Title: On the Impact of a 3d Space Varying Coefficient Model on Diffusion
Tensor Estimation and Fiber Tractography.

One major benefit from magnetic resonance diffusion tensor imaging
(DTI) is the in vivo identification of neural fiber trajectories
within the human brain. For this purpose of reliable fiber
reconstruction manifold data processing steps are necessary that may
lead to uncontrolled error accumulation. Therefore, a space varying
coefficient model (SVCM) using penalized B-splines was developed and
implemented in R (R Development Core Team, 2007) to integrate
diffusion tensor estimation, regularization and interpolation into a
unified framework. The implementation challenges originating in
multiple 3d space varying coefficient surfaces and the large
dimensions of realistic datasets were met by incorporating matrix
sparsity and efficient model approximation. Superiority of B-spline
based SVCM to the standard approach was demonstrable from simulation
studies in terms of the precision and accuracy of the individual
tensor elements. The integration with a probabilistic fiber
tractography algorithm and application on real brain data revealed
that the unified approach is at least equivalent to the serial
application of voxelwise estimation, smoothing and interpolation.   
From the error analysis using boxplots and visual inspection the
conclusion was drawn that both the standard approach and the B-spline
based SVCM may suffer from low local adaptivity. Improved results are
expected from replacing the B-spline basis functions with wavelet
basis functions. This is on-going research.