A comparative statistical analysis of neuronavigation systems in a clinical setting.

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 how accurate the visualization of a navigational probe's tip was in these systems, taking a total of 2180 measurements. We found that, despite current NN tenets, error is maximal at the six marker count and minimal in the spreaded marker setting; that is, placing less markers around the area of interest maximizes accuracy and active tracking does not necessarily increase accuracy. Comparing the two systems, we also found that accuracy of NN machines differs both overall and in different axes. As researchers continue to apply technological advances to the NN field, an increasing number of currently held tenets will be revised, making NN an even more useful tool in neurosurgery.