Understanding the physiology of cerebral blood flow (CBF) and measuring it have been main concerns in neurology. Numerous techniques have been applied to detect CBF, and various approaches have been tried to uncover the complex mechanisms of delicate regulation of flows in microvascular networks. However, current methods do not meet the demands in noninvasive monitoring of CBF and fine tuning of microvascular flows in preclinical studies with low cost. In this project, I investigated how CBF can be detected noninvasively by an optical imaging technique combined with analysis of intravascular tracer dynamics, and how certain method can be accurately validated using in silico simulation in microvascular network.
After brief introduction of this thesis in chapter 1, I demonstrated a noble optical imaging technique for measuring CBF in mice in chapter 2. In preclinical studies of ischemic brain disorders, it is crucial to measure CBF; however, this requires radiological techniques with heavy instrumentation or invasive procedures. The optical imaging method proposed here will provide a noninvasive and easy-to-use technique to detect CBF in experimental small animals. Mice were injected with indocyanine green and time-series near-infrared fluorescence signals over heads were excited by light-emitting diodes and imaged by a CCD. I calculated four CBF parameters and generated CBF maps using time and intensity information of fluorescence dynamics to estimate the status of flow under normal and ischemic conditions. The maps dominantly represented intracerebral flows in mice even in the presence of an intact skull and scalp, and I further demonstrated that this noninvasive optical imaging technique successfully detected reduced CBF in middle cerebral artery occlusion.
In chapter 3, I simulated blood flows and tracer dynamics in cerebral microvascular network, and evaluated CBF parameters suggested above. The resolutions of imaging techniques measuring CBF are usually above capillary level and their speeds are not sufficient to measure blood flow of entire microvessels at a time. The complex nature of microvascular network made it difficult to understand the details of pathophysiology in vascular occlusion diseases such as stroke and application of theoretical CBF calculation algorithms. Here I demonstrated in silico modeling and simulation methods to understand CBF in microvascular network. High resolution cortical vasculature was imaged from an anesthetized mouse using two-photon laser scanning microscopy. Topology and vascular parameters such as lengths and diameters were semi-automatically extracted from the image stacks and converted to a graph data structure. The relationships among blood pressures in branching points and blood flows in vessel segments were established using hemorheological equations. The generated large-scale simultaneous linear equations were solved, and the unknown blood flow variables were determined. Finally, using the blood flow information, virtually injected intravascular tracers were simulated in microvascular network. Time-intensity curves were generated from residue functions inside volumes of interest. Four CBF parameters were calculated from the curves and their accuracies were evaluated.
In conclusion, these two methodologies for CBF measurement, one for optical imaging and the other for in silico simulation, may play valuable roles in studies for cerebral hemodynamics and neurological disorders.