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The important convolution properties include width, area, differentiation, and integration properties.
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Multispectral Optoacoustic Tomography for Functional Imaging in Vascular Research
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Optoacoustic inversion via convolution kernel reconstruction in the paraxial approximation and beyond.

O Melchert1, M Wollweber1, B Roth1

  • 1Hannover Centre for Optical Technologies (HOT), Interdisciplinary Research Centre of the Leibniz Universität Hannover, Nienburger Str. 17, D-30167 Hannover, Germany.

Photoacoustics
|December 5, 2018
PubMed
Summary

This study presents a novel method for reconstructing initial stress profiles from optoacoustic signals. The technique accurately inverts complex signal shapes, advancing optoacoustic imaging capabilities.

Keywords:
Convolution kernel reconstructionOptoacousticsTissue phantomVolterra integral equation of the second kind

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Area of Science:

  • Biomedical Optics
  • Acoustic Physics
  • Mathematical Modeling

Background:

  • Optoacoustic imaging relies on converting light absorption into acoustic waves.
  • Accurate reconstruction of initial stress profiles from measured signals is crucial for quantitative analysis.
  • Signal distortion during propagation complicates the inversion process.

Purpose of the Study:

  • To develop a robust numerical method for inverting optoacoustic signals to initial stress profiles.
  • To address signal shape transformation due to wave propagation in the paraxial approximation.
  • To validate the method with synthetic data and experimental measurements.

Main Methods:

  • Modeling signal propagation using a Volterra integral equation of the second kind.
  • Expanding the optoacoustic convolution kernel using Fourier series for system characterization.
  • Employing a Picard-Lindelöf correction scheme for source reconstruction.
  • Validating with synthetic data and experimental measurements on tissue phantoms.

Main Results:

  • A sequence of expansion coefficients effectively characterizes the 'apparatus' setup.
  • The developed method successfully reconstructs initial stress profiles from simulated and experimental optoacoustic signals.
  • The approach demonstrates feasibility for signals beyond the paraxial approximation.

Conclusions:

  • The proposed numerical inversion protocol provides an effective means for optoacoustic signal reconstruction.
  • This method enhances the quantitative capabilities of optoacoustic imaging, particularly for complex signal behaviors.
  • The study validates the approach using both synthetic data and experimental measurements on melanin-doped phantoms.