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A new inter-individual functional mapping scheme based on a local non linear registration method
METHODS
- ANALYSIS
A new inter-individual functional mapping scheme based on a local non linear registration method Isabelle Corouge*, Pierre Hellier*, Pierre TOULOUSE?, Christian Barillot*
Bernard Gibaudt,
“IRISA, INRIA-CNRS, Vista Project, Campus de Beaulieu, 35042 Rennes Cx, France TIDM laboratory, Faculty of Medicine, University of Rennes I, 35043 Rennes Cx, France Context
and Objectives
This work is a contribution to solve the problem of inter-individual normalization of functional activations w.r.t. the anatomical substrate. This problem can be addressed in different ways. Global registration methods (rigid or non-rigid) are commonly used to register anatomical data, the registration information (matrices or deformation fields) is applied to the functional activations. Medical image analysis methods allow to rely on fine representation of cortical features like cortical sulci. These sulci are relevant landmarks for functional activities. They can be used to register functional data from a population of individuals. This work proposes a fusion scheme of anatomical and functional information based on local anatomical references (cortical sulci) and on inter-individual probabilistic variations of these references (statistical modes of deformations). We compare this fusion scheme with other classical global matching approaches. Methods A local system is defined and a statistical analysis of sulci shapes is performed. Cortical sulci are extracted from 3D MRI using the active-ribbon method [LeGoualher]. They are then realigned to the local geometrical referential based on the first and second order moments, defining a local rigid registration method (Rl). A statistical analysis is carried out (Principal Component Analysis) to analyze the sulci shapes in order to derive the variation modes around the mean shape [Barillot]. Four registration methods are studied to merge functional activations from the population of subjects: . The local rigid registration method (Rl) . A local non-linear registration method (R2): a thinplate spline transformation is applied to the functional data. It uses the variation modes of each sulcus as constraints in order to extend the deformation to a local neighborhood. l Comparison with two global registration methods: the Talairach approach and a non-linear registration method based on optical Results
Proportional Squaring (R3) as a piecewise aff& flow and a robust optimization scheme [Hellier]
registration (R4).
and discussion
The experimental study has been performed on a database of 18 volunteers (young right-handed healthy males) who underwent a Tl-MR SPGR 3D study and a MEG somatosensory exploration (stimulation of three fingers of the right hand). MEG current dipoles were reconstructed using a spatiotemporal algorithm [Schwartz] and selected by choosing the most significant one in the 45+/-15ms window. The left central sulci were segmented from MRI and used to produce the statistical shape model (mean shape and variation modes). The four registration methods (Rl-R4) have been applied to MEG data. The local non-linear registration method using thin-plate splines interpolation and variation modes (R2) brings together the current dipoles around the reference mean sulcus more than the other ones (figures l-2). This visual result is confirmed by computing the dipole localization covariance along each axis. The three other methods (Rl, R3, R4) lead to similar dispersions. The fusion scheme proposed in this paper is generic and can also be applied to various functional data such as MRI. References Barillot, C. et al., SPIE Medical Imaging: Image Processing, vol. 3661:312-321, Hellier, P. et al., IEEE CVPR, vol.I1:270-275, 2000 Le Goualher, G. et al IJPRAI, 11(8):1295-1315, 1997. Schwartz, D. et al., Brain Topography, 11(4):279-289, 1999.
SlOO
1999.
- ANALYSIS
A new inter-individual functional mapping scheme based on a local non linear registration method Isabelle Corouge*, Pierre Hellier*, Pierre TOULOUSE?, Christian Barillot*
Bernard Gibaudt,
“IRISA, INRIA-CNRS, Vista Project, Campus de Beaulieu, 35042 Rennes Cx, France TIDM laboratory, Faculty of Medicine, University of Rennes I, 35043 Rennes Cx, France Context
and Objectives
This work is a contribution to solve the problem of inter-individual normalization of functional activations w.r.t. the anatomical substrate. This problem can be addressed in different ways. Global registration methods (rigid or non-rigid) are commonly used to register anatomical data, the registration information (matrices or deformation fields) is applied to the functional activations. Medical image analysis methods allow to rely on fine representation of cortical features like cortical sulci. These sulci are relevant landmarks for functional activities. They can be used to register functional data from a population of individuals. This work proposes a fusion scheme of anatomical and functional information based on local anatomical references (cortical sulci) and on inter-individual probabilistic variations of these references (statistical modes of deformations). We compare this fusion scheme with other classical global matching approaches. Methods A local system is defined and a statistical analysis of sulci shapes is performed. Cortical sulci are extracted from 3D MRI using the active-ribbon method [LeGoualher]. They are then realigned to the local geometrical referential based on the first and second order moments, defining a local rigid registration method (Rl). A statistical analysis is carried out (Principal Component Analysis) to analyze the sulci shapes in order to derive the variation modes around the mean shape [Barillot]. Four registration methods are studied to merge functional activations from the population of subjects: . The local rigid registration method (Rl) . A local non-linear registration method (R2): a thinplate spline transformation is applied to the functional data. It uses the variation modes of each sulcus as constraints in order to extend the deformation to a local neighborhood. l Comparison with two global registration methods: the Talairach approach and a non-linear registration method based on optical Results
Proportional Squaring (R3) as a piecewise aff& flow and a robust optimization scheme [Hellier]
registration (R4).
and discussion
The experimental study has been performed on a database of 18 volunteers (young right-handed healthy males) who underwent a Tl-MR SPGR 3D study and a MEG somatosensory exploration (stimulation of three fingers of the right hand). MEG current dipoles were reconstructed using a spatiotemporal algorithm [Schwartz] and selected by choosing the most significant one in the 45+/-15ms window. The left central sulci were segmented from MRI and used to produce the statistical shape model (mean shape and variation modes). The four registration methods (Rl-R4) have been applied to MEG data. The local non-linear registration method using thin-plate splines interpolation and variation modes (R2) brings together the current dipoles around the reference mean sulcus more than the other ones (figures l-2). This visual result is confirmed by computing the dipole localization covariance along each axis. The three other methods (Rl, R3, R4) lead to similar dispersions. The fusion scheme proposed in this paper is generic and can also be applied to various functional data such as MRI. References Barillot, C. et al., SPIE Medical Imaging: Image Processing, vol. 3661:312-321, Hellier, P. et al., IEEE CVPR, vol.I1:270-275, 2000 Le Goualher, G. et al IJPRAI, 11(8):1295-1315, 1997. Schwartz, D. et al., Brain Topography, 11(4):279-289, 1999.
SlOO
1999.