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Photoacoustic Fingerprinting for Robust Molecular Imaging.

Colton McGarraugh1, Luca Menozzi1, Rui Yao1

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Photoacoustic fingerprinting (PAF) enhances molecular imaging by treating spectral unmixing as a fingerprint recognition problem. This AI-driven approach offers robust quantification, overcoming limitations of conventional methods in complex biological samples.

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Area of Science:

  • Biomedical Imaging
  • Molecular Imaging
  • Photoacoustics

Background:

  • Quantitative molecular imaging in photoacoustics is challenged by spectral unmixing limitations.
  • Conventional methods suffer from spectral overlap, noise, and unknown fluence, introducing bias.

Purpose of the Study:

  • Introduce photoacoustic fingerprinting (PAF) to reframe spectral unmixing as a fingerprint recognition problem.
  • Develop a robust framework for accurate molecular quantification in photoacoustic imaging.

Main Methods:

  • Interpreted multispectral signals as high-dimensional fingerprints encoding molecular composition and distortions.
  • Utilized a recurrent neural network trained on synthetic data with realistic variations.
  • Applied PAF to simulations, phantom studies, ex vivo mouse livers, and human arteries.

Main Results:

  • PAF demonstrated accurate and robust quantification, outperforming non-negative least squares, especially for spectrally overlapping chromophores.
  • Improved molecular specificity in phantom studies, correctly localizing collagen and recovering water contrast.
  • Detected lipid accumulation in steatotic livers and identified molecular signatures in human arteries.

Conclusions:

  • PAF is a generalizable framework for label-free molecular imaging.
  • Represents a significant advancement toward quantitative photoacoustic diagnostics.