Topological correction of brain surface meshes using spherical harmonics
Surface reconstruction methods allow advanced analysis of structural and functional brain data beyond what can be achieved using volumetric images alone. Automated generation of cortical surface meshes from 3D brain MRI often leads to topological defects and geometrical artifacts that must be correc...
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Published in | Human brain mapping Vol. 32; no. 7; pp. 1109 - 1124 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Hoboken
Wiley Subscription Services, Inc., A Wiley Company
01.07.2011
Wiley-Liss John Wiley & Sons, Inc |
Subjects | |
Online Access | Get full text |
ISSN | 1065-9471 1097-0193 1097-0193 |
DOI | 10.1002/hbm.21095 |
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Summary: | Surface reconstruction methods allow advanced analysis of structural and functional brain data beyond what can be achieved using volumetric images alone. Automated generation of cortical surface meshes from 3D brain MRI often leads to topological defects and geometrical artifacts that must be corrected to permit subsequent analysis. Here, we propose a novel method to repair topological defects using a surface reconstruction that relies on spherical harmonics. First, during reparameterization of the surface using a tiled platonic solid, the original MRI intensity values are used as a basis to select either a “fill” or “cut” operation for each topological defect. We modify the spherical map of the uncorrected brain surface mesh, such that certain triangles are favored while searching for the bounding triangle during reparameterization. Then, a low‐pass filtered alternative reconstruction based on spherical harmonics is patched into the reconstructed surface in areas that previously contained defects. Self‐intersections are repaired using a local smoothing algorithm that limits the number of affected points to less than 0.1% of the total, and as a last step, all modified points are adjusted based on the T1 intensity. We found that the corrected reconstructions have reduced distance error metrics compared with a “gold standard” surface created by averaging 12 scans of the same brain. Ninety‐three percent of the topological defects in a set of 10 scans of control subjects were accurately corrected. The entire process takes 6–8 min of computation time. Further improvements are discussed, especially regarding the use of the T1‐weighted image to make corrections. Hum Brain Mapp, 2011. © 2010 Wiley‐Liss, Inc. |
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Bibliography: | istex:E13A5ADA5AC996B863739BD2C084BBC8E94B4C97 ark:/67375/WNG-PXK92HZ3-Z NIH - No. EB008432; No. EB008281; No. EB007813; No. HD050735 ArticleID:HBM21095 German Bundesministerium für Bildung und Forschung - No. BMBF 01EV0709; No. BMBF 01GW0740 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23 |
ISSN: | 1065-9471 1097-0193 1097-0193 |
DOI: | 10.1002/hbm.21095 |