DocumentCode :
1311069
Title :
A Nonconservative Lagrangian Framework for Statistical Fluid Registration—SAFIRA
Author :
Brun, Caroline C. ; Leporé, Natasha ; Pennec, Xavier ; Chou, Yi-Yu ; Lee, Agatha D. ; De Zubicaray, Greig ; McMahon, Katie ; Wright, Margaret J. ; Gee, James C. ; Thompson, Paul M.
Author_Institution :
UCLA Sch. of Med., Lab. of Neuro Imaging, Los Angeles, CA, USA
Volume :
30
Issue :
2
fYear :
2011
Firstpage :
184
Lastpage :
202
Abstract :
In this paper, we used a nonconservative Lagrangian mechanics approach to formulate a new statistical algorithm for fluid registration of 3-D brain images. This algorithm is named SAFIRA, acronym for statistically-assisted fluid image registration algorithm. A nonstatistical version of this algorithm was implemented , where the deformation was regularized by penalizing deviations from a zero rate of strain. In , the terms regularizing the deformation included the covariance of the deformation matrices (Σ) and the vector fields (q) . Here, we used a Lagrangian framework to reformulate this algorithm, showing that the regularizing terms essentially allow nonconservative work to occur during the flow. Given 3-D brain images from a group of subjects, vector fields and their corresponding deformation matrices are computed in a first round of registrations using the nonstatistical implementation. Covariance matrices for both the deformation matrices and the vector fields are then obtained and incorporated (separately or jointly) in the nonconservative terms, creating four versions of SAFIRA. We evaluated and compared our algorithms´ performance on 92 3-D brain scans from healthy monozygotic and dizygotic twins; 2-D 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 su- gests the advantages of this approach for large-scale neuroimaging studies.
Keywords :
biomechanics; biorheology; brain; deformation; image registration; medical image processing; neurophysiology; statistical analysis; 3D brain images; SAFIRA algorithm; corpus callosum; deformation; dizygotic twins; monozygotic twins; neuroimaging; nonconservative lagrangian framework; statistical fluid registration; statistically-assisted fluid image registration algorithm; tensor-based morphometry; Brain modeling; Equations; Measurement; Statistical analysis; Tensile stress; Three dimensional displays; Fluid mechanics; Riemannian framework; nonconservative Lagrangian; registration; statistics; tensors; twins; Adult; Algorithms; Brain; Female; Humans; Image Processing, Computer-Assisted; Magnetic Resonance Imaging; Male; Reproducibility of Results; Twins;
fLanguage :
English
Journal_Title :
Medical Imaging, IEEE Transactions on
Publisher :
ieee
ISSN :
0278-0062
Type :
jour
DOI :
10.1109/TMI.2010.2067451
Filename :
5560855
Link To Document :
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