Automated Cutting Plane Positioning for Intracranial Aneurysm Quantification

Area of Science:

• Medical Imaging
• Computational Fluid Dynamics
• Neurosurgery

Background:

• Aneurysm rupture risk assessment relies on morphologic and hemodynamic features from angiographic images.
• Current feature extraction requires manual aneurysm isolation, leading to intra- and inter-rater variability.

Purpose of the Study:

• To develop and validate an automatic cutting plane (ACP) positioning method for aneurysm isolation.
• To eliminate manual segmentation variabilities and improve the accuracy of aneurysm morphology and hemodynamics quantification.

Main Methods:

• Proposed innovative Hough-like and multi-hypothesis detection for aneurysm center and parent vessel inlets.
• Utilized iterative ACP positioning with geometry-inspired cost function optimization.
• Validated against manual cutting plane (MCP) and manual neck curve-based isolation.

Main Results:

• ACP isolation yielded smaller average inter-curve distances (AICDs) compared to MCP.
• 90% of cases had AICD ≤ 0.5 mm, demonstrating high precision.
• Intra- and inter-rater variability of manual methods was significantly higher than ACP.

Conclusions:

• The ACP method provides accurate and reliable aneurysm isolation, comparable to manual methods but with reduced variability.
• Extracted morphologic features using ACP showed good agreement with MCP-based features.
• ACP has significant potential for clinical applications in aneurysm morphology and hemodynamics quantification.