Mobile C-Arm systems have enabled interventional spine procedures, such as facet joint injections, to be performed minimally-invasively under X-ray or fluoroscopy guidance. The downside to these procedures is the radiation exposure the patient and medical staff are subject to, which can vary greatly depending on the procedure as well as the skill and experience of the team. Standard training methods for these procedures involve the use of a physical C-Arm with real X-rays training on either cadavers or via an apprenticeship-based program. Many guidance systems have been proposed in the literature which aim to reduce the amount of radiation exposure intraoperatively by supplementing the X-ray images with digitally reconstructed radiographs (DRRs). These systems have shown promising results in the lab but have proven difficult to integrate into the clinical workflow due to costly equipment, safety protocols, and difficulties in maintaining patient registration. Another approach for reducing the amount of radiation exposure is by providing better hands-on training for C-Arm positioning through a pre-operative simulator. Such simulators have been proposed in the literature but still require access to a physical C-Arm or costly tracking equipment. With the goal of providing hands-on, accessible training for C-Arm positioning tasks, we have developed a miniature 3D-printed C-Arm simulator using accelerometer-based tracking. The system is comprised of a software application to interface with the accelerometers and provide a real-time DRR display based on the position of the C-Arm source. We conducted a user study, consisting of control and experimental groups, to evaluate the efficacy of the system as a training tool. The experimental group achieved significantly lower procedure time and higher positioning accuracy than the control group. The system was evaluated positively for its use in medical education via a 5-pt likert scale questionnaire. C-Arm positioning tasks are associated with a highly visual learning-based nature due to the spatial mapping required from 2D fluoroscopic image to 3D C-Arm and patient. Due to the limited physical interaction required, this task is well suited for training in Virtual Reality (VR), eliminating the need for a physical C-Arm. To this end, we extended the system presented in chapter 2 to an entirely virtual-based approach. We implemented the system as a 3DSlicer module and conducted a pilot study for preliminary evaluation. The reception was overall positive, with users expressing enthusiasm towards training in VR, but also highlighting limitations and potential areas of improvement of the system
