Abstract
The study aims to preliminarily assess the spatial-frequency domain imaging (SFDI) method for early detection of malignant changes in the oral mucosa. Experiments were conducted on tissue-mimicking phantoms using an SFDI setup equipped with lasers (660 and 808 nm) and a spatial modulation module. The method provides two-dimensional maps of optical absorption and scattering coefficients, enabling detection and quantitative assessment of differences between phantom regions simulating healthy and cancerous tissue at depths up to 6 mm. The data obtained using the spatial-frequency domain imaging method were validated by corresponding measurements performed with diffuse reflectance spectroscopy and demonstrated a high level of agreement, with correlation coefficients of 0.998 at 660 nm and 0.994 at 808 nm, and no systematic bias between the methods. Preliminary results confirm the potential of SFDI for early diagnosis of oral mucosal cancer and emphasize the need for further development, including the creation of an in vivo imaging system for oral mucosa studies.
Keywords
spatial-frequency domain imaging, oral mucosal cancer, early diagnosis, optical properties of tissues, diffuse reflectance spectroscopy, noninvasive diagnostics
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For citation
Kolpakov A.V., Sokolova D.Yu., Semenova A.S., Muravskaya N.P. Assessment of the potential of spatial-frequency domain imaging for early diagnosis of oral mucosal cancer. Almanac of Modern Metrology. 2026; 46 (2): 111–116.
Financing
The authors declare that they received no external financial support for this study.
Contribution of the authors
A.V. Kolpakov: research management and organization. D.Yu. Sokolova and А.S. Semenova: processing and analysis of experimental results. N.P. Muravskaya: consulting and organizational support.
Conflict of interest
The authors declare that they have no potential conflict of interest related to the research presented in this article.
This work is licensed under a Creative Commons Attribution 4.0 License (CC BY 4.0)
©
Alexandr V. Kolpakov, Diana Yu. Sokolova, Аnastasiya S. Semenova, Natalia P. Muravskaya, 2026
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