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Updated: Sep 13, 2025
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3D Printing of Preclinical X-ray Computed Tomographic Data Sets
Published on: March 22, 2013
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A novel method to simulate radiographs of 3D printed objects.
Maxwell C Campbell1, Steven I Pollmann2, Jaques S Milner2
1School of Mechanical and Materials Engineering, Western University, London, Ontario, Canada.
Journal of Applied Clinical Medical Physics
|August 3, 2025
Summary
This study developed a tool to simulate radiographic artifacts from 3D printed objects, aiding designers in evaluating radiographic performance before physical prototyping. The simulation tool accurately predicts artifacts, saving time and resources.
Area of Science:
- Medical Imaging
- Additive Manufacturing
- Materials Science
Background:
- 3D printing (Additive Manufacturing) offers significant potential in healthcare, but internal infill and external geometry can cause radiographic artifacts.
- Simulating the mechanical performance of 3D printed parts is feasible, yet simulating radiographic artifacts remains a challenge.
- Undesirable artifacts limit the full application of 3D printing in radiography and medical imaging.
Purpose of the Study:
- To develop a computational tool for simulating radiographic artifacts produced by 3D printed objects.
- To enable users to predict and analyze the impact of 3D printing designs on radiographic outcomes.
Main Methods:
- Three identical hexagonal objects were 3D printed using polylactic acid (PLA) filament with varying infill patterns (rectilinear grid, cubic, gyroid) on a fused deposition modeling (FDM) printer.
- Objects were radiographed using clinical-standard protocols, and results were compared to simulations generated from the slicing G-Code.
- Physical and simulated radiographs were analyzed to determine angles of least and greatest artifact.
Main Results:
- A strong visual correlation was observed between physically captured and virtually simulated radiographs.
- Least artifact projection angles were 22.5° (grid), 22.5° (cubic), and 12.25° (gyroid).
- Greatest artifact projection angles were 0° (grid), 45° (cubic), and 45° (gyroid).
Conclusions:
- The developed tool allows designers to evaluate the radiographic performance of 3D printed components computationally.
- This simulation capability significantly reduces the need for physical prototyping and radiographing, accelerating design iteration.
- The tool enhances the utility of 3D printing in medical and healthcare applications by addressing radiographic artifact prediction.
