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Image-to-mesh conversion method for multi-tissue medical image computing simulations. | LitMetric

Image-to-mesh conversion method for multi-tissue medical image computing simulations.

Eng Comput

Center for Real-Time Computing, Department of Computer Science, Old Dominion University, Norfolk, VA, United States of America.

Published: December 2024

AI Article Synopsis

Article Abstract

Converting a three-dimensional medical image into a 3D mesh that satisfies both the quality and fidelity constraints of predictive simulations and image-guided surgical procedures remains a critical problem. Presented is an image-to-mesh conversion method called CBC3D. It first discretizes a segmented image by generating an adaptive Body-Centered Cubic (BCC) mesh of high-quality elements. Next, the tetrahedral mesh is converted into a mixed-element mesh of tetrahedra, pentahedra, and hexahedra to decrease element count while maintaining quality. Finally, the mesh surfaces are deformed to their corresponding physical image boundaries, improving the mesh's fidelity. The deformation scheme builds upon the ITK open-source library and is based on the concept of energy minimization, relying on a multi-material point-based registration. It uses non-connectivity patterns to implicitly control the number of extracted feature points needed for the registration and, thus, adjusts the trade-off between the achieved mesh fidelity and the deformation speed. We compare CBC3D with four widely used and state-of-the-art homegrown image-to-mesh conversion methods from industry and academia. Results indicate that the CBC3D meshes (i) achieve high fidelity, (ii) keep the element count reasonably low, and (iii) exhibit good element quality.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11666122PMC
http://dx.doi.org/10.1007/s00366-024-02023-wDOI Listing

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