Complex wall modeling for hemodynamic simulations of intracranial aneurysms based on histologic images

Purpose

For the evaluation and rupture risk assessment of intracranial aneurysms, clinical, morphological and hemodynamic parameters are analyzed. The reliability of intracranial hemodynamic simulations strongly depends on the underlying models. Due to the missing information about the intracranial vessel wall, the patient-specific wall thickness is often neglected as well as the specific physiological and pathological properties of the vessel wall.

Methods

In this work, we present a model for structural simulations with patient-specific wall thickness including different tissue types based on postmortem histologic image data. Images of histologic 2D slices from intracranial aneurysms were manually segmented in nine tissue classes. After virtual inflation, they were combined into 3D models. This approach yields multiple 3D models of the inner and outer wall and different tissue parts as a prerequisite for subsequent simulations.

Result

We presented a pipeline to generate 3D models of aneurysms with respect to the different tissue textures occurring in the wall. First experiments show that including the variance of the tissue in the structural simulation affect the simulation result. Especially at the interfaces between neighboring tissue classes, the larger influence of stiffer components on the stability equilibrium became obvious.

Conclusion

The presented approach enables the creation of a geometric model with differentiated wall tissue. This information can be used for different applications, like hemodynamic simulations, to increase the modeling accuracy.

Interactive exploration of a 3D intracranial aneurysm wall model extracted from histologic slices

PURPOSE

Currently no detailed in vivo imaging of the intracranial vessel wall exists. Ex vivo histologic images can provide information about the intracranial aneurysm (IA) wall composition that is useful for the understanding of IA development and rupture risk. For a 3D analysis, the 2D histologic slices must be incorporated in a 3D model which can be used for a spatial evaluation of the IA's morphology, including analysis of the IA neck.

METHODS

In 2D images of histologic slices, different wall layers were manually segmented and a 3D model was generated. The nuclei were automatically detected and classified as round or elongated, and a neural network-based wall type classification was performed. The information was combined in a software prototype visualization providing a unique view of the wall characteristics of an IA and allowing interactive exploration. Furthermore, the heterogeneity (as variance of the wall thickness) of the wall was evaluated.

RESULT

A 3D model correctly representing the histologic data was reconstructed. The visualization integrating wall information was perceived as useful by a medical expert. The classification produces a plausible result.

CONCLUSION

The usage of histologic images allows to create a 3D model with new information about the aneurysm wall. The model provides information about the wall thickness, its heterogeneity and, when performed on cadaveric samples, includes information about the transition between IA neck and sac.

Wall enhancement on high-resolution magnetic resonance imaging may predict an unsteady state of an intracranial saccular aneurysm

INTRODUCTION

The aneurysm wall has been reported to play a critical role in the formation, development, and even rupture of an aneurysm. We used high-resolution magnetic resonance imaging (HRMRI) to investigate the aneurysm wall in an effort to identify evidence of inflammation invasion and define its relationship with aneurysm behavior.

METHODS

Patients with intracranial aneurysms who were prospectively evaluated using HRMRI between July 2013 and June 2014 were enrolled in this study. The aneurysm's wall enhancement and evidence of inflammation invasion were determined. In addition, the relationship between aneurysm wall enhancement and aneurysm size and symptoms, including ruptured aneurysms, giant unruputred intracranial aneurysms (UIAs) presenting as mass effect, progressively growing aneurysms, and aneurysms associated with neurological symptoms, was statistically analyzed.

RESULTS

Twenty-five patients with 30 aneurysms were available for the current study. Fourteen aneurysms showed wall enhancement, including 6 ruptured and 8 unruptured aneurysms. Evidence of inflammation was identified directly through histological studies and indirectly through intraoperative investigations and clinical courses. The statistical analysis indicated no significant correlation between aneurysm wall enhancement and aneurysm size. However, there was a strong correlation between wall enhancement and aneurysm symptoms, with a kappa value of 0.86 (95 % CI 0.68-1).

CONCLUSIONS

Aneurysm wall enhancement on HRMRI might be a sign of inflammatory change. Symptomatic aneurysms exhibited wall enhancement on HRMRI. Wall enhancement had a high consistent correlation of symptomatic aneurysms. Therefore, wall enhancement on HRMRI might predict an unsteady state of an intracranial saccular aneurysm.

Histological Analysis of Aneurysm Wall Occluded with Clip Blades. A Case Report

Reports on histological changes of vascular wall following clipping surgery have been scarce. The authors experienced a case of unruptured cerebral aneurysm in which the tissue occluded by clip blades for 6 years was obtained and histologically examined. The aneurysmal wall following clipping showed granulomatous inflammation with necrosis, and occluded aneurysmal walls were found with collagenous fibrous tissue. Mild infiltration by lymphocytes and fibrous thickened intima occurred.