In the last decade, the clinical applications of three-dimensional (3D) printed models, in the neurosurgery field among others, have expanded widely based on several technical improvements in 3D printers, an increased variety of materials, but especially in postprocessing software. More commonly, physical models are obtained from a unique imaging technique with potential utilization in presurgical planning, generation/creation of patient-specific surgical material and personalized prosthesis or implants.Using specific software solutions, it is possible to obtain a more accurate segmentation of different anatomical and pathological structures and a more precise registration between different medical image sources allowing generating hybrid computed tomography (CT) and magnetic resonance imaging (MRI) 3D printed models. The need of neurosurgeons for a better understanding of the complex anatomy of central nervous system (CNS) and spine is pushing the use of these hybrid models, which are able to combine morphological information from CT and MRI, and also able to add physiological data from advanced MRI sequences, such as diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), perfusion weighted imaging (PWI) and functional MRI (fMRI). The inclusion of physiopathological data from advanced MRI sequences enables neurosurgeons to identify those areas with increased biological aggressiveness within a certain lesion prior to surgery or biopsy procedures. Preliminary data support the use of this more accurate presurgical perspective, to select the better surgical approach, reduce the global length of surgery and minimize the rate of intraoperative complications, morbidities or patient recovery times after surgery. The use of 3D printed models in neurosurgery has also demonstrated to be a valid tool for surgeons training and to improve communication between specialists and patients. Further studies are needed to test the feasibility of this novel approach in common clinical practice and determine the degree of improvement the neurosurgeons receive and the potential impact on patient outcome.

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Magnetic resonance is an imaging modality that implies a high complexity for radiographers. Despite some simulators having been developed for training purposes, we are not aware of any attempt to quantitatively measure their educational performance. The present study gives an answer to the question: Does an MRI simulator built on specific functional and non-functional requirements help radiographers learn MRI theoretical and practical concepts better than traditional educational method based on lectures? Our study was carried out in a single day by a total of 60 students of a main hospital in Madrid, Spain. The experiment followed a randomized pre-test post-test design with a control group that used a traditional educational method, and an experimental group that used our simulator. Knowledge level was assessed by means of an instrument with evidence of validity in its format and content, while its reliability was analyzed after the experiment. Statistical differences between both groups were measured. Significant statistical differences were found in favor of the participants who used the simulator for both the post-test score and the gain (difference between post-test and pre-test scores). The effect size turned out to be significant as well. In this work we evaluated a magnetic resonance simulation paradigm as a tool to help in the training of radiographers. The study shows that a simulator built on specific design requirements is a valuable complement to traditional education procedures, backed up with significant quantitative results.

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