Purpose: Voxel based morphometry (VBM) is increasingly being used to detect diffusion tensor
(DT) image abnormalities in patients for different pathologies. An important requisite for these VBM studies
is the use of a high-dimensional, non-rigid coregistration technique, which is able to align both the spatial and
the orientational information. Recent studies furthermore indicate that high-dimensional DT information should
be included during coregistration for an optimal alignment. In this context, a population based DTI atlas
is created that preserves the orientational DT information robustly and contains a minimal bias towards any
specific individual data set. Methods: A ground truth evaluation method is developed using a single subject
DT image that is deformed with 20 deformation fields. Thereafter, an atlas is constructed based on these 20
resulting images. Thereby, the non-rigid coregistration algorithm is based on a viscous fluid model and on mutual
information. The fractional anisotropy (FA) maps as well as the DT elements are used as DT image information
during the coregistration algorithm, in order to minimize the orientational alignment inaccuracies. Results:
The population based DT atlas is compared with the ground truth image using accuracy and precision measures
of spatial and orientational dependent metrics. Results indicate that the population based atlas preserves the
orientational information in a robust way. Conclusion: A subject independent population based DT atlas is
constructed and evaluated with a ground truth method. This atlas contains all available orientational information
and can be used in future VBM studies as a reference system.
In this article, we propose a new registration method, based on a statistical analysis of deformation fields. At first, a set of MRI brain images was registered using a viscous fluid algorithm. The obtained deformation fields are then used to calculate a Principal Component Analysis (PCA) based decomposition. Since PCA models the deformations as a linear combination of statistically uncorrelated principal components, new deformations can be created by changing the coefficients in the linear combination. We then use the PCA representation of the deformation fields to non-rigidly align new sets of images. We use a gradient descent method to adjust the coefficients of the principal components, such that the resulting deformation maximizes the mutual information between the deformed image and an atlas image. The results of our method are promising. Viscous fluid registrations of new images can be recovered with an accuracy of about half a voxel. Better results can be obtained by using a more extensive database of learning images (we only used 84). Also, the optimization method used here can be improved, especially to shorten computation time.
In this paper, we evaluate different non-rigid image registration methodologies in the context of atlas-based brain image segmentation. Three non-rigid voxel-based registration regularization schemes (viscous fluid, elastic and curvature-based registration) combined with the mutual information similarity measure are compared. We conduct large-scale atlas-based segmentation experiments on a set of 20 anatomically labelled MR brain images in order to find the optimal parameter settings for each scheme. The performance of the optimal registration schemes is evaluated in their capability of accurately segmenting 49 different brain sub-structures of varying size and shape.