Properties of the target registration error for surface matching in neuronavigation

Surface matching is a relatively new method of spatial registration in neuronavigation. Compared to the traditional point matching method, surface matching does not use fiducial markers that must be fixed to the surface of the head before image scanning, and therefore does not require an image acquisition specifically dedicated for navigation purposes. However, surface matching is not widely used clinically, mainly because there is still insufficient knowledge about its application accuracy. This study aimed to explore the properties of the Target Registration Error (TRE) of surface matching in neuronavigation.

The surface matching process was simulated in the image space of a neuronavigation system so that the TRE could be calculated at any point in that space. For each registration, two point clouds were generated to represent the surface extracted from preoperative images (PC(image)) and the surface obtained intraoperatively by laser scanning (PC(laser)). The properties of the TRE were studied by performing multiple registrations with PC(laser) point clouds at different positions and generated by adding different types of error.

For each registration, the TRE had a minimal value at a point in the image space, and the iso-valued surface of the TRE was approximately ellipsoid with smaller TRE on the inner surfaces. The position of the point with minimal TRE and the shape of the iso-valued surface were highly random across different registrations, and the surface registration error between the two point clouds was irrelevant to the TRE at a specific point. The overall TRE tended to increase with the increase in errors in PC(laser), and a larger PC(laser) made it less sensitive to these errors. With the introduction of errors in PC(laser), the points with minimal TRE tended to be concentrated in the anterior and inferior part of the head.

The results indicate that the alignment between the two surfaces could not provide reliable information about the registration accuracy at an arbitrary target point. However, according to the spatial distribution of the target registration error of a single registration, enough application accuracy could be guaranteed by proper visual verification after registration. In addition, surface matching tends to achieve high accuracy in the inferior and anterior part of the head, and a relatively large scanning area is preferable.