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See detailSusceptibility-Weighted MRI for Deep Brain Stimulation: Potentials in Trajectory Planning
Hertel, Frank UL; Husch, Andreas UL; Dooms, Georges et al

in Stereotactic and Functional Neurosurgery (2015), 93(5), 303-308

Background: Deep brain stimulation (DBS) trajectory plan- ning is mostly based on standard 3-D T1-weighted gado- linium-enhanced MRI sequences (T1-Gd). Susceptibility- weighted MRI sequences (SWI) show ... [more ▼]

Background: Deep brain stimulation (DBS) trajectory plan- ning is mostly based on standard 3-D T1-weighted gado- linium-enhanced MRI sequences (T1-Gd). Susceptibility- weighted MRI sequences (SWI) show neurovascular struc- tures without the use of contrast agents. The aim of this study was to investigate whether SWI might be useful in DBS trajectory planning. Methods: We performed bilateral DBS planning using conventional T1-Gd images of 10 patients with different kinds of movement disorders. Afterwards, we matched SWI sequences and compared the visibility of vas- cular structures in both imaging modalities. Results: By ana- lyzing 100 possible trajectories, we found a potential vascu- lar conflict in 13 trajectories based on T1-Gd in contrast to 53 in SWI. Remarkably, all vessels visible in T1-Gd were also de- picted in SWI, whereas SWI showed many additional vascular structures which could not be identified in T1-Gd. Conclu- sion/Discussion: The sensitivity for detecting neurovascular structures for DBS planning seems to be significantly higher in SWI. As SWI does not require a contrast agent, we suggest that SWI may be a valuable alternative to T1-Gd MRI for DBS trajectory planning. Furthermore, the data analysis suggests that vascular interactions of DBS trajectories might be more frequent than expected from the very low incidence of symptomatic bleedings. The explanation for this is currently the subject of debate and merits further studies. [less ▲]

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See detailA solution for Multi-Alignment by Transformation Synchronisation
Bernard, Florian UL; Thunberg, Johan UL; Gemmar, Peter et al

in The proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)

The alignment of a set of objects by means of transformations plays an important role in computer vision. Whilst the case for only two objects can be solved globally, when multiple objects are considered ... [more ▼]

The alignment of a set of objects by means of transformations plays an important role in computer vision. Whilst the case for only two objects can be solved globally, when multiple objects are considered usually iterative methods are used. In practice the iterative methods perform well if the relative transformations between any pair of objects are free of noise. However, if only noisy relative transformations are available (e.g. due to missing data or wrong correspondences) the iterative methods may fail. Based on the observation that the underlying noise-free transformations lie in the null space of a matrix that can directly be obtained from pairwise alignments, this paper presents a novel method for the synchronisation of pairwise transformations such that they are globally consistent. Simulations demonstrate that for a high amount of noise, a large proportion of missing data and even for wrong correspondence assignments the method delivers encouraging results. [less ▲]

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