Biomedical Engineering Reference
A first estimate of the accuracy of the head tracking has revealed that an
tracking error smaller than 5 mm is achievable. As a registration among two laser
scans has been the basis for this evaluation, head tracking with an MRI image as
reference might be more accurate. However, these results support the feasibility of
3D laser scanning systems for direct head tracking.
Furthermore, we have presented that a 3D laser scanning system can be cali-
brated to a robot using a common hand-eye calibration method. Given the accu-
racy of the used laser scanning device, the calibration results of approximately
1.3 mm are satisfactory. However, the next generation of laser scanning systems
will severely improve in resolution and accuracy. Thus, the calibration accuracy
will also increase. Furthermore, the scanning time will decrease with the new
systems resulting in high resolution real-time capable laser scanning systems.
In conclusion, we have shown that 3D laser scanning systems can in principle
be used for direct head tracking. At present, scanning time and resolution are
limiting factors which will be improved with advanced systems.
Furthermore, we have shown that 3D laser scans of the head can be used as a
navigation source for TMS when no medical image data is on hand. Instead of a
manual head contour generation, where the data is collected with a pointer, it is
more precise and appropriate to the head due to the fact that the laser scan consists
of a magnitude more points compared to a manual head contour.
Our ongoing clinical trials have practically proven that 3D laser scans are
sufficient for application in navigated and robotized TMS systems. For a later
evaluation, registration of laser scans with acquired measurements to medical
images is possible.
When data acquisition of 3D laser scans becomes real-time capable with new
technologies, it will be possible to use laser scans as navigation source and for
direct head tracking during stimulation. This would speed up the whole process
and increase the acceptance of the system in clinical workflow as subjects could be
stimulated without any data preparation or registration.
1. Advanced Neuro Technology B.V.: The lates addition to SmartMove: TouchSense (2012).
http://ant-neuro.com/showcases_and_projects/touchsense/ ; visited on 23 May 2012
2. Albu-Schäffer, A., Haddadin, S., Ott, C., Stemmer, A., Wimböck, T., Hirzinger, G.: The DLR
lightweight robot—design and control concepts for robots in human environments. Ind.
Robot 34(5), 376-385 (2007)
3. Amaya, F., Paulus, W., Treue, S., Liebetanz, D.: Transcranial magnetic stimulation and PAS-
induced cortical neuroplasticity in the awake rhesus monkey. Clin. Neurophysiol. 121(12),
2143-2151 (2010). doi: 10.1016/j.clinph.2010.03.058
4. Bodensteiner, C., Darolti, C., Schweikard, A.: Achieving super-resolution x-ray imaging with
mobile c-arm devices. Int. J. Med. Robot. Comput. Assist. Surg. 5(3), 243-256 (2009).
5. Ehlers, K.: Anwendung der faceapi zur bewegungskompensation für die robotergestützte
transkranielle magnetstimulation. BSc thesis, University of Lübeck (2009)