Academic Journal
Neural Network-Based Depth Retrieval of Fluorescent Targets From Wide-Field Fluorescence Intensity Images in Scattering Tissues.
| Τίτλος: | Neural Network-Based Depth Retrieval of Fluorescent Targets From Wide-Field Fluorescence Intensity Images in Scattering Tissues. |
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| Συγγραφείς: | Harel M; Department of Physics, Ariel University, Ariel, Israel., Chacko N; Department of Physics, Ariel University, Ariel, Israel., Motiei M; Faculty of Engineering and the Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan, Israel., Azaria A; Department of Computer Science, Ariel University, Ariel, Israel., Ankri R; Department of Physics, Ariel University, Ariel, Israel.; Department of Applied Physics, Jerusalem College of Technology, Jerusalem, Israel. |
| Πηγή: | Journal of biophotonics [J Biophotonics] 2026 Jul; Vol. 19 (7), pp. e70325. |
| Τύπος έκδοσης: | Journal Article; Research Support, Non-U.S. Gov't |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Wiley-VCH Country of Publication: Germany NLM ID: 101318567 Publication Model: Print Cited Medium: Internet ISSN: 1864-0648 (Electronic) Linking ISSN: 1864063X NLM ISO Abbreviation: J Biophotonics Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Weinheim : Wiley-VCH |
| Ιατρικοί όροι (MeSH): | Image Processing, Computer-Assisted*/methods , Optical Imaging* , Scattering, Radiation* , Neural Networks, Computer*, Gold/chemistry ; Skin/diagnostic imaging ; Monte Carlo Method ; Humans ; Phantoms, Imaging |
| Περίληψη: | Fluorescence imaging (FI) is widely used in in vivo and cellular studies. However, accurate depth determination of fluorescent targets in vivo remains challenging due to strong photon scattering and absorption in biological tissues. In this study, we developed and validated a machine learning (ML)-based framework, supported by Monte Carlo (MC) simulations, for accurate depth estimation of near-infrared (NIR) fluorescent targets in turbid media. An optimized MC model was constructed to simulate NIR fluorescence photon propagation, generating datasets of fluorescence images corresponding to target depths between 0.1 and 1 cm. Experimental validation was conducted over a depth range of 0.1-0.7 cm using tissue-mimicking phantoms designed to replicate the optical properties of human skin, as well as on an in vivo model incorporating fluorescent gold nanostructures imaged with computed tomography (CT) and wide-field FI. The results were quantitatively validated using two performance metrics: rounded accuracy and root mean squared error (RMSE). (© 2026 The Author(s). Journal of Biophotonics published by Wiley‐VCH GmbH.) |
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| Grant Information: | Planning and Budgeting Committee of the Council for Higher Education of Israel; 704/24 Israel Science Foundation |
| Contributed Indexing: | Keywords: Monte Carlo simulation; fluorescence imaging; machine learning; multiplexed imaging; near‐infrared (NIR) |
| Substance Nomenclature: | 7440-57-5 (Gold) |
| Entry Date(s): | Date Created: 20260726 Date Completed: 20260726 Latest Revision: 20260813 |
| Update Code: | 20260814 |
| PubMed Central ID: | PMC13401773 |
| DOI: | 10.1002/jbio.70325 |
| PMID: | 42502922 |
| Βάση Δεδομένων: | MEDLINE |
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