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Rapid 3D bioprinting from medical images: an application to bone scaffolding
Author(s): Daniel Z. Lee; Matthew W. Peng; Rohit Shinde; Arbab Khalid; Abigail Hong; Sara Pennacchi; Abel Dawit; Daniel Sipzner; Jayaram K. Udupa; Chamith S. Rajapakse
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Paper Abstract

Bioprinting of tissue has its applications throughout medicine. Recent advances in medical imaging allows the generation of 3-dimensional models that can then be 3D printed. However, the conventional method of converting medical images to 3D printable G-Code instructions has several limitations, namely significant processing time for large, high resolution images, and the loss of microstructural surface information from surface resolution and subsequent reslicing. We have overcome these issues by creating a JAVA program that skips the intermediate triangularization and reslicing steps and directly converts binary dicom images into G-Code.

In this study, we tested the two methods of G-Code generation on the application of synthetic bone graft scaffold generation. We imaged human cadaveric proximal femurs at an isotropic resolution of 0.03mm using a high resolution peripheral quantitative computed tomography (HR-pQCT) scanner. These images, of the Digital Imaging and Communications in Medicine (DICOM) format, were then processed through two methods. In each method, slices and regions of print were selected, filtered to generate a smoothed image, and thresholded. In the conventional method, these processed images are converted to the STereoLithography (STL) format and then resliced to generate G-Code. In the new, direct method, these processed images are run through our JAVA program and directly converted to G-Code. File size, processing time, and print time were measured for each.

We found that this new method produced a significant reduction in G-Code file size as well as processing time (92.23% reduction). This allows for more rapid 3D printing from medical images.

Paper Details

Date Published: 6 March 2018
PDF: 5 pages
Proc. SPIE 10579, Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications, 105790H (6 March 2018); doi: 10.1117/12.2293606
Show Author Affiliations
Daniel Z. Lee, Univ. of Pennsylvania (United States)
Matthew W. Peng, Univ. of Pennsylvania (United States)
Rohit Shinde, Univ. of Pennsylvania (United States)
Arbab Khalid, Univ. of Pennsylvania (United States)
Abigail Hong, Univ. of Pennsylvania (United States)
Sara Pennacchi, Univ. of Pennsylvania (United States)
Abel Dawit, Univ. of Pennsylvania (United States)
Daniel Sipzner, Univ. of Pennsylvania (United States)
Jayaram K. Udupa, Univ. of Pennsylvania (United States)
Chamith S. Rajapakse, Univ. of Pennsylvania (United States)


Published in SPIE Proceedings Vol. 10579:
Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications
Jianguo Zhang; Po-Hao Chen, Editor(s)

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