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Radiographics. 2017 Sep-Oct;37(5):1424-1450. doi: 10.1148/rg.2017160165. Epub 2017 Aug 11.

Measuring and Establishing the Accuracy and Reproducibility of 3D Printed Medical Models.

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From the Applied Imaging Science Laboratory, Department of Radiology, Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115 (E.G., D.M.); 3D Medical Applications Center, Department of Radiology, Walter Reed National Military Medical Center, Bethesda, Md (P.L.); and Department of Radiology, University of Ottawa Faculty of Medicine and The Ottawa Hospital Research Institute, Ottawa, Ontario, Canada (F.J.R.).


Despite the rapid growth of three-dimensional (3D) printing applications in medicine, the accuracy and reproducibility of 3D printed medical models have not been thoroughly investigated. Although current technologies enable 3D models to be created with accuracy within the limits of clinical imaging spatial resolutions, this is not always achieved in practice. Inaccuracies are due to errors that occur during the imaging, segmentation, postprocessing, and 3D printing steps. Radiologists' understanding of the factors that influence 3D printed model accuracy and the metrics used to measure this accuracy is key in directing appropriate practices and establishing reference standards and validation procedures. The authors review the various factors in each step of the 3D model printing process that contribute to model inaccuracy, including the intrinsic limitations of each printing technology. In addition, common sources of model inaccuracy are illustrated. Metrics involving comparisons of model dimensions and morphology that have been developed to quantify differences between 3D models also are described and illustrated. These metrics can be used to define the accuracy of a model, as compared with the reference standard, and to measure the variability of models created by different observers or using different workflows. The accuracies reported for specific indications of 3D printing are summarized, and potential guidelines for quality assurance and workflow assessment are discussed. Online supplemental material is available for this article. ©RSNA, 2017.

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