Histopathology-based near-realistic arterial wall reconstruction of a patient-specific cerebral aneurysm for...

Cerebral aneurysms occur as balloon-like outpouchings in an artery, which commonly develop at the weak curved regions and bifurcations. When aneurysms are detected, understanding the risk of rupture is of immense clinical value for better patient management. Towards this, Fluid-Structure Interaction (FSI) studies can improve our understanding of the mechanics behind aneurysm initiation, progression, and rupture. Performing retrospective hemodynamic analysis using an accurate computational model that is closer to the actual biological milieu could yield clinically useful rupture risk predictions. Currently, the geometric model for the FSI studies rely on imaging the flow-domain using Computed Tomographic Angiography (CTA) or Digital Subtraction angiography (DSA), which limits accurate discerning of the vessel wall thickness. Histopathological information has always been ignored in accurately reconstructing the geometric model for the aneurysm. The present study combines both the shape information of the 3D lumen model (as it existed in vivo), which is accurately rendered through the CTA, in conjunction with the wall thickness information extracted from histo-pathological 2D images of the aneurysm. Furthermore, fluid-structure interaction (FSI) simulations are performed to understand the influence of patient-specific wall contribution towards rupture.

A 3D geometric model of the blood-flow domain of an anterior communicating artery (ACoA) aneurysm is extracted from the CTA of a patient that was surgically clipped. After safely clipping the aneurysm, the fundus of the aneurysm beyond the clip was cut and extracted. This was carefully preserved and sliced to obtain the wall thickness variation of the hoop at various axial sections. This study proposes a novel methodology of combining multi-modal image data to geometrically render the 3D model of the Cerebral aneurysm. The wall thickness extracted from the histological 2D cross-sectional images of the aneurysm is encapsulated around the 3D lumen model obtained from CT Angiographic data. To this end, a wall thickness transfer algorithm is developed to accurately reconstruct the patient-specific aneurysm wall thickness variation for the FSI simulations.

The wall thickness transfer algorithm accurately combines both the blood flow domain from the CT angiography and the histopathological images involving the wall thickness heterogeneity for the aneurysm. The patient-specific wall thickness variation, as it existed in vivo, has a mean wall thickness of 0.553 mm with a standard deviation of 0.256 mm. Detailed FSI simulations were performed to study the role of the patient-specific wall thickness (PWT) model vis-a-vis the uniform wall thickness (UWT) model. It was observed that the maximum wall stress for the UWT model was 13.6 kPa, while it was substantially higher for the PWT model (48.4 kPa). The maximum wall displacement for the UWT model was 58.5μm, while it was 162μm for the PWT model. Similarly, the mean wall stress for the UWT model was 2.13 kPa, while for the PWT model, it was 8.43 kPa. The mean wall displacement for the PWT model was substantially higher than the UWT model (52.58μm against 16.47μm).

The rendered patient-specific aneurysm wall model with its thickness variation, as it existed in vivo was obtained. Comparing fluid-structure interaction (FSI) simulation results, between the patient-specific wall-lumen combined model against the uniform wall thickness model have clearly shown that there were significant differences (p< 0.05) in the distribution of the hemodynamic parameters. The percentage difference in mean wall displacement and associated wall stress was 69% and 75%, respectively. Corresponding numbers for maximum wall displacement and maximum wall stress are 64% and 72%, respectively. Patient-specific fluid-structure interaction simulations show that, the present approach is highly valuable, as it improves our understanding towards rupture potential analysis for the cerebral aneurysms.