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. 2024 Nov 19;24(22):7375.
doi: 10.3390/s24227375.

Fast Multi-Distance Time-Domain NIRS and DCS System for Clinical Applications

Marco Nabacino  1 , Caterina Amendola  1 , Davide Contini  1 , Rebecca Re  1   2 , Lorenzo Spinelli  2 , Alessandro Torricelli  1   2
Affiliations

Fast Multi-Distance Time-Domain NIRS and DCS System for Clinical Applications

Marco Nabacino et al. Sensors (Basel). .

Abstract

We have designed and built an improved system for combined Time-Domain Near-Infrared Spectroscopy (TD NIRS) and Diffuse Correlation Spectroscopy (DCS) measurements. The system features two independent channels, enabling TD NIRS and DCS acquisition at short and long source-detector distances to enhance depth sensitivity in layered tissues. Moreover, the device can operate at fast acquisition rates (up to 50 Hz) to monitor hemodynamic oscillations in biological tissues. An OEM (Original Equipment Manufacturer) TD NIRS device enables stable and robust acquisition of photon distribution of time-of-flight. For the DCS signals, the use of a time tagger and a software correlator allows us flexibility in post-processing. A user-friendly GUI controls TD NIRS data acquisition and online data analysis. We present results for the system characterization on calibrated tissue phantoms according to standardized protocols for performance assessment of TD NIRS and DCS devices. In-vivo measurements during rest and during vascular occlusions are also reported to validate the system in real settings.

Keywords: diffuse correlation spectroscopy; fast acquisition; hybrid device; multi-channel device; software correlator; time-domain near-infrared spectroscopy.

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Conflict of interest statement

D.C. and A.T. are co-founders of PIONIRS s.r.l. (Italy). The other authors declare no conflicts of interest. The funders had no role in the design of this study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
(a) Picture of the setup; (b) block diagram of the device; (c) sketch of the optical probe seen from the bottom (units are in mm).
Figure 2
Figure 2
Overlapped IRFs for each source-detector pair. The curves had their backgrounds subtracted, were normalized to their respective areas and were shifted so that all their peaks coincide.
Figure 3
Figure 3
Sample DTOF curves and DNL (a) DTOF curves resulting from 500 ms integration time (top plot) compared to the DNL (bottom plot); (b) DTOF curves obtained by summing all the curves from a 13-h measurement (top plot) compared to the DNL (bottom plot).
Figure 4
Figure 4
Linearity plots for the optical parameters of the MEDPHOT phantoms at 830 nm. Full markers represent the values measured for an inter-fiber distance ρ = 1.5 cm, hollow markers are for ρ = 2.5 cm. Error bars represent standard deviations over 10 repetitions. (a) Measured absorption vs. conventional absorption; (b) measured scattering vs. conventional absorption; (c) measured absorption vs. conventional scattering; (d) measured scattering vs. conventional scattering. Different colors represent different scattering (panels (a,b)) or absorption (panels (c,d)) series. Panels (a,d) show 1:1 lines (solid) and ±3% lines (dashed).
Figure 5
Figure 5
CV of the absorption coefficient over 10 repeated measurements of the same phantom at a source-detector separation ρ = 2.5 cm as a function of the photon count of the DTOF (log-log scale). Dashed lines represent linear fits.
Figure 6
Figure 6
Relevant nEUROPt parameters of the device considering early time gates (500 ps to 1000 ps), late time gates (2000 ps to 2500 ps) and CW signal (summing all gates). (a) Contrast as a function of the inclusion depth for a source-detector separation ρ = 2.5 cm; (b) contrast to noise ratio as a function of the inclusion depth for ρ = 2.5 cm; (c) contrast as a function of the longitudinal position of the inclusion. Black vertical lines represent the source and detector positions, numbers in legend are the FWHM; (d) depth selectivity as a function of the absorption coefficient variation.
Figure 7
Figure 7
Effective Brownian diffusion coefficient of the homogeneous phantoms as a function of the glycerol concentration. Error bars represent standard deviations over 30 repetitions.
Figure 8
Figure 8
Effective Brownian diffusion coefficient of the bilayer phantoms. Error bars represent standard deviations over 30 repetitions. Black lines represent the values of DB in the two layers. (a) Measured DB of the phantom with 30% glycerol concentration in the upper layer as a function of its thickness. (b) Measured DB of the different phantoms for an upper layer thickness d = 10 mm.
Figure 9
Figure 9
Noise level of the DCS module. (a) CV of DB for a fixed number of detection channels (4) as a function of the count-rate, for different measurement durations. The dashed red line represents the theoretical trend, and only its slope is significant. (b) Sum of the variance of the autocorrelation curves as a function of the count-rate. Each line represents a different measurement duration (from 1 s up to 100 s).
Figure 10
Figure 10
Time courses of the hemodynamic parameters measured at ρ = 1.5 cm (left column) and ρ = 2.5 cm (right column) during the stepped occlusion on subject 2. Top row: HbO2 (red), HHb (blue); middle row: tHb (dark green), StO2 (light green); bottom row: BFI. Dashed vertical lines represent the start of each step and the cuff release at the end of the occlusion.
Figure 11
Figure 11
Time traces of the BFI signals (top plots) and their PSDs (bottom plots). (a) Brain; (b) forearm.
See this image and copyright information in PMC

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