18 March 2016 Determination of surgical variables for a brain shift correction pipeline using an Android application
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Abstract
Brain shift describes the deformation that the brain undergoes from mechanical and physiological effects typically during a neurosurgical or neurointerventional procedure. With respect to image guidance techniques, brain shift has been shown to compromise the fidelity of these approaches. In recent work, a computational pipeline has been developed to predict “brain shift” based on preoperatively determined surgical variables (such as head orientation), and subsequently correct preoperative images to more closely match the intraoperative state of the brain. However, a clinical workflow difficulty in the execution of this pipeline has been acquiring the surgical variables by the neurosurgeon prior to surgery. In order to simplify and expedite this process, an Android, Java-based application designed for tablets was developed to provide the neurosurgeon with the ability to orient 3D computer graphic models of the patient’s head, determine expected location and size of the craniotomy, and provide the trajectory into the tumor. These variables are exported for use as inputs for the biomechanical models of the preoperative computing phase for the brain shift correction pipeline. The accuracy of the application’s exported data was determined by comparing it to data acquired from the physical execution of the surgeon’s plan on a phantom head. Results indicated good overlap of craniotomy predictions, craniotomy centroid locations, and estimates of patient’s head orientation with respect to gravity. However, improvements in the app interface and mock surgical setup are needed to minimize error.
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Rohan Vijayan, Rebekah H. Conley, Reid C. Thompson, Logan W. Clements, Michael I. Miga, "Determination of surgical variables for a brain shift correction pipeline using an Android application", Proc. SPIE 9786, Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling, 978610 (18 March 2016); doi: 10.1117/12.2216991; https://doi.org/10.1117/12.2216991
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