Electronic Thesis and Dissertation Repository


Doctor of Philosophy


Medical Biophysics


Dr. Keith St. Lawrence


Patients with neurological diseases are vulnerable to cerebral ischemia, which can lead to brain injury. In the intensive care unit (ICU), neuromonitoring techniques that can detect flow reductions would enable timely administration of therapies aimed at restoring adequate cerebral perfusion, thereby avoiding damage to the brain. However, suitable bedside neuromonitoring methods sensitive to changes of blood flow and/or oxygen metabolism have yet to be established.

Near-infrared spectroscopy (NIRS) is a promising technique capable of non-invasively monitoring flow and oxygenation. Specifically, diffuse correlation spectroscopy (DCS) and time-resolved (TR) NIRS can be used to monitor blood flow and tissue oxygenation, respectively, and combined to measuring oxidative metabolism. The work presented in this thesis focused on advancing a DCS/TR-NIRS hybrid system for acquiring these physiological measurements at the bedside.

The application of NIRS for neuromonitoring is favourable in the neonatal ICU since the relatively thin scalp and skull of infants has minimal effect on the detected optical signal. Considering this application, the validation of a combined DCS/NIRS method for measuring the cerebral metabolic rate of oxygen (CMRO2) was investigated in Chapter 2. Although perfusion changes measured by DCS have been confirmed by various flow modalities, characterization of photon scattering in the brain is not clearly understood. Chapter 3 presents the first DCS study conducted directly on exposed cortex to confirm that the Brownian motion model is the best flow model for characterizing the DCS signal. Furthermore, a primary limitation of DCS is signal contamination from extracerebral tissues in the adult head, causing CBF to be underestimated. In Chapter 4, a multi-layered model was implemented to separate signal contributions from scalp and brain; derived CBF changes were compared to computed tomography perfusion.

Overall, this thesis advances DCS techniques by (i) quantifying cerebral oxygen metabolism, (ii) confirming the more appropriate flow model for analyzing DCS data and (iii) demonstrating the ability of DCS to measure CBF accurately despite the presence of a thick (1-cm) extracerebral layer. Ultimately, the work completed in this thesis should help with the development of a hybrid DCS/NIRS system suitable for monitoring cerebral hemodynamics and energy metabolism in critical-ill patients.