Tutorial on methods for estimation of optical absorption and scattering properties of tissue

Abstract

Significance: The estimation of tissue optical properties using diffuse optics has found a range of applications in disease detection, therapy monitoring, and general health care. Biomarkers derived from the estimated optical absorption and scattering coefficients can reflect the underlying progression of many biological processes in tissues.

Aim: Complex light-tissue interactions make it challenging to disentangle the absorption and scattering coefficients, so dedicated measurement systems are required. We aim to help readers understand the measurement principles and practical considerations needed when choosing between different estimation methods based on diffuse optics.

Approach: The estimation methods can be categorized as: steady state, time domain, time frequency domain (FD), spatial domain, and spatial FD. The experimental measurements are coupled with models of light-tissue interactions, which enable inverse solutions for the absorption and scattering coefficients from the measured tissue reflectance and/or transmittance.

Results: The estimation of tissue optical properties has been applied to characterize a variety of ex vivo and in vivo tissues, as well as tissue-mimicking phantoms. Choosing a specific estimation method for a certain application has to trade-off its advantages and limitations.

Conclusion: Optical absorption and scattering property estimation is an increasingly important and accessible approach for medical diagnosis and health monitoring.

Keywords: diffuse optics; optics; photonics; tissue optical properties; tissue optics.

Fig. 1

Absorption spectra of the main endogenous tissue chromophores and the skin scattering spectrum. Absorption spectra include: oxygenated (red line) and deoxygenated (blue line) blood with 150 g hemoglobin per liter;^, melanosome (green line), of which values beyond 1100 nm are extrapolated from the values at shorter wavelengths; purified pig fat (cyan line) and filtered human fat (cyan dots);^– and pure water (black line).^,, The skin scattering spectrum (purple line) was modeled by a linear combination of the Mie and Rayleigh expressions, using the parameters in Ref.  ${\mu }_s^{\prime }$ values beyond 1000 nm are extrapolated using the modeled expression.

Fig. 2

Relationships among different domains [steady state, time domain (TD), time frequency domain (FD), spatial domain, and spatial FD] for tissue optical property estimation. The steady-state measures diffuse transmittance ($T_d$) and reflectance ($R_d$), which are the integrals of $T_d(\rho ,t)$ and $R_d(\rho ,t)$ in both the time ($t$) and spatial ($\rho$) domains. The real domain and FD are related by FT and inverse Fourier transform (${\mathrm{FT}}^{-1}$). Reproduced with permission from Ref. .

Fig. 3

Schematics of methods for steady-state measurements. A single-integrating sphere can be used to measure (a) the reflectance and (b) the transmittance. Alternatively, (c) two spheres can be configured such that the reflectance and transmittance are measured simultaneously. The illumination beam in this figure is at 0 deg, but 8 deg incident angle is frequently used to include the specular reflection. Reflectance and transmittance spectra can be measured when the illumination is broadband and spectrometers are used as detectors. Adapted with permission from Ref.  © Optica.

Fig. 4

Schematic of a time-correlated single-photon counting (TCSPC)-based TD measurement system. A picosecond low-intensity pulsed monochromatic light source is used for illumination. Light is delivered and collected using fibers. The collected signal is detected by a hybrid photomultiplier detector and processed by a TCSPC module. The pulsed monochromatic illumination can be achieved by a picosecond low-intensity pulsed laser filtered by a tuneable filter to select the illumination wavelength. The intensity of illumination pulse is attenuated by a variable attenuator, so that no more than one photon reaches the detector for one pulse, and the detected photon has its arrival time detected, which follows the distribution of time-of-flight light spends in the sample. By repetitively delivering pulses and recording arrival time, the collected histogram is $R_d(\rho ,t)$ convolved with the instrument response function (IRF). Reproduced with permission from Ref. .

Fig. 5

Schematic of a FD single-distance multifrequency measurement system based on a network analyzer. The network analyzer generates RF current, a fraction of which is fed back through a directional bridge to the reference channel of the network analyzer to determine the amplitude and phase of the reference signal. The RF signal is superimposed with the DC signal by a bias tee, in order to modulate the laser diode. Light from the laser diode is delivered and collected using fibers. The collected light signal is converted to RF signal by a detector and is sent back to the network analyzer to extract its amplitude and phase. More laser diodes can be included in the schematic, connected in parallel with switches, for more illumination wavelengths. Adapted with permission from Ref.  © Optica.

Fig. 6

Schematics of spatial domain (SD) measurement systems. The multidistance measurement in (a) uses a broadband light source delivered by fiber optics to the sample, and $R_d(\rho )$ is spatially sampled by eight fibers and is spectrally resolved by an imaging spectrograph. (b) A monochromatic light source is used to illuminate the sample, and $R_d(\rho )$ is spatially resolved by a wide-field camera to detect pixel-wisely. Cross polarization can be implemented by linear polarizers, to minimize specular reflection. Adapted with permission from Ref.  © Optica.

Fig. 7

Schematic of the spatial frequency domain imaging (SFDI) system. A monochromatic sinusoidal pattern is projected onto the sample by a projector. The reflected light is detected by a camera. Cross polarization is implemented by linear polarizers to minimize specular reflection. Reproduced with permission from Refs.  and .