Evaluation of analytical models to estimate depth of fluorescence objects in biological media

Performing a ratiometric analysis of the fluorescence signals noninvasively measured at two different wavelengths can provide depth estimates of subsurface inner structures in a simple and fast manner, allowing for real-time applications in clinical settings. This can be done using the initially proposed single-excitation-multiple-emission wavelengths approach or by implementing a modified multiple-excitation-single-emission approach; the latter being sometimes preferred due to the larger variation of tissue optical properties at shorter wavelengths. However, previous works validating this method with Monte Carlo (MC) simulations, experiments on tissue-mimicking phantoms, and in vivo measurements on small animal models have reported different degrees of accuracy.

We tested the influence of factors not generally accounted for in the analytical model used for data interpretation (e.g., tissue geometry and boundaries, inclusion size and shape, and spectral characteristics of the excitation source). To address these limitations, we developed an improved theoretical framework that explicitly accounts for these factors during data interpretation.

Model validation was carried out with MC simulations and with phantom experiments using indocyanine green as the fluorescence contrast agent. The aimed tissue optical properties were those characteristic of the prostate in a wide range of wavelengths (from 550 to 900 nm).

The aforementioned factors have a strong influence when changing the original single-excitation-multiple-emission approach to a multiple-excitation-single-emission approach. Though this might make the latter a less preferable method, the low variability of the optical properties in the multiple emission approach (as it happens with prostate tissue) negatively impacts the depth reconstruction process.

Regardless of the ratiometric strategy employed, accurate depth estimation requires that the theoretical model closely replicate the experimental conditions. Careful matching of model assumptions to the measurement environment is essential to achieve reliable data interpretation.