic forces in biology and medicine. Exp Biol Med 236(2):132–137

    Article  CAS  Google Scholar 

  2. Emerson JF, Chang DB (2013) Electromagnetic acoustic imaging. IEEE Trans Ultrason Ferroelectr Freq Control 60(2):364–372

    Article  PubMed  Google Scholar 

  3. Haemmerich D, Schutt DJ, Wright AW, Webster JG, Mahvi DM (2009) Electrical conductivity measurement of excised human metastatic liver tumours before and after thermal ablation. Physiol Meas 30(5):459–466

    Article  PubMed  PubMed Central  Google Scholar 

  4. Gabriel S, Lau RW, Gabriel C (1996) The dielectric properties of biological tissues: II. Measurements in the frequency range 10 Hz to 20 GHz. Phys Med Biol 41(11):2251–2269

    Article  CAS  PubMed  Google Scholar 

  5. Surowiec AJ, Stuchly SS, Barr JB, Swarup A (1988) Dielectric properties of breast carcinoma and the surrounding tissues. IEEE Trans Biomed Eng 35(4):257–263

    Article  CAS  PubMed  Google Scholar 

  6. Towe BC, Islam MR (1988) A magneto-acoustic method for the noninvasive measurement of bioelectric currents. IEEE Trans Biomed Eng 35(10):892–894

    Article  CAS  PubMed  Google Scholar 

  7. Bradley JR, Basser PJ, Wikswo JP (1994) A theoretical model for magneto-acoustic imaging of bioelectric currents. IEEE Trans Biomed Eng 41(8):723–728

    Article  Google Scholar 

  8. Aliroteh MS, Scott G, Arbabian A (2014) Frequency-modulated magneto-acoustic detection and imaging. Electron Lett 50(11):790–792

    Article  Google Scholar 

  9. Wen H, Shah J, Balaban RS (1998) Hall effect imaging. IEEE Trans Biomed Eng 45(1):119–124

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Leo M, Hu G, He B (2014) Magnetoacoustic tomography with magnetic induction for high-resolution bioimepedance imaging through vector source reconstruction under the static field of MRI magnet. Med Phys 41(2):131–134

    Google Scholar 

  11. Yu K, Shao Q, Ashkenazi S, Bischof JC, He B (2016) In vivo electrical conductivity contrast imaging in a mouse model of cancer using high-frequency magnetoacoustic tomography with magnetic induction (hfMAT-MI). IEEE Trans Med Imaging 35(10):2301–2311

    Article  CAS  Google Scholar 

  12. Li X, Yu K, He B (2016) Magnetoacoustic tomography with magnetic induction (MAT-MI) for imaging electrical conductivity of biological tissue: a tutorial review. Phys Med Biol 61(18):R249

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Graslandmongrain P, Mari JM, Chapelon JY, Lafon C (2014) Lorentz force electrical impedance tomography. IRBM 34(4–5):357–360

    Google Scholar 

  14. Zhang S, Zhou X, Yin T, Liu Z (2017) Magneto-acoustic imaging by continuous-wave excitation. Med Biol Eng Comput 55(4):595–607

    Article  Google Scholar 

  15. Zhang S, Zhou X, Ma R, Yin T, Liu Z (2015) A study on locating the sonic source of sinusoidal magneto-acoustic signals using a vector method. Biomed Mater Eng 26(s1):1177–1184

    Google Scholar 

  16. Renzhiglova E, Ivantsiv V, Xu Y (2010) Difference frequency magneto-acousto-electrical tomography (DF-MAET): application of ultrasound-induced radiation force to imaging electrical current density. IEEE Trans Ultrason Ferroelectr Freq Control 57(11):2391–2402

    Article  PubMed  Google Scholar 

  17. Zhang S et al (2018) Research on barker coded excitation method for magneto-acoustic imaging. Biomed Signal Process Control 39:169–176

    Article  Google Scholar 

  18. Colone F, Falcone P, Bongioanni C, Lombardo P (2012) WiFi-based passive bistatic radar: data processing schemes and experimental results. IEEE Trans Aerosp Electron Syst 48(2):1061–1079

    Article  Google Scholar 

  19. Jibiki T (2001) Coded excitation medical ultrasound imaging. Jap J Med Phys 21(3):136–141

    Google Scholar 

  20. Golomb SW, Gong G (2005) Signal Design for Good Correlation: For Wireless Communication, Cryptography, and Radar. Cambridge University Press, U.K, pp 117–161

    Book  Google Scholar 

  21. Xu Y, He B (2005) Magnetoacoustic tomography with magnetic induction (MAT-MI). IEEE Trans Med Imaging 29(10):3173–3176

    Google Scholar 

  22. Hinich MJ (1962) A model for a self-adapting filter. Inf Control 5(3):185–203

    Article  Google Scholar 

  23. Fu J, Wei G, Huang Q, Ji F, Feng Y (2014) Barker coded excitation with linear frequency modulated carrier for ultrasonic imaging. Biomed Signal Process Control 13(1):306–312

    Article  Google Scholar 

  24. Pye SD, Wild SR, Mcdicken WN (1992) Adaptive time gain compensation for ultrasonic imaging. Ultrasound Med Biol 18(2):205–212

    Article  CAS  PubMed  Google Scholar 

  25. Marcolin MA, Padberg F (2007) Transcranial brain stimulation for treatment in mental disorders. Adv Biol Psychiatry 23:204–225

    Google Scholar 

  26. Feng X, Gao F, Kishor R, Zheng Y (2014) Coexisting and mixing phenomena of thermoacoustic and magnetoacoustic waves in water. Sci Rep 5:1–10

    Google Scholar 

  27. Mariappan L, Li X, He B (2011) B-scan based acoustic source reconstruction for magnetoacoustic tomography with magnetic induction (MAT-MI). IEEE Trans Biomed Eng 58(3):713–720

    Article  PubMed  Google Scholar 

  28. Benedetto J, Konstantinidis I, Rangaswamy M (2009) Phase-coded waveforms and their design. IEEE Signal Process Mag 26(1):22–31

    Article  Google Scholar 

  29. Yu M, Zhang C, Yu G (2014) Research on a new waveform based on MAC sequence. In: 2014 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), Guilin, China, AUG, pp 456–460

  30. Rihaczek AW, Golden RM (1971) Range sidelobe suppression for Barker codes. IEEE Trans Aerosp Electron Syst 7(6):1087–1092

    Article  Google Scholar 

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Acknowledgements

Z Liu would like to thank Dr. Cheng Yi for his assistance in editing the language of this manuscript.

Funding

This study was supported by the Grants from the National Natural Foundation of China (61501523, 81772004), CAMS Initiative for Innovative Medicine (2017-I2M- 3-020), and National Natural Foundation of Tianjin (17JCZDJC32400).

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Authors and Affiliations

  1. Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China

    Shunqi Zhang, Ren Ma, Tao Yin & Zhipeng Liu

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  1. Shunqi Zhang
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  2. Ren Ma
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  3. Tao Yin
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  4. Zhipeng Liu
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Correspondence to Zhipeng Liu.

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Zhang, S., Ma, R., Yin, T. et al. M-sequence-coded excitation for magneto-acoustic imaging. Med Biol Eng Comput 57, 1059–1067 (2019). https://doi.org/10.1007/s11517-018-1941-x

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  • DOI: https://doi.org/10.1007/s11517-018-1941-x

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