Author/Authors :
Leng, Shuai Department of Radiology - Mayo Clinic - Rochester- MN, USA , McGee, Kiaran Department of Radiology - Mayo Clinic - Rochester- MN, USA , Morris, Jonathan Department of Radiology - Mayo Clinic - Rochester- MN, USA , Alexander, Amy Department of Radiology - Mayo Clinic - Rochester- MN, USA , Vrieze, Thomas Department of Radiology - Mayo Clinic - Rochester- MN, USA , McCollough, Cynthia H. Department of Radiology - Mayo Clinic - Rochester- MN, USA , Matsumoto, Jane Department of Radiology - Mayo Clinic - Rochester- MN, USA , Kuhlmann, Joel Division of Engineering - Mayo Clinic - Rochester - MN, USA
Abstract :
The purpose of this study is to provide a framework for the development of a quality assurance (QA)
program for use in medical 3D printing applications. An interdisciplinary QA team was built with expertise from all
aspects of 3D printing. A systematic QA approach was established to assess the accuracy and precision of each step
during the 3D printing process, including: image data acquisition, segmentation and processing, and 3D printing and
cleaning. Validation of printed models was performed by qualitative inspection and quantitative measurement. The latter
was achieved by scanning the printed model with a high res olution CT scanner to obtain images of the printed model,
which were registered to the original patient images and the distance between them was calculated on a point-by-point
basis.
Results: A phantom-based QA process, with two QA phantoms, was also developed. The phantoms went through the
same 3D printing process as that of the patient models to generate printed QA models. Physical measurement, fit tests,
and image based measurements were performed to compare the printed 3D model to the original QA phantom, with
its known size and shape, providing an end-to-end assessment of errors involved in the complete 3D printing process.
Measured differences between the printed model and the original QA phantom ranged from -0.32 mm to 0.13 mm for
the line pair pattern. For a radial-ulna patient model, the mean distance between the original data set and the scanned
printed model was -0.12 mm (ranging from -0.57 to 0.34 mm), with a standard deviation of 0.17 mm.
Conclusions: A comprehensive QA process from image acquisition to completed model has been developed. Such a
program is essential to ensure the required accuracy of 3D printed models for medical applications.
Keywords :
Computed tomography (CT) , Quality assurance , 3D printing , Imaging , Segmentation Phantom