Owing to the frequent need for compromising between acquisition time and image quality, the three-dimensional vector field that is obtained using DTI data may contain a high level of noise. Several researchers have tried to quantify the effects of noise on tensor and tract reconstruction [30-35]. Unlike what takes place in standard MR, the noise present in the reconstructed tensor is not directly perceived on images like those of Fig. 8.2 and Fig. 8.4, because it affects only the direction of the tracts. Consequently, though exhibiting a consistent distribution of directions, the vectors may indicate slightly different trajectories with respect to the actual anatomy. This type of noise considerably affects fibre tracking, and one of the main problems of line propagation algorithms is that such errors accumulate with

Fig. 8.6. The crossing of two fibre bundles may result in a spherical diffusion tensor, erroneously indicating an absence of fibres increasing distance from the seed point [28]. Therefore, the greater this distance, the higher the risk of deviation of the reconstructed fibre towards an adjacent, unconnected fibre tract. This possibility should always be taken into account when analysing DTI reconstructions, especially of long fibre tracts. SNR optimization is essential to obviate this and other, conceptually related problems. Here, too, the intensity of the magnetic field used and the availability of parallel imaging play a large role.

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