Assessing extracerebral contamination in cerebral blood flow pulsatility measured by diffuse correlation spectroscopy

Abstract

Diffuse Correlation Spectroscopy (DCS) provides a non-invasive method of measuring microvasculature cerebral blood flow (CBF). Recent advancements have enabled flow pulsatility to be captured, providing a means of continuously monitoring critical closing pressure (CCP), which is intrinsically linked to intracranial pressure [1]. Similar to DCS measurements of mean CBF, DCS pulsatility data can be contaminated by blood flow in the extracerebral (EC) tissue [2]. This study focuses on extracting CBF pulsatility using the probe pressure modulation algorithm proposed by Baker et al. for removing EC contamination [3]. DCS data were collected from five healthy volunteers, along with continuous recordings of arterial blood pressure and ECG. Data were acquired at two source detector distances (rSD = 1 and 2.5 cm) at a sampling frequency of 20 Hz [4]. The pulsatile waveform was generated by two methods: (1) fitting the semi-infinite model to data acquired at rSD = 2.5 cm and (2) applying the pressure modulation algorithm to data from both distances. The two waveforms were compared based on extracting waveform features, including the systolic-to-diastolic amplitude (YSD), reflective flow peak (S2), dicrotic notch (d), diastolic peak (D), and Δt (Δt = tS1 - td). Preliminary results indicated that removing EC contamination caused a significant increase in Y_SD and Δt. Reductions in S2 and d were also observed, but these changes did not reach statistical significance. In conclusion, these preliminary findings suggest that EC contamination can alter the shape of the pulsatile waveform, which could influence parameters such as CCP used to assess brain health. Collecting multi-distance DCS data and incorporating the pressure modulation algorithm to remove EC contamination is recommended.