Continuous wave spectroscopy with diffusion theory for quantification of optical properties: Comparison between multi-distance and multi-wavelength data fitting methods

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Abstract

Typically, continuous wave spectroscopy (CWS) can be used to accurately quantify biological tissue optical properties (μa and μs ′) by employing the diffuse reflectance information acquired at multiple source-detector separations (multi-distance). On the other hand, sample optical properties can also be obtained by fitting multi-wavelength light reflectance acquired at a single source detector separation to the diffusion theory equation. To date, multi-wavelength and multidistance methods have not yet been rigorously compared for their accuracy in quantification of the sample optical properties. In this investigation, we compared the accuracy of the two above-mentioned quantifying methods in the optical properties recovery. The liquid phantoms had μa between 0.004 and 0.011 mm−1 and μs ′ between 0.55 and 1.07 mm−1 whose optical properties mimic the human breast. Multi-distance data and multi-wavelength data were fitted to the same diffusion equation for consistency. The difference between benchmark μa and μs ′ and the fitted results, ΔError (ΔE) was used to evaluate the accuracy of the two methods. The results showed that either method yielded Δ E within 15–30 % when values were within certain limits to standard values applicable to μs ′ and μa for human adipose tissue. Both methods showed no significant differences in Δ E values. Our results suggest that both multidistance and multi-wavelength methods can yield similar reasonable optical properties in biological tissue with a proper calibration.

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Lin, Y. C., Lin, Z. F., Nioka, S., Chen, L. H., Tseng, S. H., & Chung, P. C. (2016). Continuous wave spectroscopy with diffusion theory for quantification of optical properties: Comparison between multi-distance and multi-wavelength data fitting methods. In Advances in Experimental Medicine and Biology (Vol. 923, pp. 337–343). Springer New York LLC. https://doi.org/10.1007/978-3-319-38810-6_44

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