PROJECT SUMMARY/ABSTRACT
Acute brain injuries can lead to secondary brain damage that worsens the outcome. Reduced cerebral blood
flow can induce ischemia, while excess blood flow can cause hemorrhage. Thus, there is a need for
noninvasive, bedside, continuous cerebral blood flow monitoring approaches at neurointensive care units
(NICUs). Existing technologies for continuous monitoring of cerebral blood flow have critical limitations.
Functional near-infrared spectroscopy has been employed for this clinical need, but it suffers from being not
quantitative and prone to errors due to signals from superficial scalp tissue. Moreover, it measures only limited
information content of oxygen saturation. Additional blood flow contrast can provide a useful biomarker. Diffuse
correlation spectroscopy (DCS) technique is an emerging diffuse optical technique for bedside monitoring of
blood flow in humans. Currently, DCS operates in continuous-wave (CW) mode, which has limitations such as
superficial signal sensitivity and inaccurate quantification of blood flow due to dependency to priori information
of optical parameters. More recent time domain (TD) approach has low signal-to-noise ratio, costly, highly
limited for clinical translation. The goal is to address these limitations by proposing a novel technology and
method that can quantify both absolute static and dynamic parameters concurrently in a single instrument with
fast data acquisition, thus, it is highly suitable for fast functional neuroimaging. It can also separate superficial
and brain signals by discriminating early and late photons via time-gating. Additionally, longer wavelength at
the infrared allows for enhanced depth penetration. It can quantify blood flow and optical parameters in near-
real-time using deep learning, which is highly suitable for NICU settings. The proposed system and method will
completely replace the current state-of-the-art (CW-DCS) and is superior TD approach, because it can provide
higher signal-to-noise ratio (SNR) in the brain, its simplicity and significantly lower cost in instrumentation,
which will lead to fast clinical translation. To achieve our goal, we will construct and optimize the instrument
prototype, characterize the signal, and then we will test the system on phantom models and custom-developed
analytical and Monte Carlo and deep learning models and determine the quantification accuracy with respect
to static and dynamic parameters (Aim-1). We will optimize the system with respect to pulse-width, SNR for
improved quantification accuracy of static and dynamic parameters (Aim-2). Then, we will test the system in
healthy subjects and traumatic brain injury patients (Aim-3). This innovative DCS system and method will
result in quantitative blood flow parameter with enhanced brain sensitivity and will eliminate the roadblocks in
both CW and TD approaches, thereby will pave the way for fast clinical translation at NICU settings and for
general neuroimaging applications.