Augmented Reality (AR) has been proven beneficial to External Ventricular Drain (EVD) surgery by providing in-situ visual guidance during operations. During this procedure, the key challenge is estimating the spatial relationship between pre-operative images and actual patient anatomy accurately and efficiently. Previous works have revealed conflicts between tracking accuracy, workflow efficiency, and non-invasiveness in tracking pipelines. This research fully utilizes the capabilities of Time of Flight (ToF) depth sensors, including retro-reflective tool tracking and dense surface information, to construct a convenient and accurate EVD guiding pipeline. As previous studies have proven significant depth errors in ToF depth sensors, we first evaluated the feasibility of using ToF sensors in surgical guidance by estimating its accuracy under different conditions and corrected this error in our pipeline. Our results show $ \text{7.580}\pm \text{1.488}\,\text{mm}$7.580±1.488mm depth value errors on human skin under HoloLens 2 depth camera, indicating the significance of depth correction. This error was reduced by over 85% using proposed depth correction method on head phantoms in different materials. The corrected depth information can then be utilized to reconstruct the head surface with sub-millimeter accuracy, validated on a series of 3D-printed models and a sheep head. To demonstrate the effectiveness of the proposed framework, we conducted a case study simulating EVD surgery. Five surgeons were involved in this study, each performing nine k-wire insertions on a head phantom under virtual guidance without tracking for surgical tools. The results revealed $ \text{2.09} \pm \text{1.00}\,\text{mm}$2.09±1.00mm translational and $\text{2.97}\pm \text{1.95}^\circ$2.97±1.95∘ orientational guidance accuracy, demonstrating competitive performance with previous research.

The use of Augmented Reality (AR) for navigation purposes has shown beneficial in assisting physicians during the performance of surgical procedures. These applications commonly require knowing the pose of surgical tools and patients to provide visual information that surgeons can use during the performance of the task. Existing medical-grade tracking systems use infrared cameras placed inside the Operating Room (OR) to identify retro-reflective markers attached to objects of interest and compute their pose. Some commercially available AR Head-Mounted Displays (HMDs) use similar cameras for self-localization, hand tracking, and estimating the objects' depth. This work presents a framework that uses the built-in cameras of AR HMDs to enable accurate tracking of retro-reflective markers without the need to integrate any additional electronics into the HMD. The proposed framework can simultaneously track multiple tools without having previous knowledge of their geometry and only requires establishing a local network between the headset and a workstation. Our results show that the tracking and detection of the markers can be achieved with an accuracy of$0.09\pm 0.06\ mm$on lateral translation,$0.42 \pm 0.32\ mm$on longitudinal translation and$0.80 \pm 0.39^\circ$for rotations around the vertical axis. Furthermore, to showcase the relevance of the proposed framework, we evaluate the system's performance in the context of surgical procedures. This use case was designed to replicate the scenarios of k-wire insertions in orthopedic procedures. For evaluation, seven surgeons were provided with visual navigation and asked to perform 24 injections using the proposed framework. A second study with ten participants served to investigate the capabilities of the framework in the context of more general scenarios. Results from these studies provided comparable accuracy to those reported in the literature for AR-based navigation procedures.

Radiofrequency ablation (RFA) is now a widely used minimally invasive treatment method for hepatic tumors. Preoperative planning plays a vital role in RFA therapy. With increasing tumor size, multiple overlapping ablations are needed, which are challenging to optimize while considering clinical constraints. In this paper, we present a new automatic RFA planning method. First, a 2-steps set cover-based model is formulated, which can integrate multiple clinical constraints for optimization of overlapping ablations. To ensure that the planning model can be solved in a reasonable time, a search space reducing strategy is then proposed. We also developed an algorithm for automatic RFA electrode selection, which provides a proper electrode ablation zone for the planning model. The proposed method was evaluated with 20 tumors of varying sizes (0.92 cm3to 28.4 cm3). Results showed that the proposed method can generate clinical feasible RFA plans with a minimum number of RFA electrodes and ablations, complete tumor coverage and minimized ablation of normal tissue.