@neurIST complex information processing toolchain for the integrated management of cerebral aneurysms

Area of Science:

• Medical imaging analysis
• Computational fluid dynamics
• Biomechanical engineering

Background:

• Cerebral aneurysms are a complex disease with significant health implications.
• Identifying rupture risk factors is crucial for effective patient management.
• Previous studies faced challenges due to heterogeneous data and manual processing.

Purpose of the Study:

• To physically characterize cerebral aneurysms for risk factor identification.
• To develop and validate an automated toolchain for consistent aneurysm analysis.
• To establish a data model for managing simulation-related data and ensuring provenance.

Main Methods:

• Analysis of morphological, hemodynamic, and aneurysm wall structure for over 300 cases.
• Development of a highly automated toolchain to ensure result consistency across heterogeneous image data.
• Implementation of an abstract data model for simulation data management and modification.

Main Results:

• Extracted descriptors from over 300 ruptured and unruptured aneurysms suitable for statistical studies.
• Achieved consistent analysis results despite heterogeneous data sources and multiple operators through automation and training.
• Toolchain positively evaluated by over 260 clinicians during hands-on workshops, demonstrating successful automation.

Conclusions:

• The automated toolchain effectively addresses challenges in cerebral aneurysm characterization.
• The developed methods enable robust statistical analysis for identifying aneurysm rupture risk factors.
• The abstract data model facilitates data management, provenance, and future adaptability of the toolchain.