A non-conservative Lagrangian framework for statistical fluid registration: SAFIRA

Brun, Caroline C., Lepore, Natasha, Pennec, Xavier, Chou, Yi-Yu, Lee, Agatha D., de Zubicaray, Greig, McMahon, Katie, Wright, Margaret J., Gee, James C. and Thompson, Paul M. (2011) A non-conservative Lagrangian framework for statistical fluid registration: SAFIRA. IEEE Transactions on Medical Imaging, 30 2: 184-202. doi:10.1109/TMI.2010.2067451

Author Brun, Caroline C.
Lepore, Natasha
Pennec, Xavier
Chou, Yi-Yu
Lee, Agatha D.
de Zubicaray, Greig
McMahon, Katie
Wright, Margaret J.
Gee, James C.
Thompson, Paul M.
Title A non-conservative Lagrangian framework for statistical fluid registration: SAFIRA
Journal name IEEE Transactions on Medical Imaging   Check publisher's open access policy
ISSN 0278-0062
Publication date 2011-02
Year available 2010
Sub-type Article (original research)
DOI 10.1109/TMI.2010.2067451
Volume 30
Issue 2
Start page 184
End page 202
Total pages 9
Place of publication Piscataway, N.J., U.S.A.
Publisher IEEE
Collection year 2011
Language eng
Abstract In this paper, we used a non-conservative Lagrangian mechanics approach to formulate a new statistical algorithm for fluid registration of 3D brain images. This algorithm is named SAFIRA, acronym for Statistically-Assisted Fluid Image Registration Algorithm. A non-statistical version of this algorithm was implemented [9], where the deformation was regularized by penalizing deviations from a zero rate of strain. In [9], the terms regularizing the deformation included the covariance of the deformation matrices () and the vector fields (q). Here we used a Lagrangian framework to re-formulate this algorithm, showing that the regularizing terms essentially allow non-conservative work to occur during the flow. Given 3D brain images from a group of subjects, vector fields and their corresponding deformation matrices are computed in a first round of registrations using the non-statistical implementation. Covariance matrices for both the deformation matrices and the vector fields are then obtained and incorporated (separately or jointly) in the non-conservative terms, creating four versions of SAFIRA. We evaluated and compared our algorithms performance on 92 3D brain scans from healthy monozygotic and dizygotic twins; 2D validations are also shown for corpus callosum shapes delineated at midline in the same subjects. After preliminary tests to demonstrate each method, we compared their detection power using tensor-based morphometry (TBM), a technique to analyze local volumetric differences in brain structure. We compared the accuracy of each algorithm variant using various statistical metrics derived from the images and deformation fields. All these tests were also run with a traditional fluid method, which has been quite widely used in TBM studies. The versions incorporating vector-based empirical statistics on brain variation were consistently more accurate than their counterparts, when used for automated volumetric quantification in new brain images. This suggests the advantages of this approach for largescale neuroimaging studies.
Keyword Brain modeling
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Published online 2 September 2010

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2011 Collection
Centre for Advanced Imaging Publications
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Citation counts: TR Web of Science Citation Count  Cited 7 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 10 times in Scopus Article | Citations
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Created: Thu, 28 Oct 2010, 16:08:22 EST by Sandrine Ducrot on behalf of Centre for Advanced Imaging