Speaker
Description
In photoacoustic tomography, biological tissue is illuminated with a short laser pulse of near infrared light. The absorbed energy creates a local pressure increase that propagates through the tissue, governed by the acoustic wave equation and can be measured on the boundary. From this measured time-series the initial pressure in the tissue is reconstructed, providing valuable information on local structures. Subsequently, it is possible to recover the quantitative optical parameters of absorption and scattering. Correct recovery of the optical parameters would provide valuable functional and biological information.
Solving both the acoustic and optical inverse problem comes with challenges. From limited-view geometries to modelling errors and uncertainties. The above challenges can be effectively mitigated by training a learned reconstruction method, but three crucial ingredients are necessary: a learned method with good generalisability for out-of-distribution data, a computationally fast model to allow for feasible training and inference times, and finally reference data for the training procedure.
Here, we use a learned model-based iterative reconstruction. A novel fast FFT based approach to solve the acoustic problem in circular geometries. And finally, training and evaluation using a digital twin providing a link between experimental and simulated data. Reconstructions are presented for experimental data and the digital twin.
Bibliography
@article{manninen2025towards,
title={Towards robust quantitative photoacoustic tomography via learned iterative methods},
author={Manninen, Anssi and Gr{\"o}hl, Janek and Lucka, Felix and Hauptmann, Andreas},
journal={arXiv preprint arXiv:2510.27487},
year={2025}
}
@article{hauptmann2026fast,
title={Fast algorithms enabling optimization and deep learning for photoacoustic tomography in a circular detection geometry},
author={Hauptmann, Andreas and Kunyansky, Leonid and Poimala, Jenni},
journal={SIAM Journal on Imaging Sciences},
year={2026}
}
@article{grohl2025digital,
title={Digital twins enable full-reference quality assessment of photoacoustic image reconstructions},
author={Gr{\"o}hl, Janek and Kunyansky, Leonid and Poimala, Jenni and Else, Thomas R and Di Cecio, Francesca and Bohndiek, Sarah E and Cox, Ben T and Hauptmann, Andreas},
journal={The Journal of the Acoustical Society of America},
volume={158},
number={1},
pages={590--601},
year={2025},
publisher={AIP Publishing}
}
