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 |
|---|---|
| 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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| Abstract | 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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| AbstractList | 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. 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. 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. 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.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. 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. [PUBLICATION ABSTRACT] |
| Author | Dahnke, Robert Yotter, Rachel Aine Gaser, Christian Thompson, Paul M. |
| AuthorAffiliation | 1 Department of Psychiatry, Friedrich‐Schiller University, Jena, Germany 2 Laboratory of Neuro Imaging, Division of Brain Mapping, Department of Neurology, UCLA School of Medicine, Los Angeles, California |
| AuthorAffiliation_xml | – name: 2 Laboratory of Neuro Imaging, Division of Brain Mapping, Department of Neurology, UCLA School of Medicine, Los Angeles, California – name: 1 Department of Psychiatry, Friedrich‐Schiller University, Jena, Germany |
| Author_xml | – sequence: 1 givenname: Rachel Aine surname: Yotter fullname: Yotter, Rachel Aine email: rachel.yotter@uni-jena.de organization: Department of Psychiatry, Friedrich-Schiller University, Jena, Germany – sequence: 2 givenname: Robert surname: Dahnke fullname: Dahnke, Robert organization: Department of Psychiatry, Friedrich-Schiller University, Jena, Germany – sequence: 3 givenname: Paul M. surname: Thompson fullname: Thompson, Paul M. organization: Laboratory of Neuro Imaging, Division of Brain Mapping, Department of Neurology, UCLA School of Medicine, Los Angeles, California – sequence: 4 givenname: Christian surname: Gaser fullname: Gaser, Christian organization: Department of Psychiatry, Friedrich-Schiller University, Jena, Germany |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=24266585$$DView record in Pascal Francis https://www.ncbi.nlm.nih.gov/pubmed/20665722$$D View this record in MEDLINE/PubMed |
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| Copyright | Copyright © 2010 Wiley‐Liss, Inc. 2015 INIST-CNRS Copyright © 2010 Wiley-Liss, Inc. |
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| Keywords | Reconstruction Nervous system diseases Radiodiagnosis Noise MRI topological defects Central nervous system topology correction surface reconstruction Nuclear magnetic resonance imaging Encephalon self-intersections spherical harmonics Spherical harmonic Corrections |
| Language | English |
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| PublicationTitle | Human brain mapping |
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Cereb Cortex 17: 1889-1898. – reference: Van Essen DC ( 2004): Surface-based approaches to spatial localization and registration in primate cerebral cortex. Neuroimage 23: S97-S107. – reference: Chung MK, Dalton KM, Li S, Evans AC, Davidson RJ ( 2007): Weighted Fourier series representation and its application to quantifying the amount of gray matter. IEEE Trans Med Imaging 26: 566-581. – reference: Kostelec PJ, Maslen DK, Dennis M, Healy J, Rockmore DN ( 2000): Computational harmonic analysis for tensor fields on the two-sphere. J Comput Phys 162: 514-535. – reference: Thompson PM, Bartzokis G, Hayashi KM, Klunder AD, Lu PH, Edwards N, Hong MS, Yu M, Geaga JA, Toga AW, Charles C, Perkins DO, McEvoy J, Hamer RM, Tohen M, Tollefson GD, Lieberman JA, Group THS ( 2009): Time-lapse mapping of cortical changes in schizophrenia with different treatments. Cereb Cortex 19: 1107-1123. – reference: Han X, Pham DL, Tosun D, Rettmann ME, Xu C, Prince JL ( 2004): 3RUISE: Cortical reconstruction using implicit surface evolution. Neuroimage 23: 997-1012. – reference: Formisano E, Esposito F, Di Salle F, Goebel R ( 2004): Cortex-based independent component analysis of fMRI time series. Magn Reson Imaging 22: 1493-1504. – reference: MacDonald D, Kabani N, Avis D, Evans AC ( 2000): Automated 3-D extraction of inner and outer surfaces of cerebral cortex from MRI. Neuroimage 12: 340-356. – reference: Ballmaier M, Kumar A, Thompson PM, Narr KL, Lavretsky H, Estanol L, DeLuca H, Toga AW ( 2004): Localizing gray matter deficits in late-onset depression using computational cortical pattern matching methods. 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| SubjectTerms | Algorithms Biological and medical sciences Brain - anatomy & histology Brain mapping Brain Mapping - methods Cortex Data processing Electrocardiography. Vectocardiography Electrodiagnosis. Electric activity recording Humans Image Processing, Computer-Assisted - methods Investigative techniques, diagnostic techniques (general aspects) Magnetic Resonance Imaging Medical sciences MRI Nervous system noise Patched protein Radiodiagnosis. Nmr imagery. Nmr spectrometry self-intersections spherical harmonics Structure-function relationships surface reconstruction topological defects topology correction |
| Title | Topological correction of brain surface meshes using spherical harmonics |
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