Augmented reality (AR) offers a new medical treatment approach. We aimed to evaluate frameless (mask) fixation navigation using a 3D-printed patient model with fixed-AR technology for gamma knife radiosurgery (GKRS). Fixed-AR navigation was developed using the inside-out method with visual inertial odometry algorithms, and the flexible Quick Response marker was created for object-feature recognition. Virtual 3D-patient models for AR-rendering were created via 3D-scanning utilizing TrueDepth and cone-beam computed tomography (CBCT) to generate a new GammaKnife Icon™ model. A 3D-printed patient model included fiducial markers, and virtual 3D-patient models were used to validate registration accuracy. Registration accuracy between initial frameless fixation and re-fixation navigated fixed-AR was validated through visualization and quantitative method. The quantitative method was validated through set-up errors, fiducial marker coordinates, and high-definition motion management (HDMM) values. A 3D-printed model and virtual models were correctly overlapped under frameless fixation. Virtual models from both 3D-scanning and CBCT were enough to tolerate the navigated frameless re-fixation. Although the CBCT virtual model consistently delivered more accurate results, 3D-scanning was sufficient. Frameless re-fixation accuracy navigated in virtual models had mean set-up errors within 1 mm and 1.5° in all axes. Mean fiducial marker differences from coordinates in virtual models were within 2.5 mm in all axes, and mean 3D errors were within 3 mm. Mean HDMM difference values in virtual models were within 1.5 mm of initial HDMM values. The variability from navigation fixed-AR is enough to consider repositioning frameless fixation without CBCT scanning for treating patients fractionated with large multiple metastases lesions (> 3 cm) who have difficulty enduring long beam-on time. This system could be applied to novel GKRS navigation for frameless fixation with reduced preparation time.
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