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PreprintarXiv licencearXiv

Multi-Spectral QPAT with Frequency Averaged Measurements

  • Yunhao Sun
  • Yang Yang
  • Yunan Yang
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Abstract

We study some inverse problems in a frequency-averaged model for quantitative photoacoustic tomography, in which the optical parameters are modeled by a finite number of spectral functions and spatial coefficients, and the measurements are given by internal observables averaged over a spectral bandwidth. Using multiple measurements generated by suitable complex geometrical optics solutions, we establish uniqueness and Lipschitz-type stability for the associated linearized inverse problem for diffusion and absorption recovery. This linearized stability further yields local Hölder-type stability for the nonlinear inverse problem via an abstract linear-to-nonlinear argument.

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Cite

BibTeXRIS
BibTeX
@misc{sun2026multispectral,
  title = {{Multi-Spectral QPAT with Frequency Averaged Measurements}},
  author = {Yunhao Sun and Yang Yang and Yunan Yang},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.12232},
  eprint = {2609.12232},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.12232},
}
RIS
TY  - UNPB
TI  - Multi-Spectral QPAT with Frequency Averaged Measurements
AU  - Yunhao Sun
AU  - Yang Yang
AU  - Yunan Yang
PY  - 2026
DA  - 2026/09/10
PB  - arXiv
DO  - 10.48550/arxiv.2609.12232
UR  - https://arxiv.org/abs/2609.12232
AB  - We study some inverse problems in a frequency-averaged model for quantitative photoacoustic tomography, in which the optical parameters are modeled by a finite number of spectral functions and spatial coefficients, and the measurements are given by internal observables averaged over a spectral bandwidth. Using multiple measurements generated by suitable complex geometrical optics solutions, we establish uniqueness and Lipschitz-type stability for the associated linearized inverse problem for diffusion and absorption recovery. This linearized stability further yields local Hölder-type stability for the nonlinear inverse problem via an abstract linear-to-nonlinear argument.
ER  -