Visible spatial frequency domain imaging with a digital light microprojector

Abstract

There is a need for cost effective, quantitative tissue spectroscopy and imaging systems in clinical diagnostics and pre-clinical biomedical research. A platform that utilizes a commercially available light-emitting diode (LED) based projector, cameras, and scaled Monte Carlo model for calculating tissue optical properties is presented. These components are put together to perform spatial frequency domain imaging (SFDI), a model-based reflectance technique that measures and maps absorption coefficients (μa) and reduced scattering coefficients (μs') in thick tissue such as skin or brain. We validate the performance of the flexible LED and modulation element (FLaME) system at 460, 530, and 632 nm across a range of physiologically relevant μa values (0.07 to 1.5 mm-1) in tissue-simulating intralipid phantoms, showing an overall accuracy within 11% of spectrophotometer values for μa and 3% for μs'. Comparison of oxy- and total hemoglobin fits between the FLaME system and a spectrophotometer (450 to 1000 nm) is differed by 3%. Finally, we acquire optical property maps of a mouse brain in vivo with and without an overlying saline well. These results demonstrate the potential of FLaME to perform tissue optical property mapping in visible spectral regions and highlight how the optical clearing effect of saline is correlated to a decrease in μs' of the skull.

Fig. 1

(a) Diagram of FLaME experimental imaging setup. All components were controlled using LabVIEW software on a personal computer. (b) Expanded view of the AAXA M2 microprojector.

Fig. 2

The nonlinear input–output intensity curve (gamma function) of the projector (middle) distorts a sine wave image input from the computer (top left) to appear as a square wave (top right). Using the projector response function as a look-up-table, the computer image input was adjusted such that the actual projected image (bottom left) is sinusoidal when detected on spectralon (Labsphere) (bottom right). Cross-section intensity profiles of the images are shown in red under the images to better illustrate the projection transformation.

Fig. 3

(a) Absorption spectra of oxy-hemoglobin (${\mathrm{HbO}}_2$) and deoxy-hemoglobin (Hb). Vertical lines highlight 460, 530, and 632 nm absorption features. (b) Scaled Monte Carlo predictions of diffuse reflectance as a function of spatial frequency with ${\mu }_{\mathrm{s}}^{\prime }$ constant at $1{\mathrm{mm}}^{-1}$ and ${\mu }_{\mathrm{a}}$ increasing by increments of $0.03{\mathrm{mm}}^{-1}$ from 0.01 to $0.1{\mathrm{mm}}^{-1}$. (c) Scaled Monte Carlo predictions of diffuse reflectance as a function of spatial frequency with ${\mu }_{\mathrm{s}}^{\prime }$ constant at $1{\mathrm{mm}}^{-1}$ and ${\mu }_{\mathrm{a}}$ increasing by increments of $0.3{\mathrm{mm}}^{-1}$ from 0.1 to $1{\mathrm{mm}}^{-1}$.

Fig. 4

(a) Example plot of $R_{\mathrm{d}}$ at 0, 0.05, 0.1, 0.2, and $0.4{\mathrm{mm}}^{-1}$ spatial frequencies for three concentrations of napthol green B at 460 nm. Lines are the least-squares fits of the Monte Carlo forward model of $R_{\mathrm{d}}$ as a function of ${\mu }_{\mathrm{a}}$ and ${\mu }_{\mathrm{s}}^{\prime }$. Standard deviation bars of the data $<0.006$ and are not shown for clarity. (b) Plot of ${\mu }_a$ spectra of three concentrations of napthol green B. Lines are from the spectrophotometer and points are from FLaME measurements. Standard deviation bars of the FLaME data $<0.03$ and are not shown for clarity. (c) Plot of ${\mu }_{\mathrm{s}}^{\text{'}}$ spectra of three concentrations of napthol green B in 1% Intralipid solution. Line is the expected scattering of 1% Intralipid from Mie theory. Standard deviation bars of the FLaME data $<0.07$ and are not shown for clarity. (d) Plot of data from (b) showing an outlier in FLaME data when the expected ${\mu }_{\mathrm{a}}=1.5{\mathrm{mm}}^{-1}$.

Fig. 5

Blood phantom spectra acquired using a conventional transmission spectrophotometer (thick green line), FLaME (red dots), and the best fit of the spectrophotometer data to hemoglobin (dashed black line). Standard deviations for FLaME data are ${\sigma }_{460\mathrm{nm}}=0.05{\mathrm{mm}}^{-1}$, ${\sigma }_{530\mathrm{nm}}=0.04{\mathrm{mm}}^{-1}$, and ${\sigma }_{632\mathrm{nm}}=0.002{\mathrm{mm}}^{-1}$. They are not shown for clarity.

Fig. 6

A summary of the SFDI process in a mouse brain at 530 nm: (Top row) Raw camera snapshots of mouse brain with projected spatial frequencies. (Middle row) Cropped and calibrated diffuse reflectance images of the mouse brain as a function of spatial frequency. (Bottom row) Pixel-by-pixel fitted ${\mu }_{\mathrm{a}}$ and ${\mu }_{\mathrm{s}}^{\prime }$ maps of the mouse brain at 530 nm. The midsagittal vein, a prominent vessel on the midline surface of the brain, can be seen on the ${\mu }_{\mathrm{a}}$ map. The sagittal skull suture is marked by increased scattering on the ${\mu }_{\mathrm{s}}^{\prime }$ map.

Fig. 7

Adding a saline well over the skull, as commonly done in optical intrinsic signal imaging causes a significant ($p<0.05$) decrease in the detected scattering when compared to imaging a dry skull. (a and b) Images of the dry and saline-soaked regions of interest (ROI). Plots of diffuse reflectance as a function of spatial frequency for the dry skull (c) and the wet skull (d). (e) A comparison of fitted ${\mu }_{\mathrm{a}}$ values for a dry skull (squares) versus a wet skull (circles). (f) A comparison of fitted ${\mu }_{\mathrm{s}}^{\prime }$ values for a dry skull (squares) versus a wet skull (circles). All error bars are standard deviation of the ROI pixels.