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Image-to-mesh conversion method for multi-tissue medical image computing simulations
Fotis Drakopoulos1, Yixun Liu1, Kevin Garner1
1Center for Real-Time Computing, Department of Computer Science, Old Dominion University, Norfolk, VA, United States of America.
Engineering with Computers
|December 24, 2024
Summary
CBC3D converts medical images into high-quality 3D meshes for simulations and surgery. This method enhances mesh fidelity and reduces element count, outperforming existing image-to-mesh techniques.
Area of Science:
- Medical imaging
- Computational geometry
- Scientific simulation
Background:
- Converting 3D medical images to 3D meshes is crucial for simulations and image-guided surgery.
- Existing methods face challenges in balancing mesh quality, fidelity, and computational efficiency.
Purpose of the Study:
- To present CBC3D, a novel image-to-mesh conversion method.
- To improve mesh quality and fidelity for predictive simulations and surgical applications.
Main Methods:
- CBC3D discretizes segmented images using an adaptive Body-Centered Cubic (BCC) mesh.
- Tetrahedral meshes are converted to mixed-element meshes (tetrahedra, pentahedra, hexahedra) to reduce element count.
- Mesh surfaces are deformed using energy minimization and multi-material point-based registration for improved fidelity.
Main Results:
- CBC3D achieves high mesh fidelity, closely matching physical image boundaries.
- The method maintains a reasonably low element count.
- Generated meshes exhibit good element quality, suitable for simulations.
Conclusions:
- CBC3D offers a superior image-to-mesh conversion approach.
- The method effectively balances mesh fidelity, element quality, and computational efficiency.
- CBC3D shows promise for advancing medical simulations and image-guided surgical procedures.
