iOS neuro-imaging was started by Dr. Andrew Abumoussa (MD), with the focus of creating an iOS application designed to aid neurosurgeons in surgery preparation by projecting the course in which a surgical instrument would be inserted. This is done by rendering and mapping scanned CT images in correspondence to one’s facial features to project a viable model of the patient’s brain which can later be used to pinpoint precise surgical locations. This is a very important development because the successful production of this application would significantly reduces hospital costs, replacing the need for expensive machinery in order to map-out the scanned results of these CT images. Due to the only requirements being an iPhone on a tripod, an Apple Watch worn by the surgeon, and a 3D printed object that attaches to the surgeon tool, this essentially makes this technology available to all surgeons around the world.
Introducing a potentially revolutionary application to the preparation phase of neurosurgery, essentially offers an accessible solution for surgeons to create models and perform more accurate surgeries which will benefit all humans in the community. This not only will improve surgeries performed worldwide, it will also significantly reduce hospital costs and allow better usage of funding throughout the industry.
Considering that there needs to be an imaging process and a mapping process when using this app, the computations and imaging data that is captured by the iPhone is not necessarily fully accurate. This is due to the introduction of noise between transitions of images and while moving the camera to perceive an image of the patient’s face with correspondence to their scanned CT images. It is important to reduce this noise through the usage of image filtering procedures, however there are many different image filtering algorithms but in this instance, the Kalman Filter would work best. By introducing a Kalman filter to the data streamed by the iPhone, we can reduce noise through the process of combining high-pass/low-pass signals which will make the images captured have higher accuracy and more precise data points.
Another important component that provides essential data to the app is the Apple Watch, this acts as a second degree of ensuring data accuracy for the app. The apple watch needs to be able to streamline data that is generated from the acceleration of the surgeon wearing the watch in order for the data to be processed and utilized. By utilizing the apple watch’s Gyroscope API, the data can now be streamed and interpreted by the iPhone and then passed into the Kalman Filter which then ensures an accurate portrayal of data imaging.
Our work will ensure that the app produces noise-reduced images throughout the imaging process. This refinement will improve the quality of processed patient scan results, ensuring that neurosurgeons receive clearer and more accurate representations of the patient’s brain structure in preparation to surgery. We aim to provide a greater basis for the projected trajectory of surgical instruments with advancements focusing on not only the visual clarities, but the generated 3D models also optimized so that it minimizes the likelihood of displaying errors.