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PubMed2004

Registration-based interpolation.

A method is presented to interpolate between neighboring slices in a grey-scale tomographic data set. Spatial correspondence between adjacent slices is established using a nonrigid registration algorithm based on B-splines which optimizes the normalized mutual information similarity measure. Linear interpolation of the image intensities is then carried out along the directions calculated by the registration algorithm. The registration-based method is compared to both standard linear interpolation and shape-based interpolation in 20 tomographic data sets. Results show that the proposed method statistically significantly outperforms both linear and shape-based interpolation.

Algorithms↗

PubMed2000

Nonlinear smoothing for reduction of systematic and random errors in diffusion tensor imaging.

Calculation and sorting of the eigenvectors of diffusion using diffusion tensor imaging has previously been shown to be sensitive to noise levels in the acquired data. This sensitivity manifests as random and systematic errors in the diffusion eigenvalues and derived parameters such as indices of anisotropy. An optimized application of nonlinear smoothing techniques to diffusion data prior to calculation of the diffusion tensor is shown to reduce both random and systematic errors, while causing little blurring of anatomical structures. Conversely, filtering applied to calculated images of fractional anisotropy is shown to fail in reducing systematic errors and in recovering anatomical detail. Using both real and simulated brain data sets, it is demonstrated that this approach has the potential to allow acquisition of data that would otherwise be too noisy to be of use.

Anisotropy↗