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2014 Abstracts

3D printing from MRI data of stroke and Alzheimer’s disease subjects: An educational model of neurologic disease

Brett Gardiner, Brigham Young University

Life Sciences

Normal human anatomy used in the classroom is not reflective of variations confronted in pathology subjects. Current commercialized models are not products of real data, rather representations of it. While learning complicated medical anatomy, students take an enormous stride from the anatomy lab to situational surgical settings. 3D models can bridge this gap in medical education without patient risk, particularly for the brain where surface regions have strong associations to specific physiological activity. Subject specific models are especially advantageous for comprehending real surface morphology of neurologic diseases. Using rapid prototype technology, we have developed an accessible process to produce physical 3D models from specific MRI data of stroke and Alzheimer’s subjects. The neuroanatomical abnormalities modeled from real data by our 3D printouts will educate students on the anatomical variations encountered in an authentic clinical scenario of neuropathology. Our project consists of three phases: (1) image acquisition, (2) post-processing imaging data with segmentation, and (3) 3D printing. By delineating cortical regions we are providing a unique multidimensional facet of clinically accurate data not before available to the classroom. This powerful and versatile technique can allow students and professionals to visualize the inherently complicated structures as seen in clinical neuropathology. From students in the classroom, lawyers in the courtroom or preoperative surgical explanations, these customizable models will resemble real anatomical information. Through rapid prototyping of specific subject data, unique variations in pathology can be reviewed outside of the clinical setting. Beyond its potential use by teachers, lawyers and doctors can benefit from a 3D production to enhance their explanations of anatomical variations from specific pathological subject data.