Personalized Fluence Correction for Photoacoustic Imaging Using Ultrasound-Based Tissue Segmentation

Schillinger M, Schlereth M, Wachter F, Bühler A, Gillet J, Zahnd G, Breininger K (2026)

Publication Type: Conference contribution, Abstract of a poster

Publication year: 2026

Journal

Proceedings of SPIE Society of Photo-optical Instrumentation Engineers (SPIE)

Publisher: SPIE

Book Volume: 13851

Conference Proceedings Title: Proceedings of SPIE - The International Society for Optical Engineering

Event location: San Francisco, CA US

ISBN: 9781510696150

DOI: 10.1117/12.3080335

Abstract

Photoacoustic imaging (PAI) suffers from light fluence decay and wavelength-dependent attenuation, which introduce depth- and wavelength-related biases that limit its clinical reliability. We propose a subject-specific, physics-informed framework for fluence correction in PAI using anatomical information from co-registered ultrasound scans. A deep neural network segments key tissues to construct individualized digital twins consisting of spatially varying optical parameter maps. Monte Carlo simulations generate personalized in-silico fluence estimates, which are used to correct the in-vivo initial pressure image. Epidermal melanin content is estimated from the epidermis signal and incorporated into the model to account for inter-subject variation in skin pigmentation. Our method significantly reduces depth-related bias in clinical datasets and maintains or improves diagnostic performance, as shown in a peripheral artery disease classification task. By combining anatomical segmentation with personalized optical modeling, our approach enables subject-specific fluence correction at scale, making it suitable for automated processing of large clinical PAI datasets. Ongoing work investigates whether incorporating melanin content estimation can also help mitigate skin tone–related bias, a concern increasingly recognized in clinical PAI studies.

Authors with CRIS profile

Moritz Schillinger Department Artificial Intelligence in Biomedical Engineering (AIBE) Maja Schlereth Department Artificial Intelligence in Biomedical Engineering (AIBE) Felix Wachter Lehrstuhl für Kinder- und Jugendmedizin Adrian Bühler Department of Paediatrics and Adolescent Medicine Katharina Breininger Department Artificial Intelligence in Biomedical Engineering (AIBE)

Involved external institutions

iThera Medical GmbH DE Germany (DE)

How to cite

APA:

Schillinger, M., Schlereth, M., Wachter, F., Bühler, A., Gillet, J., Zahnd, G., & Breininger, K. (2026, March). Personalized Fluence Correction for Photoacoustic Imaging Using Ultrasound-Based Tissue Segmentation. Poster presentation at Photons Plus Ultrasound: Imaging and Sensing 2026, San Francisco, CA, US.

MLA:

Schillinger, Moritz, et al. "Personalized Fluence Correction for Photoacoustic Imaging Using Ultrasound-Based Tissue Segmentation." Presented at Photons Plus Ultrasound: Imaging and Sensing 2026, San Francisco, CA Ed. Alexander A. Oraevsky, Lihong V. Wang, SPIE, 2026.

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