The anatomy of the ear and the bones surrounding it are intricate yet critical for medical professionals to know. Current best practices teach ear anatomy through two-dimensional representations, which poorly characterize the three-dimensional (3D), spatial nature of the anatomy and make it difficult to learn and visualize. In this work, we describe an immersive, stereoscopic visualization tool for the anatomy of the ear based on real patient data. We describe the interface and its construction. And we compare how well medical students learn ear anatomy in the simulation compared with more traditional learning methods. Our preliminary results suggest that virtual reality may be an effective tool for anatomy education in this context.

Surgeons and robots typically use different approaches for bone milling. Surgeons adjust their speed and tool incidence angle constantly, which enables them to efficiently mill porous bone. Surgeons also adjust milling parameters such as speed and depth of cut throughout the procedure based on proximity to sensitive structures like nerves and blood vessels. In this paper we use image-based bone density estimates and segmentations of vital anatomy to make a robot mill more like a surgeon and less like an industrial computer numeric controlled (CNC) milling machine. We produce patient-specific plans optimizing velocity and incidence angles for spherical cutting burrs. These plans are particularly useful in bones of variable density and porosity like the human temporal bone. They result in fast milling in non-critical areas, reducing overall procedure time, and lower forces near vital anatomy. We experimentally demonstrate the algorithm on temporal bone phantoms and show that it reduces mean forces near vital anatomy by 63% and peak forces by 50% in comparison to a CNC-type path, without adding time to the procedure.

Multi-channel cochlear implants (CI) leverage frequency based cochlear tonotopic mapping to map acoustic information to the cochlear place of stimulation which is primarily determined by electrode locations. Despite the fact that electrode locations within the cochlea are unique to each patient, the acoustic frequencies assigned to the electrodes by the CI processor are determined generically, resulting in a mismatch between intended and actual pitch perception. This is known to be a limiting factor for hearing outcomes with CIs. In this study, we propose a novel, image-guided CI processor programming strategy to select more optimal, patient-customized frequency assignments. The performance of the proposed strategy was evaluated using vocoder-based simulations with ten normal hearing listeners. In our simulations, our strategy results in significantly better speech recognition scores than the standard clinical strategy.

Cochlear electrode insertion is a challenging manual procedure. One technique requires the physician to coordinate the motions of an electrode array approximately 1mm in diameter and the smaller stylet within it, using miniature forceps. A new minimally invasive access technique precludes forceps insertion because the electrode must travel through a small-diameter drilled hole to reach the cochlear access point. To address this, we present an automated insertion tool. This second generation device not only enables deployment in the minimally invasive setting, but also makes insertion velocity profiles repeatable and can sense insertion forces. Force sensing is essential because insertion forces can indicate impending damage to cochlear membranes, but are below the thresholds that can be sensed by human hands. The Automated Insertion Tool we present is designed to be compact and lightweight for straightforward integration into the operating room environment. It is able to insert an electrode with a resolution of less than 1μm, achieve velocities of up to 5 mm/sec and resolve forces as small as 0.005 N.