Statistically assisted fluid image registration algorithm - SAFIRA
C. Brun,Natasha Lepore,Xavier Pennec,Yi-Yu Chou,Agatha D. Lee,Marina Barysheva,Greig I. de Zubicaray,Katie L. McMahon,Margaret J. Wright,Paul M. Thompson +9 more
- 14 Apr 2010
- Vol. 2010, pp 364-367
TL;DR: A new Statistically Assisted Fluid Registration Algorithm for brain images (SAFIRA) is developed and validated and compared the power of the different algorithms using tensor-based morphometry-a technique to analyze local volumetric differences in brain structure- applied to 46 3D brain scans from healthy monozygotic twins.
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Abstract: In this paper, we develop and validate a new Statistically Assisted Fluid Registration Algorithm (SAFIRA) for brain images. A non-statistical version of this algorithm was first implemented in [2] and re-formulated using Lagrangian mechanics in [3]. Here we extend this algorithm to 3D: given 3D brain images from a population, 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 regularizing (i.e., the non-conservative Lagrangian) terms, creating four versions of the algorithm. We evaluated the accuracy of each algorithm variant using the manually labeled LPBA40 dataset, which provides us with ground truth anatomical segmentations. We also compared the power of the different algorithms using tensor-based morphometry-a technique to analyze local volumetric differences in brain structure- applied to 46 3D brain scans from healthy monozygotic twins.
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Citations
A Lagrangian formulation for statistical fluid registration
C. Brun,Natasha Lepore,Xavier Pennec,Yi-Yu Chou,Agatha D. Lee,Marina Barysheva,Greig I. de Zubicaray,Katie L. McMahon,Margaret J. Wright,Arthur W. Toga,Paul M. Thompson +10 more
- 28 Jun 2009
TL;DR: The Riemannian fluid algorithm was reformulated, and a Lagrangian framework was used to incorporate 0th and 1st order statistics in the regularization process to combine the advantages of a large-deformation fluid matching approach with empirical statistics on population variability in anatomy.
A 3D Statistical Fluid Registration Algorithm
C. Brun,Natasha Lepore,Xavier Pennec,Yi-Yu Chou,Greig I. de Zubicaray,Katie L. McMahon,Margaret J. Wright,James C. Gee,Paul M. Thompson +8 more
- 01 Jan 2010
TL;DR: This paper compares the vector-statistics version of SAFIRA to the non-statistical one and to the widely-used fluid registration, which is based on the Navier-Stokes equation.
A Matlab User Interface for the Statistically-Assisted Fluid Registration Algorithm and Tensor-Based Morphometry
Fernando Yepes-Calderon,C. Brun,Nishita Sant,Paul M. Thompson,Natasha Lepore +4 more
- 28 Jan 2015
TL;DR: A new, intuitive, easy to use, Matlab-based graphical user interface for SAFIRA’s multivariate TBM, which generates different choices for the TBM statistics, including both the traditional univariate statistics on the Jacobian matrix, and comparison of the full deformation tensors.
Bayesian tensor regression using the Tucker decomposition for sparse spatial modeling
Daniela Michele Spencer,Rajarshi Guhaniyogi,Russell T. Shinohara,Raquel Prado +3 more
- 09 Mar 2022
TL;DR: A Bayesian method is proposed to model a scalar response with a tensor covariate using the Tucker tensor decomposition in order to retain the spatial relationship within a Tensor coefficient, while reducing the number of parameters varying within the model and applying regularization methods.
Bayesian scalar-on-tensor regression using the Tucker decomposition for sparse spatial modeling finds promising results analyzing neuroimaging data
TL;DR: A Bayesian method is proposed to model a scalar response with a Tensor covariate using the Tucker tensor decomposition in order to retain the spatial relationship within a tensor coefficient, while reducing the number of parameters varying within the model and applying regularization methods.
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