Toward Accessible Neuronavigation: Tracking Retroreflective Markers with a Consumer-Grade Depth Camera
Computer-aided neuronavigation systems are a key component of several modern medical procedures, including transcranial magnetic stimulation (TMS). Existing offerings require expensive, often proprietary hardware and software, limiting the widespread adoption of neuronavigation. We propose a novel setup which employs a single consumer-grade depth camera paired with a custom algorithm to track retroreflective infrared (IR) markers. We validated the proposed framework by comparing it to the NDI Polaris Vicra camera, a common component in many commercially available neuronavigation systems. Our empirical results indicate that the proposed tracking method operated with < 1% displacement error, suggesting that consumer-grade cameras are a feasible alternative to the expensive, industry-standard IR cameras currently used for neuronavigation. The code is available at github.com/rajkundu/ir-tracking-urucon-2024.
