OBJECTIVE: Mathematical modeling of cerebral hemodynamics by descriptive equations can estimate the underlying pulsatile component of cerebral arterial blood volume (CaBV). This way, clinical monitoring of changes in cerebral compartmental compliances becomes possible. Our aim is to validate the most adequate method of CaBV estimation in neurocritical care. APPROACH: We retrospectively reviewed patients with severe traumatic brain injury (TBI) [admitted from 1992-2012] and continuous transcranial Doppler (TCD) monitoring of cerebral blood flow velocity (FV) displaying either plateau waves of intracranial pressure (ICP), episodes of controlled, mild hypocapnia, or vasopressor-induced increases in arterial blood pressure (ABP). Each cohort was analyzed with continuous flow forward (CFF, pulsatile blood inflow and steady blood outflow) or pulsatile flow forward (PFF, both blood inflow and outflow are pulsatile) modeling approaches for estimating the pulse component of CaBV. Spectral pulsatility index (sPI, the first harmonic of the FV pulse/mean FV) can be estimated using the compliance of the vascular bed (Ca) and the cerebrovascular resistance (CVR - here, Ra). We compared three possible methods of assessing Ca (C1: the CFF model, C2 and C3: the PFF models based on ABP or cerebral perfusion pressure (CPP) pulsations, respectively) and combined them with three possible methods of assessing Ra (Ra1= ABP/FV, Ra2= the resistance area product, and Ra3= CPP/FV). Linear regression techniques were applied to describe the strength of each CaBV estimator (a combination of Ca and Ra) against sPI. MAIN RESULTS: The combination of C1 and Ra3 (PI_C1Ra3) was the superior descriptor of CaBV as approximated by sPI for both the plateau waves and the hypocapnia cohorts (r = 0.915 and r = 0.955, respectively). The combination of C1 and Ra1 (PI_C1Ra1) was nearly as robust in the vasopressors cohort (r = 0.938 and r = 0.931, respectively). SIGNIFICANCE: TCD-based estimation of CaBV pulsations seems to be feasible when employing the CFF modeling approach.