For outdoor navigation, GPS provides the most widelyused means of node localization; however, the level of accuracy provided by low-cost receivers is typically insufficient for use in high-precision applications. Additionally, many of these applications do not require precise absolute Earth coordinates, but rather rely on relative positioning to infer information about the geometric configuration of the constituent nodes in a system. This paper presents a novel approach that uses GPS to derive relative location information for a scalable network of single-frequency receivers. Networked nodes share their raw satellite observations, enabling each node to localize its neighbors in a pairwise fashion as opposed to computing its own standalone position. Random and systematic errors are mitigated in novel ways, challenging long-standing beliefs that precision GPS systems require extensive stationary calibration times or complex equipment configurations. In addition to presenting the mathematical basis for our technique, a working prototype is developed, enabling experimental evaluation of several real-world test scenarios. The results of these experiments indicate sub-meter relative positioning accuracy under various conditions and in varying environments. This represents up to order of magnitude increase in precision over existing absolute positioning techniques or other unimodal GPS-based solutions.

Hypothesis: Generic guidelines for insertion depth of precurved electrodes are suboptimal for many individuals. Background: Insertion depths that are too shallow result in decreased cochlear coverage, and ones that are too deep lift electrodes away from the modiolus and degrade the electro-neural interface. Guidelines for insertion depth are generically applied to all individuals using insertion depth markers on the array that can be referenced against anatomical landmarks. Methods: To normalize our measurements, we determined the optimal position and insertion vector where a precurved array best fits the cochlea for each patient in an IRB-approved, N = 131 subject CT database. The distances from the most basal electrode on an optimally placed array to anatomical landmarks, including the round window (RW) and facial recess (FR), was measured for all patients. Results: The standard deviations of the distance from the most basal electrode to the FR and RW are 0.65 mm and 0.26 mm, respectively. Owing to the high variability in FR distance, using the FR as a landmark to determine insertion depth results in >0.5 mm difference with ideal depth in 44% of cases. Alignment of either of the two most proximal RW markers with the RW would result in over-insertion failures for >80% of cases, whereas the use of the third, most medial marker would result in under-insertion in only 19% of cases. Conclusions: Normalized measurements using the optimized insertion vector show low variance in distance from the basal electrode position to the RW, thereby suggesting it as a better landmark for determining insertion depth than the FR.

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Cochlear implants (CIs) use electrode arrays that are surgically inserted into the cochlea to treat patients with hearing loss. For CI recipients, sound bypasses the natural transduction mechanism and directly stimulates the neural regions, thus creating a sense of hearing. Post-operatively, CIs need to be programmed. Traditionally, this is done by an audiologist who is blind to the positions of the electrodes relative to the cochlea and only relies on the subjective response of the patient. Multiple programming sessions are usually needed, which can take a frustratingly long time. We have developed an image-guided cochlear implant programming (IGCIP) system to facilitate the process. In IGCIP, we segment the intra-cochlear anatomy and localize the electrode arrays in the patient's head CT image. By utilizing their spatial relationship, we can suggest programming settings that can significantly improve hearing outcomes. To segment the intra-cochlear anatomy, we use an active shape model (ASM)-based method. Though it produces satisfactory results in most cases, sub-optimal segmentation still happens. As an alternative, herein we explore using a deep learning method to perform the segmentation task. Large image sets with accurate ground truth (in our case manual delineation) are typically needed to train a deep learning model for segmentation but such a dataset does not exist for our application. To tackle this problem, we use segmentations generated by the ASM-based method to pre-train the model and fine-tune it on a small image set for which accurate manual delineation is available. Using this method, we achieve better results than the ASM-based method.