Biomedical Engineering Reference
In-Depth Information
real-time calibration method, which allows to check the calibration during oper-
ation for increased safety, is performed.
Chapter 5 introduces Force-Torque (FT)-control to the robotized TMS system
to optimize precision and clinical applicability. The general issue of gravity
compensation and tool calibration is introduced, and two aspects of FT-control are
described: (1) Contact pressure control allows to position the TMS coil on the
head with an optimal pressure and maintains this optimal contact pressure during
operation. (2) Hand-assisted positioning enables the operator to position the TMS
coil in an intuitive fashion with the robotized TMS system.
Even though FT-control greatly enhances usability and precision, general
system safety cannot be ensured. The FT-control is implemented in the TMS
control software and latencies and dependencies are unavoidable. To guarantee
safety, a novel sensor is developed, called FTA sensor, and described in Chap. 6 . It
combines a standard force/torque sensor with an accelerometer for independence
from robot input. An Embedded System (ES) provides the necessary computations
in real-time and triggers the robot's Emergency stop (e-stop) instantaneously in
case of an error or collision. In this way, system safety can be achieved.
As the FTA sensor operates in real-time, the presented FT-control is optimized.
Chapter 7 presents the integration of the FTA sensor into the robotized TMS
system and its application. It shows an advanced hand-assisted positioning method
that is implemented directly on the robot controller. Furthermore, an optimized
tool calibration method and the integration into the robot server are presented.
As a concluding improvement, Chap. 8 introduces the potential of direct
tracking for neuro-navigated and robotized TMS. Two methods for direct head
tracking are presented and first results are shown.
Chapter 9 discusses the presented developments in the context of safety and
clinical applicability of robotized TMS. In conclusion, future prospects of robot-
ized TMS are briefly presented and discussed in Chap. 10 .
References
1. Adrian, E.D., Moruzzi, G.: Impulses in the pyramidal tract. J. Physiol. 97(2), 153-199 (1939)
2. Awiszus, F.: TMS and threshold hunting. Suppl. Clin. Neurophysiol. 56, 13-23 (2003)
3. Awiszus, F.: Fast estimation of transcranial magnetic stimulation motor threshold: is it safe?
Brain Stimulation 4(1), 58-59 (2011). doi: 10.1016/j.brs.2010.09.004
4. Awiszus,
F.,
Borckardt,
J.J.:
TMS
Motor
Threshold
Assessment
Tool.
http://
clinicalresearcher.org/software.htm (2011). Version 2.0
5. Barker, A.T., Jalinous, R., Freeston, I.L.: Non-invasive magnetic stimulation of human motor
cortex. The Lancet 325(8437), 1106-1107 (1985). doi: 10.1016/S0140-6736(85)92413-4
6. Besl, P.J., McKay, H.D.: A method for registration of 3-d shapes. IEEE Trans. Pattern Anal.
Mach. Intell. 14(2), 239-256 (1992). doi: 10.1109/34.121791
7. Bohning, D.E.: Introduction and overview of tms physics. In: George, M.S., Belmaker, R.H.
(eds.)
Transcranial
Magnetic
Stimulation
in
Neuropsychiatry,
pp.
13-44.
American
Psychiatric Press, Washington, DC (2000)
 
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