Research Project
10265818
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.
