We propose a minimum cost path approach to track the centerlines of the internal and external carotid arteries in
multispectral MR data. User interaction is limited to the annotation of three seed points. The cost image is based
on both a measure of vessel medialness and lumen intensity similarity in two MRA image sequences: Black Blood
MRA and Phase Contrast MRA. After intensity inhomogeneity correction and noise reduction, the two images are
aligned using affine registration. The two parameters that control the contrast of the cost image were determined
in an optimization experiment on 40 training datasets. Experiments on the training datasets also showed that a cost
image composed of a combination of gradient-based medialness and lumen intensity similarity increases the tracking
accuracy compared to using only one of the constituents. Furthermore, centerline tracking using both MRA sequences
outperformed tracking using only one of these MRA images. An independent test set of 152 images from 38 patients
served to validate the technique. The centerlines of 148 images were successfully extracted using the parameters
optimized on the training sets. The average mean distance to the reference standard, manually annotated centerlines,
was 0.98 mm, which is comparable to the in-plane resolution. This indicates that the proposed method has a high
potential to replace the manual centerline annotation.
In this paper a method to remove the divergence from a vector field is presented. When applied to a displacement field, this will remove all local compression and expansion. The method can be used as a post-processing step for (unconstrained) registered images, when volume changes in the deformation field are undesired. The method involves solving Poisson's equation for a large system. Algorithms to solve such systems include Fourier analysis and Cyclic Reduction. These solvers are vastly applied in the field of fluid dynamics, to compensate for numerical errors in calculated velocity fields. The application to medical image registration as described in this paper, has to our knowledge not been done before. To show the effect of the method, it is applied to the registration of both synthetic data and dynamic MR series of the liver. The results show that the divergence in the displacement field can be reduced by a factor of 10-1000 and that the accuracy of the registration increases.