A Comparative Statistical Error Analysis of Neuronavigation
Systems in a Clinical Setting
Abbasi H. MD PhD, Hariri S. CandMed, Martin D. MD, Kim D. MD,
Shahidi R. PhD
Department of Neurosurgery – Stanford University, 300 Pasteur Dr,
Stanford CA, 94305 USA
Abstract
The use of neuronavigation (NN) in neurosurgery has become ubiquitous. A growing
number of neurosurgeons are utilizing NN for a wide variety of purposes, including
optimizing the surgical approach (macrosurgery) and locating small areas of interest
(microsurgery). The goal of our team is to apply rapid advances in hardware and software
technology to the field of NN, challenging and ultimately updating current NN
assumptions. To identify possible areas in which new technology may improve the
surgical applications of NN, we have assessed the accuracy of neuronavigational
measurements in the Radionics™ and BrainLab™ systems. Using a phantom skull, we
measured the accuracy of the navigational systems, taking a total of 2616 measurements.
We found that, despite current NN tenets, the six marker count does not yield optimal
accuracy in either system, and the spreaded marker setting yields best accuracy in both
systems. Placing less markers around the region of interest (ROI) minimizes registration
error, and active tracking does not necessarily increase accuracy. Comparing the two
systems, we also found that the accuracy of NN machines differs both overall and in
different axes.
Introduction
▪
Experimental and methodological assumptions that have been retained in the use
of NN over its development:
1.
Using more markers increases accuracy.
2.
Putting more markers around the area of interest increases accuracy.
3.
Using 6 markers yields the most efficient accuracy.
4.
Using active tracking [e.g. an active DRF (dynamic reference frame) and
probe which emit infrared beams rather than passively reflecting them]
increases accuracy.
5.
Minimizing the distance from the marker or marker group to the area of
interest increases accuracy.
▪
While rapid advances in hardware and software have emerged in the last few
years, there have been only few attempts at challenging the old NN tenets and applying
new technology to update these systems.
▪
To identify possible areas in which new technology may improve the surgical
applications of NN, we conducted accuracy tests of NN measurements in two currently
used systems: Radionics™ and BrainLab™.
▪
Questions we sought to address:
1.
How many markers efficiently maximize accuracy?
2.
What pattern of marker localization maximizes accuracy?
3.
4.
5.
Are there significant accuracy differences between various marker
arrangements in different systems?
Are systems significantly different in their level of accuracy?
Is accuracy different in the x-, y- and z-axes?
Materials and Methods
▪
We obtained a standard plastic skull, removed the calvaria, and installed three
Plexiglas square rods of different heights in each of the three anatomical fossae (anterior,
middle and posterior). We used the edges of these rods as our targets. We installed a
Plexiglas ball of known diameter on the phantom's sella turcica. (Fig. 1 and 2)
▪
Replacing the calvaria, we placed a total of 12 markers bilaterally on the exterior
of the skull in the following regions: 6 frontal, 2 mastoid, 2 occipital and 2 high parietal.
(Fig. 3)
Fig. 3: Placement of the markers
Marker count
Local Setting
4
F1, F3, F4, F6
6
F1, F2, F3, F4, F5, F6,
8 (Radionics only)
F1, F2, F3, F4, F5, F6,
O1, O6
F1: Frontolateral right below calvaria line
F3: Frontolateral left below calvaria line
F5: Frontomedian above calvaria line
O1: Above right mastoid process
O3: High parietal right
O5: Left occipital
Spreaded Setting
F1, F3, O1, O6
F1, F3, O1,O3, O4, O6
F1, F3,
O1, O2, O3, O4, O5, O6
F2: Frontomedian below calvaria line
F4: Frontolateral right above calvaria line
F6: Frontolateral left above calvaria line
O2: Right occipital
O4: High parietal left
O6: Above left mastoid process
Fig. 4: Relation of the measurements on the monitor to the actual coordinates of the skull
as illustrated in Pic. 2
Screen Coordinate
Skull Coordinate
Screen Coordinate
Skull Coordinate
Axial X
+X
Axial Y
+Y
Sagital X
-Y
Sagital Y
+Z
Coronal X
+X
Coronal Y
+Z
▪
We performed a CT of the skull in 1.25 mm slices and sent the data over the
network to the two NN machines evaluated in this study. The systems utilize different
registration and tracking systems to localize the probe's tip in 3D:
▪
BrainLab VectorVision version 2.3: Passive registration & tracking: the
DRF and probe reflect the IR signal shot out by emitters around the cameras. Local and
spreaded marker settings with 4, 6, and 8 markers.
