A Novel Registration Method for a Mixed Reality Navigation System Based on a Laser Crosshair Simulator: A Technical Note

Abstract

Mixed Reality Navigation (MRN) is pivotal in augmented reality-assisted intelligent neurosurgical interventions. However, existing MRN registration methods face challenges in concurrently achieving low user dependency, high accuracy, and clinical applicability. This study proposes and evaluates a novel registration method based on a laser crosshair simulator, evaluating its feasibility and accuracy. A novel registration method employing a laser crosshair simulator was introduced, designed to replicate the scanner frame's position on the patient. The system autonomously calculates the transformation, mapping coordinates from the tracking space to the reference image space. A mathematical model and workflow for registration were designed, and a Universal Windows Platform (UWP) application was developed on HoloLens-2. Finally, a head phantom was used to measure the system's target registration error (TRE). The proposed method was successfully implemented, obviating the need for user interactions with virtual objects during the registration process. Regarding accuracy, the average deviation was 3.7 ± 1.7 mm. This method shows encouraging results in efficiency and intuitiveness and marks a valuable advancement in low-cost, easy-to-use MRN systems. The potential for enhancing accuracy and adaptability in intervention procedures positions this approach as promising for improving surgical outcomes.

Keywords: accuracy; augmented reality; crosshair simulator; head phantom; laser; mixed reality navigation; neurosurgical interventions; preoperative planning; registration method; target registration error.

Figure 1

Mixed reality neuro-navigation (MRN) registration using scanner tracking (A) is challenging due to tracking signal interruption, as the user must stay away from the scanner during data acquisition. MRN registration using the proposed concept of a crosshair simulator (B) transferring the laser crosshair (red) location from the scanner to the simulator (cyan) enabling the projection of corresponding imaging data onto the patient’s head (HMD = head mounted display).

Figure 2

Structural and functional demonstration of the crosshair simulator. The crosshair simulator (A) exemplifies its ability to simulate scanner-generated laser crosshairs, forming two crosshair laser projections on a patient’s head, identical to those observed in CT or MRI scanners (B). Figure 2B provided by ©Alamy. The image has been granted copyright authorization from the Alamy platform.

Figure 3

The three coordinate systems in the crosshair simulator (SCS = simulator coordinate system (red); RICS = reference image coordinate system (brown); VCS = virtual coordinate system (green); The transformations (colored arrows) from one to the other coordinate system are color-coded as gradients of the related coordinate systems.) and the mathematical expression utilizing both physical and virtual calibration spheres to calibrate the intrinsic and extrinsic matrices.

Figure 4

The calibration procedure of intrinsic and extrinsic matrices using a custom calibration sphere in the crosshair simulator. The structure of the calibration sphere is depicted in schematic form (A) and was used for calibrating the intrinsic of the crosshair simulator (B). During intrinsic calibration, eight known orthogonal calibration arc combinations on the sphere can be used (C). The process of extrinsic calibration is shown, with the virtual calibration sphere representing RICS not aligning with the physical calibration sphere (D). After adjustment, the virtual calibration sphere perfectly aligns with the physical calibration sphere, signifying successful calibration (E).

Figure 5

The mathematical model for crosshair simulator-based registration with coordinates systems (i.e., the world (blue),scanner (purple), RICS (brown), SCS (red), VCS (green), and HoloLens-2 (light blue) coordinate systems) and the related transformations between those (gradient color-coded).

Figure 6

Practical workflow chart for the proposed MRN system compared with conventional neuronavigation system. (OR = operating room).

Figure 7

An illustrative case demonstrates the crosshair simulator-based registration process and its accuracy measurement method. CT imaging data, including visible fiducial markers attached to the scalp of a patient presenting with a right basal ganglia hematoma (A). The CT scanner’s laser crosshair projection lines on orthogonal reference planes were recreated using 3D Slicer (B). A 1:1 scale 3D printed model was generated with laser projection lines marked in red (C). Using 3D Slicer, a set of holograms for validation, including the hematoma in red, a puncture path in green, fiducial markers in yellow, and the two quadrants of the scalp divided by the reference plane in cyan, was created (D). Manual adjustment of the crosshair simulator showing a perfect match of the laser crosshairs and the marked laser positioning lines on the head model (E). Successful hologram registration perfectly aligned the holographic image with the 3D-printed head model (F). Coordinates of six fiducial points in the RICS, as shown in the green box, were selected for accuracy measurement using 3D Slicer software (G). Following MRN registration, the user positioned the virtual probe, consisting of a white line handle and a white spherical tip, on the perceived real-world fiducial points, as shown in the red box (H).

Figure 8

Results of accuracy measurement. A color gradient scatter plot demonstrates the deviation across all measured points (A). Overlapping polygons colored in red, cyan, and blue depict deviations for three different registrations on the original model (Legend: R = registration, M = measurement, and the numbers denote the respective sessions, e.g., ‘R1M1′ corresponds to the first registration followed by the first measurement) (B). The measured points extrapolated the full-head error distribution for nine sessions from R1M1 to R3M3 (C). The histograms present the distribution of deviations and their components along the X, Y, and Z axis (D). Box plots compare inter-group deviations grouped by fiducial points (Makers A, B, C, D, E, F) and deviations in the X, Y, and Z components (E). The whiskers represent the minimum and maximum values within 1.5 times the interquartile range (IQR) from the first (Q1) and third (Q3) quartiles. Any data points beyond this range, which are considered outliers, are marked with red crosses (+).

Figure 9

Comparison of laser crosshair projection on areas with large curvature radius (A) and small curvature radius (B). The projection simulation was conducted in MATLAB R2022a, and to simplify calculations, two parabolic surfaces with different apertures and curvatures were plotted. Assuming a slight angular disparity between the simulator laser (depicted as the red line) and the scanner laser (depicted as the green line) during the deployment of the crosshair simulator, rather than being perfectly coaxial, the projection of the simulator’s crosshair on the patient’s skin (depicted as the red curve) will distort at the reference lines (depicted as the green curve), resulting in an imperfect match. This mismatch is more pronounced in areas with a smaller curvature radius, aiding users in promptly detecting registration errors and adjusting the simulator’s deployment. Hence, these regions are more critical for the value of registration.