Spatio-Temporal Regularization for Longitudinal Registration to Subject-Specific 3d Template.
Neurodegenerative diseases such as Alzheimer's disease present subtle anatomical brain changes before the appearance of clinical symptoms. Manual structure segmentation is long and tedious and although automatic methods exist, they are often performed in a cross-sectional manner where each time...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2015-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0133352 |
| _version_ | 1818400000124649472 |
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| author | Nicolas Guizard Vladimir S Fonov Daniel García-Lorenzo Kunio Nakamura Bérengère Aubert-Broche D Louis Collins |
| author_facet | Nicolas Guizard Vladimir S Fonov Daniel García-Lorenzo Kunio Nakamura Bérengère Aubert-Broche D Louis Collins |
| author_sort | Nicolas Guizard |
| collection | DOAJ |
| description | Neurodegenerative diseases such as Alzheimer's disease present subtle anatomical brain changes before the appearance of clinical symptoms. Manual structure segmentation is long and tedious and although automatic methods exist, they are often performed in a cross-sectional manner where each time-point is analyzed independently. With such analysis methods, bias, error and longitudinal noise may be introduced. Noise due to MR scanners and other physiological effects may also introduce variability in the measurement. We propose to use 4D non-linear registration with spatio-temporal regularization to correct for potential longitudinal inconsistencies in the context of structure segmentation. The major contribution of this article is the use of individual template creation with spatio-temporal regularization of the deformation fields for each subject. We validate our method with different sets of real MRI data, compare it to available longitudinal methods such as FreeSurfer, SPM12, QUARC, TBM, and KNBSI, and demonstrate that spatially local temporal regularization yields more consistent rates of change of global structures resulting in better statistical power to detect significant changes over time and between populations. |
| first_indexed | 2024-12-14T07:29:37Z |
| format | Article |
| id | doaj.art-77499305259b4567ab4711a63a81a886 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-14T07:29:37Z |
| publishDate | 2015-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-77499305259b4567ab4711a63a81a8862022-12-21T23:11:25ZengPublic Library of Science (PLoS)PLoS ONE1932-62032015-01-01108e013335210.1371/journal.pone.0133352Spatio-Temporal Regularization for Longitudinal Registration to Subject-Specific 3d Template.Nicolas GuizardVladimir S FonovDaniel García-LorenzoKunio NakamuraBérengère Aubert-BrocheD Louis CollinsNeurodegenerative diseases such as Alzheimer's disease present subtle anatomical brain changes before the appearance of clinical symptoms. Manual structure segmentation is long and tedious and although automatic methods exist, they are often performed in a cross-sectional manner where each time-point is analyzed independently. With such analysis methods, bias, error and longitudinal noise may be introduced. Noise due to MR scanners and other physiological effects may also introduce variability in the measurement. We propose to use 4D non-linear registration with spatio-temporal regularization to correct for potential longitudinal inconsistencies in the context of structure segmentation. The major contribution of this article is the use of individual template creation with spatio-temporal regularization of the deformation fields for each subject. We validate our method with different sets of real MRI data, compare it to available longitudinal methods such as FreeSurfer, SPM12, QUARC, TBM, and KNBSI, and demonstrate that spatially local temporal regularization yields more consistent rates of change of global structures resulting in better statistical power to detect significant changes over time and between populations.https://doi.org/10.1371/journal.pone.0133352 |
| spellingShingle | Nicolas Guizard Vladimir S Fonov Daniel García-Lorenzo Kunio Nakamura Bérengère Aubert-Broche D Louis Collins Spatio-Temporal Regularization for Longitudinal Registration to Subject-Specific 3d Template. PLoS ONE |
| title | Spatio-Temporal Regularization for Longitudinal Registration to Subject-Specific 3d Template. |
| title_full | Spatio-Temporal Regularization for Longitudinal Registration to Subject-Specific 3d Template. |
| title_fullStr | Spatio-Temporal Regularization for Longitudinal Registration to Subject-Specific 3d Template. |
| title_full_unstemmed | Spatio-Temporal Regularization for Longitudinal Registration to Subject-Specific 3d Template. |
| title_short | Spatio-Temporal Regularization for Longitudinal Registration to Subject-Specific 3d Template. |
| title_sort | spatio temporal regularization for longitudinal registration to subject specific 3d template |
| url | https://doi.org/10.1371/journal.pone.0133352 |
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