Canine atlantoaxial optimal safe implantation corridors – description and validation of a novel 3D presurgical planning method using OsiriX™

BACKGROUND Canine ventral atlantoaxial (AA) stabilization is most commonly performed in very small dogs and is technically challenging due to extremely narrow bone corridors. Multiple implantation sites have been suggested but detailed anatomical studies investigating these sites are lacking and therefore current surgical guidelines are based upon approximate anatomical landmarks. In order to study AA optimal safe implantation corridors (OSICs), we developed a method based on computed tomography (CT) and semi-automated three-dimensional (3D) mathematical modelling using OsiriX™ and Microsoft®Excel software. The objectives of this study were 1- to provide a detailed description of the bone corridor analysis method and 2- to assess the reproducibility of the method. CT images of the craniocervical junction were prospectively obtained in 27 dogs and our method of OSIC analysis was applied in all dogs. For each dog, 13 optimal implant sites were simulated via geometrical simplification of the bone corridors. Each implant 3D position was then defined with respect to anatomical axes using 2 projected angles (ProjA). The safety margins around each implant were also estimated with angles (SafA) measured in 4 orthogonal directions. A sample of 12 simulated implants was randomly selected and each mathematically calculated angle was compared to direct measurements obtained within OsiriX™ from 2 observers repeated twice. The landmarks simulating anatomical axes were also positioned 4 times to determine their effect on ProjA reproducibility. RESULTS OsiriX could be used successfully to simulate optimal implant positions in all cases. There was excellent agreement between the calculated and measured values for both ProjA (ρc = 0.9986) and SafA (ρc = 0.9996). Absolute differences between calculated and measured values were respectively [ProjA = 0.44 ± 0.53°; SafA = 0.27 ± 0.25°] and [ProjA = 0.26 ± 0.21°; SafA = 0.18 ± 0.18°] for each observer. The 95 % tolerance interval comparing ProjA obtained with 4 different sets of anatomical axis landmarks was [-1.62°, 1.61°] which was considered appropriate for clinical use. CONCLUSIONS A new method for determination of optimal implant placement is provided. Semi-automated calculation of optimal implant 3D positions could be further developed to facilitate preoperative planning and to generate large descriptive anatomical datasets.