Background Cerebrovascular disease, in particular stroke, is a major public health challenge. An important biomarker is cerebral hemodynamics. To measure and quantify cerebral hemodynamics, however, only invasive, potentially harmful or time-to-treatment prolonging methods are available. Results We present a simulation-based approach which allows calculation of cerebral hemodynamics based on the patient-individual vessel configuration derived from structural vessel imaging. For this, we implemented a framework allowing segmentation and annotation of brain vessels from structural imaging followed by 0-dimensional lumped simulation modeling of cerebral hemodynamics. For annotation, a 3D-graphical user interface was implemented. For 0D-simulation, we used a modified nodal analysis, which was adapted for easy implementation by code. The simulation enables identification of areas vulnerable to stroke and simulation of changes due to different systemic blood pressures. Moreover, sensitivity analysis was implemented allowing the live simulation of changes to simulate procedures and disease progression. Beyond presentation of the framework, we demonstrated in an exploratory analysis in 67 patients that the simulation has a high specificity and low-to-moderate sensitivity to detect perfusion changes in classic perfusion imaging. Conclusions The presented precision medicine approach using novel biomarkers has the potential to make the application of harmful and complex perfusion methods obsolete.

Introduction Cerebrovascular disease is a major public health challenge. An important biomarker is cerebral hemodynamics. To measure cerebral hemodynamics, however, only invasive, potentially harmful or time-to-treatment prolonging methods are available. We present a simulation-based alternative which allows calculation of cerebral hemodynamics based on the individual vessel configuration of a patient derived from structural vessel imaging. Methods We implemented a framework allowing annotation of extracted brain vessels from structural imaging followed by 0-dimensional lumped modelling of cerebral hemodynamics. For annotation, a 3D-graphical user interface (GUI) was implemented. For 0D-simulation, we used a modified nodal analysis (MNA), which was adapted for easy implementation by code. The code was written in-house in java. The simulation GUI allows inspection of simulation results, identification of vulnerable areas, simulation of changes due to different systemic blood pressures. Moreover, sensitivity analysis was implemented allowing the live simulation of changes of variables such as vessel lumen to simulate procedures and disease courses. In two exemplary patients, simulation results were compared to dynamic-susceptibility-weighted-contrast-enhanced magnetic-resonance(DSC-MR) perfusion imaging. Results The successful implementation of the framework allowing individualized annotation and simulation of patients is presented. In two exemplary patients, both the simulation as well as DSC-MRI showed the same results pertaining to the identification of areas vulnerable to ischemia. Sensitivity analysis allows the individualized simulation of changes in vessel lumen and the effect on hemodynamics. Discussion We present the first precision medicine pipeline for cerebrovascular disease which allows annotation of the arterial vasculature derived from structural vessel imaging followed by personalized simulation of brain hemodynamics. This paves the way for further development of precision medicine in stroke using novel biomarkers and might make the application of harmful and complex perfusion methods obsolete for certain use cases in the future.

Cerebrovascular disease, in particular stroke, is a major public health challenge. An important biomarker is cerebral hemodynamics. To measure and quantify cerebral hemodynamics, however, only invasive, potentially harmful or time-to-treatment prolonging methods are available. We present a simulation-based approach which allows calculation of cerebral hemodynamics based on the patient-individual vessel configuration derived from structural vessel imaging. For this, we implemented a framework allowing segmentation and annotation of brain vessels from structural imaging followed by 0-dimensional lumped simulation modelling of cerebral hemodynamics. For annotation, a 3D-graphical user interface was implemented. For 0D-simulation, we used a modified nodal analysis, which was adapted for easy implementation by code. The simulation enables identification of areas vulnerable to stroke and simulation of changes due to different systemic blood pressures. Moreover, sensitivity analysis was implemented allowing the live simulation of changes to simulate procedures and disease progression. Beyond presentation of the framework, we demonstrated in an exploratory analysis in 67 patients that the simulation has a high specificity and low to moderate sensitivity to detect perfusion changes in classic perfusion imaging. The presented precision medicine approach using novel biomarkers has the potential to make the application of harmful and complex perfusion methods obsolete.