Purpose: To characterize the radiation response of 3D printed materials for photon, electron, proton, and CT in absolute terms and as compared to a Plastic Water® baseline for extrapolation to clinical use.
Methods: Six materials were 3D printed into blocks using a Fused Deposition Modelling printer. Measurements were made using electron beam to create PDD curves, using photon beam to create TMR curves, and using proton beam to determine the RSP of the materials. The materials were also scanned using CT to examine the variations in HU value within each block and between blocks. The effective density of each printed block was determined to examine the variations in the printing. The densities of the block were used to analyze the results of the CT, electron, photon, and proton results.
Results: The effective density of each material varied widely between blocks and within each block. All results for each radiation type were dependent on the effective density of the 3D printed material, with an approximately linear relationship with the average HU value of the material, the R50 of the PDD curves for electron, and the RSP of the block using proton beam. Materials with densities lower than Plastic Water exhibited a negative percent difference trend compared with Plastic Water®, and materials with densities higher exhibited a positive trend.
Conclusion: Although 3D printing has much promise for use in radiation oncology, establishing a solid quality assurance protocol prior to its implementation is key to an accurate and successful clinical application. It is recommended that each 3D printed object be properly characterized before clinical use, including determining the effective density. Through the implementation of these measures, 3D printing in the clinical setting has the potential to further improve patient care within radiation oncology departments.
Funding Support, Disclosures, and Conflict of Interest: The University of Oklahoma Health Sciences Center funded the purchase of 3D printing materials.
TH- Response Assessment: Modeling: other than machine learning