Parameter estimations for the cerebrospinal fluid infusion test

We consider parameter estimation for a single compartment model of a cerebrospinal fluid (CSF) infusion test using an inverse power law cerebral compliance depending on intracranial pressure (ICP). A least squares optimization is used to solve the inverse problem of estimating model parameter values from ICP observed during an infusion test. The optimization is applied to synthetic test data and to clinical ICP data from a number of infusion tests.We show that in tests that successfully reach a new plateau pressure, the resistance to CSF outflow and elastance can be reliably estimated using an automated least squares process. We find that a single infusion test does not provide enough resolution to distinguish between compliance models.