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

Three-dimensional wall-thickness distributions of unruptured intracranial aneurysms characterized by micro-computed tomography

Aneurysmal rupture is associated with wall thinning, but the mechanism is poorly understood. This study aimed to characterize the three-dimensional wall-thickness distributions of unruptured intracranial aneurysms. Five aneurysmal tissues were investigated using micro-computed tomography. First, the wall thickness was related to the aneurysmal wall appearances during surgery. The median wall thicknesses of the translucent and non-translucent walls were 50.56 and 155.93 µm, respectively (p < 0.05) with significant variation in the non-translucent wall thicknesses (p < 0.05). The three-dimensional observations characterized the spatial variation of wall thicknesses. Thin walls showed a uniform thickness profile ranging from 10 to 40 µm, whereas thick walls presented a peaked thickness profile ranging from 300 to 500 µm. In transition walls, the profile undulated due to the formation of focal thin/thick spots. Overall, the aneurysmal wall thicknesses were strongly site-dependent and spatially varied by 10 to 40 times within individual cases. Aneurysmal walls are exposed to wall stress driven by blood pressure. In theory, the magnitude of wall stress is inversely proportional to wall thickness. Thus, the observed spatial variation of wall thickness may increase the spatial variation of wall stress to a similar extent. The irregular wall thickness may yield stress concentration. The observed thin walls and focal thin spots may be caused by excessive wall stresses at the range of mechanical failure inducing wall injuries, such as microscopic tears, during aneurysmal enlargement. The present results suggested that blood pressure (wall stress) may have a potential of acting as a trigger of aneurysmal wall injury.

Multimodal exploration of the intracranial aneurysm wall

PURPOSE

Intracranial aneurysms (IAs) are pathological changes of the intracranial vessel wall, although clinical image data can only show the vessel lumen. Histology can provide wall information but is typically restricted to ex vivo 2D slices where the shape of the tissue is altered.

METHODS

We developed a visual exploration pipeline for a comprehensive view of an IA. We extract multimodal information (like stain classification and segmentation of histologic images) and combine them via 2D to 3D mapping and virtual inflation of deformed tissue. Histological data, including four stains, micro-CT data and segmented calcifications as well as hemodynamic information like wall shear stress (WSS), are combined with the 3D model of the resected aneurysm.

RESULTS

Calcifications were mostly present in the tissue part with increased WSS. In the 3D model, an area of increased wall thickness was identified and correlated to histology, where the Oil red O (ORO) stained images showed a lipid accumulation and the alpha-smooth muscle actin (aSMA) stained images showed a slight loss of muscle cells.

CONCLUSION

Our visual exploration pipeline combines multimodal information about the aneurysm wall to improve the understanding of wall changes and IA development. The user can identify regions and correlate how hemodynamic forces, e.g. WSS, are reflected by histological structures of the vessel wall, wall thickness and calcifications.

Towards Realistic 3D Models of Tumor Vascular Networks

For reliable in silico or in vitro investigations in, for example, biosensing and drug delivery applications, accurate models of tumor vascular networks down to the capillary size are essential. Compared to images acquired with conventional medical imaging techniques, digitalized histological tumor slices have a higher resolution, enabling the delineation of capillaries. Volume rendering procedures can then be used to generate a 3D model. However, the preparation of such slices leads to misalignments in relative slice orientation between consecutive slices. Thus, image registration algorithms are necessary to re-align the slices. Here, we present an algorithm for the registration and reconstruction of a vascular network from histologic slices applied to 169 tumor slices. The registration includes two steps. First, consecutive images are incrementally pre-aligned using feature- and area-based transformations. Second, using the previous transformations, parallel registration for all images is enabled. Combining intensity- and color-based thresholds along with heuristic analysis, vascular structures are segmented. A 3D interpolation technique is used for volume rendering. This results in a 3D vascular network with approximately 400-450 vessels with diameters down to 25-30 µm. A delineation of vessel structures with close distance was limited in areas of high structural density. Improvement can be achieved by using images with higher resolution and or machine learning techniques.

FEM based simulation of magnetic drug targeting in a multibranched vessel model

BACKGROUND AND OBJECTIVE

Magnetic drug targeting (MDT) is a promising technology to improve cancer therapy. MDT describes the accumulation of drug loaded superparamagnetic iron oxide nanoparticles (SPIONs) at a desired location, e. g. a tumor, by application of a magnetic field. Here, we evaluate the effectivity of MDT for an endoscopic placement of two different configurations of magnet arrays, i. e. six magnets with same poles facing each other and a Halbach array. Compared to conventional magnet setups outside the body, this endoscopic placement gives the possibility to achieve higher magnetic field gradients inside the tumor.

METHODS

For such a MDT concept, we present FEM based simulations of MDT tracing single SPIONs in a 3D geometry of eight multibranched vessels with sizes in the range of capillaries. In these simulations, the effect of the magnetic field gradient as well as of magnet distance to the vessel geometry, magnetic flux density of the magnets, SPIONs hydrodynamic diameter and magnetic moment on the MDT effectivity is calculated. The blood flow is modelled as an incompressible Newtonian fluid and the SPIONs are suspended in the blood flow. Statistical significance of the targeting effectivity results is tested with the Mann-Whitney-U-Test.

RESULTS

The results show that the magnetic targeting effectivity is up to 32 % higher than the one calculated without the presence of a magnetic field. In the investigated vessel network, this effect on the targeting effectivity is dependent on the number of local magnetic field maxima that are approached with a high gradient and is noticeable up to 200 µm distance of the magnet to the vessel geometry.

CONCLUSIONS

We conclude that for an effective application of MDT, the magnet configuration needs to be placed close to the tumor and should yield a large number of magnetic field maxima that are approached with a high gradient.

Shedding the Light on the Natural History of Intracranial Aneurysms: An Updated Overview

The exact molecular pathways underlying the multifactorial natural history of intracranial aneurysms (IAs) are still largely unknown, to the point that their understanding represents an imperative challenge in neurovascular research. Wall shear stress (WSS) promotes the genesis of IAs through an endothelial dysfunction causing an inflammatory cascade, vessel remodeling, phenotypic switching of the smooth muscle cells, and myointimal hyperplasia. Aneurysm growth is supported by endothelial oxidative stress and inflammatory mediators, whereas low and high WSS determine the rupture in sidewall and endwall IAs, respectively. Angioarchitecture, age older than 60 years, female gender, hypertension, cigarette smoking, alcohol abuse, and hypercholesterolemia also contribute to growth and rupture. The improvements of aneurysm wall imaging techniques and the implementation of target therapies targeted against inflammatory cascade may contribute to significantly modify the natural history of IAs. This narrative review strives to summarize the recent advances in the comprehension of the mechanisms underlying the genesis, growth, and rupture of IAs.

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.