Iconic registration
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Iconic registration |
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L. Freire and J.-F. Mangin.
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| This paper describes several experiments that prove that standard motion correction methods may induce spurious activations in some motion-free fMRI studies. This artifact stems from the fact that activated areas behave like biasing outliers for the difference of square-based measures usually driving such registration methods. This effect is demonstrated first using a motion-free simulated time series including artificial activation-like signal changes. Several additional simulations explore the influence of activation amplitude and extent. The effect is finally highlighted on an actual time series obtained from a 3-T magnet. All the experiments are performed using four different realignment methods, which allows us to show that the problem may be overcome by methods based on a robust similarity measure like mutual information. |
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P. Cachier, J.-F. Mangin, X. Pennec, D. Rivière,
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| In this article we merge point feature and intensity-based registration in a single algorithm to tackle the problem of multiple brain registration. Because of the high variability of the shape of the cortex across individuals, there exist geometrical ambiguities in the registration process that an intensity measure alone is unable to solve. This problem can be tackled using anatomical knowledge. First, we automatically segment and label the whole set of the cortical sulci, with a non-parametric approach that enables the capture of their highly variable shape and topology. Then, we develop a registration energy that merges intensity and feature point matching. Its minimization leads to a linear combination of a dense smooth vector field and radial basis functions. We use and process differently the bottom line of the sulci from its upper border, whose localization is even more variable across individuals. We show that the additional sulcal energy improves the registration of the cortical sulci, while still keeping the transformation smooth and one-to-one. |
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J.-F. Mangin, C. Poupon, C. A. Clark, |
| This paper presents a new procedure to estimate the diffusion tensor from a sequence of diffusion-weighted images. The first step of this procedure consists of the correction of the distortions usually induced by eddy-current related to the large diffusion-sensitizing gradients. This correction algorithm relies on the maximization of mutual information to estimate the three parameters of a geometric distortion model inferred from the acquisition principle. The second step of the procedure amounts to replacing the standard least squares-based approach by the Geman-McLure M-estimator, in order to reduce outlier-related artefacts. Several experiments prove that the whole procedure highly improves the quality of the final diffusion maps. |
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J.-F. Mangin, V. Frouin, I. Bloch,
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| We propose a fully nonsupervised methodology dedicated to the fast registration of positron emission tomography (PET) and magnetic resonance images of the brain. First, discrete representations of the surfaces of interest (head or brain surface) are automatically extracted from both images. Then, a shape-independent surface-matching algorithm gives a rigid body transformation, which allows the transfer of information between both modalities. A three-dimensional (3D) extension of the chamfer-matching principle makes up the core of this surface-matching algorithm. The optimal transformation is inferred from the minimization of a quadratic generalized distance between discrete surfaces, taking into account between-modality differences in the localization of the segmented surfaces. The minimization process is efficiently performed via the precomputation of a 3D distance map. Validation studies using a dedicated brain-shaped phantom have shown that the maximum registration error was of the order of the PET pixel size (2 mm) for the wide variety of tested configurations. The software is routinely used today in a clinical context by the physicians of the Service Hospitalier Frederic Joliot (> 150 registrations performed). The entire registration process requires approximately 5 min on a conventional workstation. |