Abstract
Diffuse Correlation Spectroscopy (DCS) is a well-established optical technique that has been used for non-invasive measurement of blood flow in tissues. Instrumentation for DCS includes a correlation device that computes the temporal intensity autocorrelation of a coherent laser source after it has undergone diffuse scattering through a turbid medium. Typically, the signal acquisition and its autocorrelation are performed by a correlation board. These boards have dedicated hardware to acquire and compute intensity autocorrelations of rapidly varying input signal and usually are quite expensive. Here we show that a Raspberry Pi minicomputer can acquire and store a rapidly varying time-signal with high fidelity. We show that this signal collected by a Raspberry Pi device can be processed numerically to yield intensity autocorrelations well suited for DCS applications. DCS measurements made using the Raspberry Pi device were compared to those acquired using a commercial hardware autocorrelation board to investigate the stability, performance, and accuracy of the data acquired in controlled experiments. This paper represents a first step toward lowering the instrumentation cost of a DCS system and may offer the potential to make DCS become more widely used in biomedical applications.
Highlights
Diffuse Correlation Spectroscopy (DCS) is a non-invasive method that has been developed [1,2] over the last few decades and provides a powerful technology that can been used to monitor hemodynamic properties of biological tissue in vivo DCS has been used clinically to measure blood flow within various tissues including skeletal muscle [3,4,5] and the brain [6,7,8,9,10,11], and has been validated via comparison to standard clinical imaging modalities including MRI and ultrasound
Since we are dealing digitized signals, the intensity output by the avalanche photodiode detector (APD) can in general be denoted as an indexed array, Ij, where j represents the time-bin corresponding to the detected time-interval, with the first bin being at j = 0 and the last bin being at j = N
The data acquired using the Raspberry Pi device yielded results quantitatively equivalent to those acquired through a hardware correlator
Summary
Diffuse Correlation Spectroscopy (DCS) is a non-invasive method that has been developed [1,2] over the last few decades and provides a powerful technology that can been used to monitor hemodynamic properties of biological tissue in vivo DCS has been used clinically to measure blood flow within various tissues including skeletal muscle [3,4,5] and the brain [6,7,8,9,10,11], and has been validated via comparison to standard clinical imaging modalities including MRI and ultrasound. A DCS system consists of a coherent near-infrared (NIR) laser source, a fiber optical probe containing the source and detector fibers, a fast avalanche photodiode detector (APD), and a correlation device that collects the temporal intensity output from the APD and performs the intensity autocorrelation. A temporal sequence of detected photons is collected and used to compute the autocorrelation function of the signal using a correlation device. This intensity autocorrelation is related to the autocorrelation of the temporal electric field of the incident (coherent) photon field within the medium. The collection of the temporal output from the APD as well as the computing of its autocorrelation is done using a hardware correlator board. We demonstrate a proof-of-concept for the development of a data acquisition system to collect and process intensity autocorrelations for DCS applications using a low-cost Raspberry Pi minicomputer
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