Mixed-Reality Tool for Craniotomy Procedures: Preliminary Evaluation of a Hologram-to-Head Registration Algorithm

Craniotomy is one of the most frequently performed procedures to access the brain to surgically remove tumors. Despite being a standard procedure, craniotomy poses clinical challenges, including a high risk of tissue damage and dependence on surgeon expertise. External navigation tools, e.g., neuro navigators, offer valuable guidance and better spatial understanding to physicians, enhancing surgical outcomes. However, these tools are costly, cumbersome, and not widely accessible. In this study, we introduce a Mixed-reality tool designed to guide craniotomy procedures. We present the initial in vitro evaluation of our algorithm to co-register the holographic head anatomy reconstructed from pre-operative medical imaging and the physical head intraoperatively acquired through a Head-Mounted Display. We progressively stressed the model by introducing various levels of Gaussian noise to the intraoperative acquired data, to simulate the effects of real-world surgical environments. Also, we compared our method to a state-of-the-art one. Experimental results confirmed the accuracy and robustness of our algorithm, which showed better performance vs. the state-of-the-art as long as realistic noise levels were considered.