An Efficient Algorithm for Mapping Imaging Data to 3D Unstructured Grids in Computational Biomechanics

Journal Article · Tue Jan 01 00:00:00 EST 2013 · International Journal for Numerical Methods in Biomedical Engineering, 29(1):1-16

DOI:https://doi.org/10.1002/cnm.2489· OSTI ID:1060095

Einstein, Daniel R.; Kuprat, Andrew P.; Jiao, Xiangmin; Carson, James P.; Einstein, David M.; Corley, Richard A.; Jacob, Rick E.

Geometries for organ scale and multiscale simulations of organ function are now routinely derived from imaging data. However, medical images may also contain spatially heterogeneous information other than geometry that are relevant to such simulations either as initial conditions or in the form of model parameters. In this manuscript, we present an algorithm for the efficient and robust mapping of such data to imaging based unstructured polyhedral grids in parallel. We then illustrate the application of our mapping algorithm to three different mapping problems: 1) the mapping of MRI diffusion tensor data to an unstuctured ventricular grid; 2) the mapping of serial cyro-section histology data to an unstructured mouse brain grid; and 3) the mapping of CT-derived volumetric strain data to an unstructured multiscale lung grid. Execution times and parallel performance are reported for each case.

• Research Organization: Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1060095
• Report Number(s): PNNL-SA-85247
• Journal Information: International Journal for Numerical Methods in Biomedical Engineering, 29(1):1-16, Journal Name: International Journal for Numerical Methods in Biomedical Engineering, 29(1):1-16
• Country of Publication: United States
• Language: English