We present a new preoperative planning method to quantify and help reduce the risk associated with needle and tool
insertion trajectories in image-guided keyhole neurosurgery. The goal is to quantify the risk of a proposed straight
trajectory, and/or to find the trajectory with the lowest risk to nearby brain structures based on pre-operative CT/MRI
images. The method automatically computes the risk associated with a given trajectory, or finds the trajectory with the
lowest risk to nearby brain structures based on preoperative image segmentation and on a risk volume map. The
surgeon can revise the suggested trajectory, add a new one using interactive 3D visualization, and obtain a quantitative
risk measure. The trajectory risk is evaluated based on the tool placement uncertainty, on the proximity of critical brain
structures, and on a predefined table of quantitative geometric risk measures. Our preliminary results on a clinical
dataset with eight targets show a significant reduction in trajectory risk and a shortening of the preoperative planning
time as compared to the conventional method.
Fiducial-based rigid registration is the preferred method for aligning the preoperative image with the intra-operative
physical anatomy in existing image-guided surgery systems. After registration, the targets locations usually cannot be measured directly, so the Target Registration Error (TRE) is often estimated with the Fiducial Registration Error (FRE), or with Fitzpatrick TRE (FTRE) estimation formula. However, large discrepancies between the FRE and the TRE have been exemplified in hypothetical setups and have been observed in the clinic. In this paper, we formally prove that in the worst case the FRE and the TRE, and the FTRE and the TRE are independent, regardless of the target location, it location, the number of fiducials, and their configuration. The worst case occurs when the unknown Fiducial Localization Error (FLE) is modeled as an affine anisotropic inhomogeneous bias. Our results generalize previous examples, contribute to the mathematical understanding of TRE estimation in fiducial-based rigid-body registration, and strengthen the need for realistic and reliable FLE models and effective TRE estimation methods.
We describe a new framework and method for the optimal selection of anatomical landmarks and optimal placement of
fiducial markers in image-guided neurosurgery. The method allows the surgeon to optimally plan the markers locations
on routine diagnostic images before preoperative imaging and to intraoperatively select the fiducial markers and the
anatomical landmarks that minimize the Target Registration Error (TRE). The optimal fiducial marker configuration
selection is performed by the surgeon on the diagnostic image following the target selection based on a visual Estimated
TRE (E-TRE) map. The E-TRE map is automatically updated when the surgeon interactively adds and deletes candidate
markers and targets. The method takes the guesswork out of the registration process, provides a reliable localization
uncertainty error for navigation, and can reduce the localization error without additional imaging and hardware. Our
clinical experiments on five patients who underwent brain surgery with a navigation system show that optimizing one
marker location and the anatomical landmarks configuration reduces the average TRE from 4.7mm to 3.2mm, with a
maximum improvement of 4mm. The reduction of the target registration error has the potential to support safer and more
accurate minimally invasive neurosurgical procedures.