| Summary: | Acoustic neuroma surgery is a procedure in which a benign mass is removed from the Internal Auditory Canal (IAC). Currently this surgical procedure requires the manual drilling of the temporal bone followed by exposure and removal of the acoustic neuroma. This procedure is physically and mentally taxing to the surgeon. Our group is working to develop an Acoustic Neuroma Surgery Robot (ANSR) to perform the initial drilling procedure. Planning the ANSRs drilling region using pre-operative CT requires expertise and around 35 minutes time. We propose an approach for automatically producing a resection plan for the ANSR that would avoid damage to sensitive ear structures and requires minimal editing by the surgeon. We first compute an atlas-based segmentation of the mastoid section of the temporal bone, refine it based on the position of anatomical landmarks, and apply a safety margin to the result to produce the automatic resection plan. In experiments with CTs from 9 subjects, our automated process resulted in a resection plan that was verified to be safe in every case. Approximately 2 minutes time was required in each case for the surgeon to edit the plan to permit functional access to the IAC. We measured a mean Dice coefficient of 0.99 and surface error of 0.08 mm between the final and automatically proposed plans. These preliminary results indicate that our approach is a viable method for resection planning for the ANSR and drastically reduces the surgeons planning effort.
The procedures used to place pre-curved cochlear implant electrode arrays could be optimized to improve electrode placement. Positioning of a pre-curved electrode array in the scala tympani (ST) of the cochlea is highly dependent on the depth at which the array is advanced off the stylet and the overall depth of insertion. However, the optimal stylet depth is variable with individual ST geometry, and actual overall depth of electrode insertion highly variable across individuals. We developed software to simulate the insertion trajectories of two different pre-curved arrays in the ST of 9 individuals. ST geometry was computed using validated software designed to accurately identify the ST in CT. A novel electrode shape model was developed to simulate the insertion trajectory of the arrays. Using this model, we quantify variance in stylet depth due to ST geometry and evaluate optimal overall insertion depths using a novel insertion trajectory quality metric. We found range in angular stylet depth of 50 degrees. Our data suggest modiolar hugging array positioning is best achieved by inserting arrays until the depth marker is approximately 2 mm outside the entry site. Our simulations suggest variable outcomes due to variable ST geometry. They also suggest that the optimal depth of insertion of pre-curved arrays may be different than the depth typically chosen. Future investigations are needed to verify that optimized insertion depths will result in better modiolar placement of pre-curved arrays.
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