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

摘要

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 fewer 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 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 field tenets will be revised, making NN an even more useful neurosurgical tool.