Data-efficient extraction of optical properties from 3D Monte Carlo TPSFs using Bi-LSTM transfer learning
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866908960051691520 |
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| author | Aghili, Joubine Imbach, Rémi Pallarès, Anne Schmitt, Philippe Uhring, Wilfried |
| author_facet | Aghili, Joubine Imbach, Rémi Pallarès, Anne Schmitt, Philippe Uhring, Wilfried |
| contents | Time-Resolved Spectroscopy (TRS) is a powerful modality for non-invasive characterization of turbid media. However, extracting optical properties, absorption $μ_a$ and reduced scattering $μ_s'$, from 3D stochastic measurements remains computationally expensive for real-time applications. In this paper, we propose a data-efficient, physics-informed transfer learning strategy using a Bidirectional Long Short-Term Memory (Bi-LSTM) network. By leveraging a fast deterministic solver to establish a physical prior before fine-tuning on a restricted set of 3D Monte Carlo simulations, our model successfully bridges the analytical-to-stochastic domain gap. The proposed method eliminates the systematic bias of analytical models while maintaining a competitive error with near-instantaneous inference time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_11437 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Data-efficient extraction of optical properties from 3D Monte Carlo TPSFs using Bi-LSTM transfer learning Aghili, Joubine Imbach, Rémi Pallarès, Anne Schmitt, Philippe Uhring, Wilfried Numerical Analysis Computational Physics Time-Resolved Spectroscopy (TRS) is a powerful modality for non-invasive characterization of turbid media. However, extracting optical properties, absorption $μ_a$ and reduced scattering $μ_s'$, from 3D stochastic measurements remains computationally expensive for real-time applications. In this paper, we propose a data-efficient, physics-informed transfer learning strategy using a Bidirectional Long Short-Term Memory (Bi-LSTM) network. By leveraging a fast deterministic solver to establish a physical prior before fine-tuning on a restricted set of 3D Monte Carlo simulations, our model successfully bridges the analytical-to-stochastic domain gap. The proposed method eliminates the systematic bias of analytical models while maintaining a competitive error with near-instantaneous inference time. |
| title | Data-efficient extraction of optical properties from 3D Monte Carlo TPSFs using Bi-LSTM transfer learning |
| topic | Numerical Analysis Computational Physics |
| url | https://arxiv.org/abs/2604.11437 |