Marker-to-Object Calibration Using Landmark Touch.

In image-guided interventions, fiducial markers are widely used for medical instrument tracking by attaching them to designated positions. However, due to the difficulty of precise marker placement, obtaining an accurate marker-to-object transformation remains technically challenging, particularly with customized markers or those with non-standard geometries. To accurately identify the transformation, this study introduces a novel calibration method achieved by sequentially touching a fixed tip with landmarks on the object. An inverse sample consensus filter was proposed to remove potential measurement outliers and improve the robustness of the calibration result. Validation through simulations and experiments under two tracking modalities demonstrated superior translational accuracy and improved robustness compared to conventional methods. Specifically, the experiment conducted under electromagnetic tracking system demonstrated a translational error of 0.61 ± 0.11 mm and a rotational error of 0.97 ± 0.18°. The experiment using magnetic resonance imaging system demonstrated a translational error of 0.60 mm and a rotational error of 2.81°. A use case with an intracerebral hemorrhage evacuation robot further verified the feasibility of integrating the calibration method into the image-guided workflow. The proposed method achieved sub-millimeter calibration accuracy across different scenarios, demonstrating its effectiveness and strong potential for diverse research and clinical applications.