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Novel data types for frequency-domain diffuse optical spectroscopy and imaging of tissues: characterization of sensitivity and contrast-to-noise ratio for absorption perturbations.

Angelo SassaroliGiles BlaneySergio Fantini
Published in: Biomedical optics express (2023)
In frequency-domain (FD) diffuse optics it is known that the phase of photon-density waves ( ϕ ) has a stronger deep-to-superficial sensitivity ratio to absorption perturbations than the alternate current (AC) amplitude, or the direct current intensity (DC). This work is an attempt to find FD data types that feature similar or even better sensitivity and/or contrast-to-noise for deeper absorption perturbations than phase. One way is to start from the definition of characteristic function (X t ( ω )) of the photon's arrival time ( t ) and combining the real ( ℜ ( X t ( ω ) ) = A C D C c o s ( ϕ ) ) and imaginary parts ( ℑ [ X t ( ω ) ] = A C D C s i n ( ϕ ) ) with phase to yield new data types. These new data types enhance the role of higher order moments of the probability distribution of the photon's arrival time t . We study the contrast-to-noise and sensitivity features of these new data types not only in the single-distance arrangement (traditionally used in diffuse optics), but we also consider the spatial gradients, which we named dual-slope arrangements. We have identified six data types that for typical values of the optical properties of tissues and depths of interest, have better sensitivity or contrast-to-noise features than phase data and that can be used to enhance the limits of imaging of tissue in FD near infrared spectroscopy (NIRS). For example, one promising data type is ϕ - ℑ [ X t ( ω ) ] which shows, in the single-distance source-detector arrangement, an increase of deep-to-superficial sensitivity ratio with respect to phase by 41% and 27% at a source-detector separation of 25 and 35 mm, respectively. The same data type also shows an increase of contrast-to noise up to 35% with respect to phase when the spatial gradients of the data are considered.
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