▪
Radionics OTS version 2.2: Active registration & tracking: the DRF and
probe contain diodes emitting IR signals that are collected by 2 cameras. Local and
spreaded marker settings with 4, 6, and 7 markers.
▪
When the probe tip was placed at the edge of a rod, the NN systems visualized the
probe's position on their screens in the original axial plane of the CT scans and in the
sagital and coronal planes reconstructed from the CT scans (Fig. 2).
In each of the three cross-sections, we measured how far from the actual edge of the rod
(x=0, y=0) the system was representing the probe tip (Fig. 4). 12 series of measurements
taken, each series consisting of 218 separate measurements.
Results
▪
Marker Count (Figs. 5 & 7)
BrainLab: In the local marker setting, 4 or 7, but not 6 markers, maximize
accuracy. In the spreaded marker setting, accuracy declines with additional markers.
Radionics: In both the spreaded and local marker setting, as expected, error
declined as the experimenter went from 4 to 6 to 8 markers. The greatest improvement in
accuracy is obtained when increasing from 6 to 8 markers. However, this improvement
in accuracy given more markers was not as great as expected.
▪
Proximity of Markers to Region of Interest (ROI) (Fig. 5)
BrainLab: In the spreaded and local marker setting placing more markers around
the ROI only somewhat maximizes accuracy.
Radionics: In the local marker setting, placing more markers around the ROI
maximizes accuracy. However, in the spreaded marker setting, placing fewer markers
around the ROI maximizes accuracy (i.e. error was maximal in the frontal fossa and
minimal in the occipital fossa).
▪
Active versus Passive Tracking. (Fig. 6)
Active tracking does not necessarily increase accuracy, as shown by BrainLab's
greater accuracy than the Radionics system.
▪
Spreaded versus Local Marker Setting. (Fig. 7)
Except for the 7 and 8 marker counts, the spreaded marker setting yielded greater
accuracy than the local marker setting. However, in our experimental setup, the 7 and 8
marker spreaded and local settings were extremely similar.
▪
Radionics versus BrainLab (Fig. 5 and 6)
Accuracy of the NN machines differs both overall and in different axes. The
overall accuracy of the BrainLab system is greater than the Radionics system.
Discussion
Overall Error = Registration Error + System Error
Registration error = the error generated during the process of telling the NN system
where each marker is located.
System error = the mechanical, engineering, and software errors, such as machine or
camera error.
▪
Having more markers can decrease system accuracy. However, at some point, the
marginal increase in error associated with registering an additional marker surpasses the
marginal decrease in system error due to more markers (Fig. 9).
▪
The registration error found in surgeries is probably greater than the registration
error found in our phantom model because skin markers used for patients are applied to
the skin while the skin markers used for the phantom were applied directly to the skull
(Fig. 8).
Conclusion
This study scrutinized conventional assumptions of neuronavigation and found many of
them to be invalid. The new NN tenets supported by our findings are:
▪
4 or 8, but not 6, markers yields most efficient accuracy. We are aware of the
counterintuitive nature of this finding, and our lab is currently investigating this
result. Also, the movement of skin on the skull is not included in this study and
may influence the clinical results.
▪
Placing fewer markers around the region of interest (ROI) decreases registration
error at the ROI.
▪
Active tracking does not necessarily increase accuracy.
▪
The spreaded marker setting increases accuracy.
▪
Accuracy of the NN machines differs both overall and in different axes.
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