Comparing registration methods for mapping brain change using tensor-based morphometry
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| Title: | Comparing registration methods for mapping brain change using tensor-based morphometry |
|---|---|
| Authors: | Yanovsky, Igor1,2 yanovsky@ucla.edu, Leow, Alex D.3 feuillet@ucla.edu, Lee, Suh3 slee04@ucla.edu, Osher, Stanley J.2 sjo@math.ucla.edu, Thompson, Paul M.3 thompson@loni.ucla.edu |
| Source: | Medical Image Analysis. Oct2009, Vol. 13 Issue 5, p679-700. 22p. |
| Subjects: | Image registration, Brain mapping, Magnetic resonance imaging of the brain, Image analysis, Clinical trials, Information theory |
| Abstract: | Abstract: Measures of brain changes can be computed from sequential MRI scans, providing valuable information on disease progression for neuroscientific studies and clinical trials. Tensor-based morphometry (TBM) creates maps of these brain changes, visualizing the 3D profile and rates of tissue growth or atrophy. In this paper, we examine the power of different nonrigid registration models to detect changes in TBM, and their stability when no real changes are present. Specifically, we investigate an asymmetric version of a recently proposed Unbiased registration method, using mutual information as the matching criterion. We compare matching functionals (sum of squared differences and mutual information), as well as large-deformation registration schemes (viscous fluid and inverse-consistent linear elastic registration methods versus Symmetric and Asymmetric Unbiased registration) for detecting changes in serial MRI scans of 10 elderly normal subjects and 10 patients with Alzheimer’s Disease scanned at 2-week and 1-year intervals. We also analyzed registration results when matching images corrupted with artificial noise. We demonstrated that the unbiased methods, both symmetric and asymmetric, have higher reproducibility. The unbiased methods were also less likely to detect changes in the absence of any real physiological change. Moreover, they measured biological deformations more accurately by penalizing bias in the corresponding statistical maps. [Copyright &y& Elsevier] |
| Copyright of Medical Image Analysis is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 43874755 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Comparing registration methods for mapping brain change using tensor-based morphometry – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yanovsky%2C+Igor%22">Yanovsky, Igor</searchLink><relatesTo>1,2</relatesTo><i> yanovsky@ucla.edu</i><br /><searchLink fieldCode="AR" term="%22Leow%2C+Alex+D%2E%22">Leow, Alex D.</searchLink><relatesTo>3</relatesTo><i> feuillet@ucla.edu</i><br /><searchLink fieldCode="AR" term="%22Lee%2C+Suh%22">Lee, Suh</searchLink><relatesTo>3</relatesTo><i> slee04@ucla.edu</i><br /><searchLink fieldCode="AR" term="%22Osher%2C+Stanley+J%2E%22">Osher, Stanley J.</searchLink><relatesTo>2</relatesTo><i> sjo@math.ucla.edu</i><br /><searchLink fieldCode="AR" term="%22Thompson%2C+Paul+M%2E%22">Thompson, Paul M.</searchLink><relatesTo>3</relatesTo><i> thompson@loni.ucla.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Medical+Image+Analysis%22">Medical Image Analysis</searchLink>. Oct2009, Vol. 13 Issue 5, p679-700. 22p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Image+registration%22">Image registration</searchLink><br /><searchLink fieldCode="DE" term="%22Brain+mapping%22">Brain mapping</searchLink><br /><searchLink fieldCode="DE" term="%22Magnetic+resonance+imaging+of+the+brain%22">Magnetic resonance imaging of the brain</searchLink><br /><searchLink fieldCode="DE" term="%22Image+analysis%22">Image analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Clinical+trials%22">Clinical trials</searchLink><br /><searchLink fieldCode="DE" term="%22Information+theory%22">Information theory</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Abstract: Measures of brain changes can be computed from sequential MRI scans, providing valuable information on disease progression for neuroscientific studies and clinical trials. Tensor-based morphometry (TBM) creates maps of these brain changes, visualizing the 3D profile and rates of tissue growth or atrophy. In this paper, we examine the power of different nonrigid registration models to detect changes in TBM, and their stability when no real changes are present. Specifically, we investigate an asymmetric version of a recently proposed Unbiased registration method, using mutual information as the matching criterion. We compare matching functionals (sum of squared differences and mutual information), as well as large-deformation registration schemes (viscous fluid and inverse-consistent linear elastic registration methods versus Symmetric and Asymmetric Unbiased registration) for detecting changes in serial MRI scans of 10 elderly normal subjects and 10 patients with Alzheimer’s Disease scanned at 2-week and 1-year intervals. We also analyzed registration results when matching images corrupted with artificial noise. We demonstrated that the unbiased methods, both symmetric and asymmetric, have higher reproducibility. The unbiased methods were also less likely to detect changes in the absence of any real physiological change. Moreover, they measured biological deformations more accurately by penalizing bias in the corresponding statistical maps. [Copyright &y& Elsevier] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Medical Image Analysis is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.media.2009.06.002 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 679 Subjects: – SubjectFull: Image registration Type: general – SubjectFull: Brain mapping Type: general – SubjectFull: Magnetic resonance imaging of the brain Type: general – SubjectFull: Image analysis Type: general – SubjectFull: Clinical trials Type: general – SubjectFull: Information theory Type: general Titles: – TitleFull: Comparing registration methods for mapping brain change using tensor-based morphometry Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yanovsky, Igor – PersonEntity: Name: NameFull: Leow, Alex D. – PersonEntity: Name: NameFull: Lee, Suh – PersonEntity: Name: NameFull: Osher, Stanley J. – PersonEntity: Name: NameFull: Thompson, Paul M. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2009 Type: published Y: 2009 Identifiers: – Type: issn-print Value: 13618415 Numbering: – Type: volume Value: 13 – Type: issue Value: 5 Titles: – TitleFull: Medical Image Analysis Type: main |
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