  Precise management of patients with cerebral diseases often requires
intracranial pressure (ICP) monitoring, which is highly invasive and requires a
specialized ICU setting. The ability to noninvasively estimate ICP is highly
compelling as an alternative to, or screening for, invasive ICP measurement.
Most existing approaches for noninvasive ICP estimation aim to build a
regression function that maps noninvasive measurements to an ICP estimate using
statistical learning techniques. These data-based approaches have met limited
success, likely because the amount of training data needed is onerous for this
complex applications. In this work, we discuss an alternative strategy that
aims to better utilize noninvasive measurement data by leveraging mechanistic
understanding of physiology. Specifically, we developed a Bayesian framework
that combines a multiscale model of intracranial physiology with noninvasive
measurements of cerebral blood flow using transcranial Doppler. Virtual
experiments with synthetic data are conducted to verify and analyze the
proposed framework. A preliminary clinical application study on two patients is
also performed in which we demonstrate the ability of this method to improve
ICP prediction.
