Fig. 1. Conceptual illustration of mDCS measurement. Coherent light is coupled into a scattering medium (a), and collected by one or more single-photon detectors. The photon statistics of the speckle pattern (b) are stored for the duration of an integration time, and analyzed to produce a temporal correlation curve (c). After fitting the data to an expected behavioral model, the average correlation time τ_c is estimated. Changes to τ_c can be directly attributed to, for example, a change in blood flow. Note that (b) represents a speckle pattern from a multipixel array, whereas DCS systems have conventionally utilized one detector.

Fig. 2. Setup for multi-pixel DCS experiment. A continuous wave 785 nm laser is fiber coupled into a water / milk liquid phantom. Scattered photons are collected by a multimode, large core (1500 µm, NA = 0.5) fiber placed a separation distance ρ away, and imaged onto SwissSPAD3. Binary detection data for each half of the array is sent to one of two XEM7360 FPGAs where intermediate data analysis is performed, with results continuously streamed to a PC over two USB 3.0 interfaces. Final calculations and curve plotting is done with MATLAB on a PC.

Fig. 3. Example speckle field (left) and measured autocorrelation function (right) of 100 pixels × 100 pixels when the distance between the optical fiber and the SPAD camera is set to z = 94 ± 1 mm. The left figure illustrates the recorded photon detections when integrated for approximately one second at a frame rate of approximately 92.2 kfps. A slice of the 2D ACF (blue dots) was compared to a Gaussian fit (red line) to estimate the average speckle diameter of 3.0 ± 0.1 pixels. “Hot pixels” have been removed in post processing.

Fig. 4. DCS ensemble average of the entire 500 pixel × 500 pixel area of the SwissSPAD3 (left). The water-to-milk ratio of the liquid phantom was 1:5. The measured ḡ₂(τ)|_{M = 2.5e5} (blue dots) has been fit (red dashed line) to the expected diffusive scattering model of e^(-τ/τc), with τ_c= 107.7 µs. The SNR increases with pixel area (right), and averaging over the entire 500 pixels × 500 pixels (excluding hot pixels and edge effects) allows for unprecedented accuracy, with STD(ḡ₂(τ)|_{M = 2.5e5}) = 2.6 × 10⁻⁴, and an SNR gain of 473.0 over a single pixel.

Fig. 5. DCS measurements using reduced areas of SwissSPAD3. By lowering the number of measured pixels, the exposure time (and shortest measurable time lag τ) can be reduced. This allows for the measurement of processes with faster decorrelation times, at the cost of a reduced SNR. In the left plot (1:4 milk-to-water, µ_a = 0.033 cm^-1_, µ_s^’ = 6.3 cm^-1, ρ = 33.0 mm), the two SS3 halves were configured to measure different temporal correlation ranges simultaneously (red = top, blue = bottom). This allowed for a wider range of measurement per acquisition, but lowers the number of effective pixels (and maximum SNR gain). In the right plot (1:3 milk-to-water, µ_a = 0.033 cm^-1_, µ_s^’ = 8.1 cm^-1, ρ = 22.0 mm), all measured pixels were used to capture two distinct temporal ranges, ${\tau _{lag}}$ = 0 (red) and ${\tau _{lag}}$= 14 (blue) bins. Each acquisition was separated by approximately 1 ms. The solid line corresponds to the mean value of g2(τ) over 160 integration periods, and the shade corresponds to 16 standard deviations.

Fig. 6. Measured DCS curve (left) of a calibrated diffuser (INO Biomimic optical phantom, µ_a = 0.1 cm^-1, µ_s’ = 13.4 cm^-1), when rotating at 20 °/s in a transmission geometry. Beta variation (∼3%) due to slight optical nonuniformity across the surface of the diffuser is resolved in a single 50 ms integration time.