Analysis and modeling of a system for optoacoustic tomography based on heterodyne optical interferometry

Authors

DOI:

https://doi.org/10.37537/rev.elektron.5.2.139.2021

Keywords:

optoacoustic, interferometer, electrical noise

Abstract

In this work, the source of the artifacts introduced in the images obtained with an optoacoustic tomography system based on the software-defined optoelectronics concept are analyzed and characterized. It is shown that the measured signals are affected both by the cylindrical geometry of the optical sensor and by electrical noise. The latter has well-defined frequencies within the spectrum caused by the electronics used in the heterodyning process of the ultrasound optical detector. A way to include these effects in simulated signals is proposed and the model is tested against measurements. The results of this work will allow the use of the deep learning technique to improve the quality of the images obtained with this type of tomographic systems.

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References

L. V. Wang and H. Wu, Biomedical Optics: Principles and Imaging. John Wiley & Sons, 2009.

A. Hauptmann and B. Cox, “Deep learning in photoacoustic tomography: current approaches and future directions,” Journal of Biomedical Optics, vol. 25, no. 11, pp. 1 – 46, 2020.

M. G. Gonzalez, E. Acosta, and G. Santiago, “Simple method to determine the resolution and sensitivity of systems for optoacoustic tomography,” Elektron, vol. 2, pp. 63–66, 2018.

A. F. Vidal, L. C. Brazzano, C. Matteo, P. Sorichetti, and M. G. Gonzalez, “Parametric modeling of wideband piezoelectric polymer sensors: design for optoacoustic applications,” Rev. Sci. Instrum., vol. 88, no. 9, p. 095004, 2017.

C. Tian, M. Pei, K. Shen, S. Liu, Z. Hu, and T. Feng, “Impact of system factors on the performance of photoacoustic tomography scanners,” Phys. Rev. Applied, vol. 13, p. 014001, 2020.

G. Paltauf, R. Nuster, and P. Burgholzer, “Characterization of integrating ultrasound detectors for photoacoustic tomography,” Journal of Applied Physics, vol. 105, 2009.

M. G. Gonzalez, L. Riobo, L. C. Brazzano, F. Veiras, P. Sorichetti, and G. Santiago, “Generation of sub-microsecond quasi-unipolar pressure pulses,” Ultrasonics, vol. 98, pp. 15–19, 2019.

S. Tzoumas, A. Rosenthal, C. Lutzweiler, D. Razansky, and V. Ntziachristos, “Spatiospectral denoising framework for multispectral optoacoustic imaging based on sparse signal representation,” Medical Physics, vol. 41, p. 113301, 2014.

J. Barry, E. Lee, and D. Messerschmitt, Digital communication. Springer Science and Business Media, 2012.

C. Dehner, I. Olefir, K. Chowdhury, D. Juestel, and V. Ntziachristos, “Deep learning based electrical noise removal enables high spectral optoacoustic contrast in deep tissue,” arXiv, 2021.

B. Cox, J. Laufer, S. Arridge, and P. Beard, “Quantitative spec- troscopic photoacoustic imaging: a review,” Journal of Biomedical Optics, vol. 17, p. 061202, 2012.

L. Zeng, D. Xing, H. Gu, D. Yang, S. Yang, and L. Xiang, “High antinoise photoacoustic tomography based on a modified filtered backprojection algorithm with combination wavelet,” Medical Physics, vol. 34, pp. 556–563, 2007.

R. Insabella, M. Gonzalez, R. Riobo, K. Hass, and F. Veiras, “Software-defined optoacoustic tomography,” Appl. Opt., vol. 59, pp. 706–711, 2020.

L. Riobo, F. Veiras, M. G. Gonzalez, M. T. Garea, and P. Sorichetti, “High-speed real-time heterodyne interferometry using software-defined radio,” Appl. Opt., vol. 57, no. 2, pp. 217–224, 2017.

A. Sharma, S. Kalva, and M. Pramanik, “A comparative study of continuous versus stop-and-go scanning in circular scanning photoacoustic tomography,” IEEE J. Sel. Top. Quantum Electron, vol. 25, no. 1, pp. 1–9, 2019.

M. Xu, Y. Xu, and L. Wang, “Time-domain reconstruction algorithms and numerical simulations for thermoacoustic tomography in various geometries,” IEEE Transactions on Biomedical Engineering, vol. 50, pp. 1086–1099, 2003.

A. Rosenthal, V. Ntziachristos, and D. Razansky, “Acoustic inversion in optoacoustic tomography: A review,” Current Medical Imaging Reviews, vol. 9, pp. 318–336, 2013.

L. Ding, X. Dean-Ben, and D. Razansky, “Real-time model-based inversion in cross-sectional optoacoustic tomography,” IEEE Trans Med Imaging, vol. 35, pp. 1883–1891, 2016.

A. Rosenthal, D. Razansky, and V. Ntziachristos, “Fast semi-analytical model-based acoustic inversion for quantitative optoacoustic tomography,” IEEE Trans Med Imaging, vol. 29, no. 6, pp. 1275–1285, 2010.

L. Hirsch, M. G. Gonzalez, and L. R. Vega, “On the robustness of model-based algorithms for photoacoustic tomography: comparison between time and frequency domains,” Rev. Sci. Instrum., vol. 92, p. 114901, 2021.

Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Transactions on Image Processing, vol. 13, no. 4, p. 600–612, Apr 2004.

Published

2021-12-15

Issue

Section

Bioengineering

How to Cite

[1]
R. M. Insabella and M. G. González, “Analysis and modeling of a system for optoacoustic tomography based on heterodyne optical interferometry”, Elektron, vol. 5, no. 2, pp. 94–99, Dec. 2021, doi: 10.37537/rev.elektron.5.2.139.2021.