MRI Guided 3D Mesh Generation and Registration for Biological Modeling

[+] Author and Article Information
James Qingyang Zhang

 Worcester Polytechnic Institute, Worcester MAzqy@wpi.edu

John M. Sullivan

 Worcester Polytechnic Institute, Worcester MAsullivan@wpi.edu

Hamid Ghadyani

 Worcester Polytechnic Institute, Worcester MAhamid@wpi.edu

Donna M. Meyer

 University of Rhode Island, Kingston, RIdmmeyer@egr.uri.edu

J. Comput. Inf. Sci. Eng 5(4), 283-290 (Jul 06, 2005) (8 pages) doi:10.1115/1.2052828 History: Received October 01, 2004; Revised July 06, 2005

An accurate three-dimensional (3D) mesh of biological models is fundamental for analysis and treatment simulations. Generally noninvasive magnetic resonance image (MRI) data are taken as the input for the simulation. The topologic relationship of anatomy is extracted from MR images through segmentation processes. To accelerate the biological modeling phase, template surface and volume meshes are generated based on MR images and∕or anatomical atlases (e.g., brain atlas, etc.). The boundary surfaces are extracted from segmented regions on the image slices, which are used as the input for 3D volume mesh generation. An intuitive graphic user interface was developed for biomedical applications. It integrated MRI data manipulation with surface mesh and volume mesh generators. Image volume and mesh geometries are registered in the MRI working space. As the core component of the system, a robust 3D mesh generation approach is presented. It is capable of describing irregular geometries exhibiting concave and convex surfaces. It uses deltahedral building blocks for volume mesh generation and creates high-quality, regular-shaped tetrahedral mesh elements. The approach supports multiple levels of localized refinement without reducing the overall mesh quality. The validity of this new mesh generation strategy and implementation is demonstrated via the medical applications in brain vasculature modeling, multimodality imaging for breast cancer detection, and numerous anatomically accurate models presented. Multiple material boundaries are preserved in each mesh with fidelity.

Copyright © 2005 by American Society of Mechanical Engineers
Your Session has timed out. Please sign back in to continue.



Grahic Jump Location
Figure 14

The microwave imaging result with breast MRI

Grahic Jump Location
Figure 3

T2O1 deltahedral building block

Grahic Jump Location
Figure 4

Splitting a deltahedron block into 6 tetrahedra

Grahic Jump Location
Figure 5

The virtual tetrahedra for knitting

Grahic Jump Location
Figure 6

Searching for optimal moving direction

Grahic Jump Location
Figure 7

The overall time complexity

Grahic Jump Location
Figure 1

The patterns of bricks to form tetrahedral elements

Grahic Jump Location
Figure 2

Some isotropic blocks

Grahic Jump Location
Figure 8

Block diagram of the GUI for the mesh generation in medical applications

Grahic Jump Location
Figure 9

The process of image guided mesh generation and postprocessing

Grahic Jump Location
Figure 10

Tetrahedral mesh for human brain with tumor: (a) registered surface boundary, (b) volume mesh

Grahic Jump Location
Figure 11

The mesh quality in aspect ratio for the human brain model

Grahic Jump Location
Figure 12

Mesh generation for the brain vasculature model: (a) the surface registered with MRI, (b) volume mesh

Grahic Jump Location
Figure 13

MRE breast tissue model: (a) T2* MRI with boundary surface, (b) volume mesh displayed with shear modulus




Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In