ORIGINAL RESEARCH article
Front. Photonics
Sec. Biophotonics
Volume 6 - 2025 | doi: 10.3389/fphot.2025.1634102
This article is part of the Research TopicAI-Empowered BiophotonicsView all articles
Physics inspired Neural network for optical property retrieval in from diffuse reflectance
Provisionally accepted
- 1The Netherlands Cancer Institute (NKI), Amsterdam, Netherlands
- 2Universiteit Twente, Enschede, Netherlands
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Optical property retrieval in diffuse reflectance imaging, like diffuse reflectance spectroscopy (DRS) and hyperspectral imaging (HSI), often involves fitting measured spectra to analytical solutions using approximations such as Diffusion Theory (DT). This method, while accurate, is not always generalizable due to the assumptions inherent in DT and results in non-unique solutions for optical properties and physiological parameters. In addition, it is computationally intensive. Physics-inspired deep learning offers generalizable data descriptions guided by physical principles but requires extensive labelled data, which is hard to obtain, especially in medical contexts. We propose a deep learning approach to retrieve physiological parameters from DRS and HSI spectra using DT-simulated training data. The DT-simulated data is synthesised using a range for the optical properties: Blood Volume Fraction (BVF), Saturation, Water Fat ratio (WFR), average blood vessel radius (R), scattering amplitude (SA), and scattering slope (SL). The range for these parameters we have extracted from literature. Our feed-forward neural network achieved median relative errors of 4% and 2% for DRS and HSI, respectively.
Keywords: Diffuse reflectance spectroscopy, hyperspectral imaging, optical property retrieval, Diffusion theory, deep learning, Physiological parameters, Simulated training data, biomedical optics
Received: 23 May 2025; Accepted: 01 Sep 2025.
Copyright: © 2025 Witteveen, Natali, Ruers and Dashtbozorg. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Mark Witteveen, The Netherlands Cancer Institute (NKI), Amsterdam, Netherlands
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