Population and morphology models

 

In this WP, we will focus on the following objectives:

  1. Development of a population model for the generation of virtual populations of stroke patients

Based on available clinical and imaging data we will develop a population model. This model consists of a set of probability density distributions of relevant parameters, including known correlations, obtained from real patient data. A virtual patient is a sample from this model and is interpolated from these distributions. As virtual patients need to be accurate representations of characteristics present in real populations of stroke patients, we include imaging derived information on vascular anatomy and thrombus, besides relevant clinical data. Furthermore, the variety and correlation of all included parameters will be taken into account as part of the population model.

  1. Development of vessel segmentations, atlases of clots and ischemic cores

We will create extensive vessel models based on segmented arteries obtained from available imaging data. Also, frequency distributions and correlation matrices of different thrombus imaging characteristics, such as size (volume, length), location, distance from internal carotid artery terminus, attenuation, and perviousness will be generated. Furthermore, probability maps of thrombus locations, indicating the chance that a thrombus is present in a specific part of the intracranial arteries, will be created. In addition, we will predict the final infarct volume based on the thrombus location, thrombus composition and collateral score. Probability maps of infarcts that indicate the chance of having an infarct in a specific region based on prior clinical and imaging information will be created.

 

  1. Correlation between clinical and imaging characteristics and clot composition

Thrombi obtained during mechanical thrombectomy will be collected and analyzed using routine macroscopy, histology and immuno-histochemistry. Clot composition will be studied by quantitative determination of the amount of red blood cells, white blood cells, platelets, fibrin and von Willebrand factor. In addition, the stent thrombus interaction as a function of the thrombus composition will be assessed by electron microscopy of the stents. The thrombus database will be used to create a clot population model. We will build statistical models to correlate histologic clot composition with (clinical and) imaging features. These models will be used to connect the in silico models of thrombolysis and thrombectomy within the virtual stroke population.

 

  1. Correlation between procedural outcome (revascularization, reperfusion) and clinical outcome

We will develop statistical models that correlate vessel geometry and clot composition to imaging outcome parameters: revascularization, reperfusion and final infarct volume. The results of the in silico models will be compared with the results of the statistical models, allowing a validation of the in silico models on the population level. Finally, we will also relate the output of the in silico models to clinical outcome. Therefore, we will develop  statistical models to predict clinical outcome (modified Rankin scale and National Institutes of Health Stroke Scale) based on available data on recanalization, reperfusion, in situ propagation of thrombus, final infarct volume, and ischemic core location.