Framework Development for Patient-Specific Compliant Aortic Dissection Phantom Model Fabrication: Magnetic Resonance...

Area of Science:

• Biomedical Engineering
• Medical Imaging
• Fluid Dynamics

Background:

• Type B aortic dissection poses significant risks, including aortic rupture, necessitating better understanding of hemodynamics.
• Patient-specific factors complicate the study of flow patterns in dissected aortas, limiting current research.
• In vitro modeling using patient data can enhance hemodynamic insights into aortic dissections.

Purpose of the Study:

• To develop a fully automated method for fabricating patient-specific Type B aortic dissection models.
• To utilize deep learning for accurate segmentation and 3D model creation.
• To create compliant, physiologically accurate phantom models for in vitro hemodynamic analysis.

Main Methods:

• A novel deep-learning-based segmentation approach was employed for negative mold manufacturing.
• Deep learning models were trained on computed tomography scans and tested on patient data.
• 3D models were printed using polyvinyl alcohol, coated with latex, and validated using MRI.

Main Results:

• The manufacturing technique successfully replicated patient-specific intimal septum walls and tears.
• In vitro experiments demonstrated that the fabricated phantoms yield physiologically accurate pressure results.
• Deep learning segmentation achieved high similarity metrics, with a Dice score up to 0.86.

Conclusions:

• The proposed deep learning-based method offers an inexpensive, reproducible, and accurate approach to fabricate patient-specific phantom models.
• This technique is suitable for detailed flow modeling in Type B aortic dissections.
• The automated fabrication process advances the study of hemodynamics in complex cardiovascular conditions.