RGBD-Based Head Models Enable Precise MRI-Free Robotic Transcranial Magnetic Stimulation

Robotic transcranial magnetic stimulation (TMS) offers precise, operator-independent motor mapping, but its widespread adoption is hindered by the requirement for structural MRI for neuronavigation. This study assessed whether individual head models constructed from a depth (RGBD) camera could serve as a valid, MRI-free alternative for robotic TMS. In a within-subject design, healthy participants underwent robotic motor mapping using both MRI-based and RGBD-based head models. We evaluated geometric registration accuracy and the test-retest reliability of functional outcomes (resting motor threshold [RMT], map area, and motor hotspot coordinates). RGBD-based head registration yielded a mean error of $2.53~\pm ~0.86$ mm. Although slightly higher than that of MRI-based registration ( $2.02~\pm ~0.28$ mm), this accuracy remained comparable to the typical error range of conventional neuronavigation systems (2-4 mm). Functionally, RGBD-guided mapping demonstrated reliability comparable to that of the MRI-guided condition. Crucially, the differences between the two models, such as the <1% maximum stimulator output difference in RMT and <1 mm shift in hotspot location, fell well within the known limits of physiological variability and technical precision. RGBD-based head models provide sufficient anatomical and functional fidelity to replace MRI for robotic TMS. By eliminating the logistical, financial, and safety constraints of MRI, this RGBD workflow significantly lowers the barrier to entry for precise, automated brain mapping, facilitating its application in clinical and research settings where MRI is unavailable.