Elevated intracranial pressure (ICP) remains a critical problem in management of patients with neurological problems and is the most common cause of death in these patients. Elevated ICP can be managed and treated if detected at an early stage. Therefore, measurement of ICP is important for diagnosis and treatment decisions. At this time only invasive techniques are available for measurement of ICP. These techniques require physical penetration of the central nervous system (CNS). The objective this project is to develop a clinically viable tool for noninvasive measurement of ICP. The noninvasive measurement of ICP would be at a lower cost and without risk or pain to the patient. Hence, this important clinical parameter would be measured more often and may significantly improve the diagnosis and treatment decisions for patients with neurological problems. The method integrates knowledge from human neuro-physiology, principles of fluid dynamics, and dynamic MRI techniques in a novel technology to noninvasively measure ICP. The technology is innovative as it makes use of the change in intracranial volume (ICV) and ICP that occur naturally during each cardiac cycle rather than altering the state of the CNS by external intervention. The ICP is derived from dynamic MRI measurements of the cerebrospinal fluid (CSF) and blood flow to and from the brain. Recently published work by the PI with animal model, and a small number of patients, demonstrated a good correlation between invasively measured pressure values and values derived noninvasively using the MRI-based method. However, multiple measurements were needed to overcome measurement variability. The objective of this project is to develop a new MRI data acquisition scheme and implement a reliable method for flow quantitation for ICP measurement. These steps would accelerate the transfer of this technology from the research phase to become a clinically viable tool. The proposed specific aims are: 1. Develop MRI data acquisition protocol for simultaneous measurement of slow (CSF) and fast (blood) flow dynamics. 2. Implement an automated method to identify the blood and CSF lumens boundary for reproducible volumetric flow measurements. 3. Quantify and demonstrate improved reproducibility of the volumetric flow rates and the resulting ICP values
