| 日期 18 December 2025
Diffuse Correlation Spectroscopy (DCS) is a non-invasive method used to assess blood flow in the brain in real-time, through the analysis of intensity fluctuations of back-scattered light. Time-Domain DCS (TD-DCS) measures the time of flight (ToF) of photons that are shone through a section of the brain and calculates the correlation function, g2(tau). This technique is widely used in the biomedical field to monitor blood flow in real-time and has applications in monitoring oxygen in the brain after traumatic brain injury, strokes, or surgery. This monitoring must be extremely precise and done in real time, relying heavily on timing electronics that have the capability for continuous transfer of high data rates and the ability to analyze enormous amounts of data with low timing jitter.
Mitchell Robinson and his group at Martinos Center at Massachusetts General Hospital use pathlength-selective, interferometric DCS (PaLS-iDCS) to measure blood flow in the brain with improved deep tissue sensitivity and measurement. The PaLS-iDCS experimental setup consists of a custom-designed pulsed laser source, which is split into sample and reference arms. Light diffusely reflected from the sample is captured with a superconducting nanowire single-photon detector (SNSPD), then recombined with the reference arm. The photodetection events, as collected from the SNSPD, were recorded by two independent channels of the Time Tagger Ultra, with the resulting data stored for later post-processing. 1 Due to the high count rate seen in these experiments, it is important that the timing electronics used have the capability for high data transfer rates, as well as the ability to analyze the enormous amount of data to find the relevant information.
When asked how Swabian Instruments’ Time Tagger addressed common issues within his data acquisition, Mitchell said, “Swabian [Instruments’] Time Taggers can process large amounts of data very quickly, with extreme precision. It is best that the data is taken as closely together as possible, so we are acquiring a huge amount of counts. The Time Tagger helps with acquiring this information, as well as going through the egregious amounts of data. The Time Tagger allows for data to be taken independently from two lasers with their own native repetition rates.”
In post-processing, the detected diffused photons are first binned by their Time-of-Flight (ToF) into a temporal point spread function (TPSF), which gives information into how deeply the photons traveled into the sample. “Late photons” have longer path lengths, meaning they travel deeper into the brain tissue, therefore making them more likely to carry relevant information about the blood flow in the brain. When asked about the use of the Time Tagger for data analysis, Mitchell emphasized the importance of the precise timestamping of the Time Tagger and access to the raw data for real-time analysis and calculation of the persistence graph. He also explained the use of the Virtual Channels DelayedChannel() and GatedChannel() to perform the gating of the signals and ensure the “late photons” are characterized for a deeper resolution in their brain studies.
Mitchell’s group continues to explore the use and implementation of PaLS-iDC, saying, “Blood flow is important; it’s why we do what we do. The Time Tagger helped us implement a strategy we knew worked, but didn’t have the hardware to accomplish.”
Mitchell B. Robinson, et al., Pathlength-selective, interferometric diffuse correlation spectroscopy. bioRxiv 2024.06.21.600096; doi: https://doi.org/10.1101/2024.06.21.600096 ↩︎