An efficient pipeline to compute patient-specific cerebral aneurysm wall tension
<|reference_start|>An efficient pipeline to compute patient-specific cerebral aneurysm wall tension: Cerebral aneurysm rupture, leading to subarachnoid hemorrhage with a high mortality rate, disproportionately affects younger populations, resulting in a significant loss of productive life years. A significant proportion of these deaths is due to aneurysmal re-bleeding within the first three days following the initial bleed, prior to treatment. While early aneurysm treatment is recommended, there is no consensus on the ideal timing, and emergency treatment offers only an incremental benefit at a significant cost. Although various multivariable prediction models have been proposed to provide personalized risk assessments, no validated patient-specific predictor is available to rationalize emergency treatment. Furthermore, no model has yet incorporated emerging computational biomechanics-based biomarkers such as wall tension. In this paper, we proposed and validated an efficient semi-automatic pipeline to compute patient-specific cerebral aneurysm wall tension as a potential biomarker for the likelihood of re-bleeding. Our pipeline uses the patient's computed tomography angiography (CTA) image obtained at the time of subarachnoid hemorrhage diagnosis to create a patient-specific biomechanical model of the cerebral aneurysm using the finite element method. A distinctive feature of our approach is the straightforward model creation and wall tension computation using shell finite elements, without requiring patient-specific material properties or aneurysm wall thickness. Our non-invasive, patient-specific method for cerebral aneurysm wall tension can potentially provide individualized risk prediction and enhance clinical decision-making.<|reference_end|>
@article{jamshidian2024an,
    title={An efficient pipeline to compute patient-specific cerebral aneurysm wall
  tension},
    author={Mostafa Jamshidian, Benjamin Zwick, Arosha S Dissanayake, Adam Wittek,
  Timothy J Phillips, Stephen Honeybul, Graeme J Hankey, Karol Miller},
    journal={arXiv preprint arXiv:2407.05247},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.05247},
    primaryClass={cs.CE}
}