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Related Experiment Video

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3D Printing of Preclinical X-ray Computed Tomographic Data Sets
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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
PubMed
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.

Keywords:
3D printingfused deposition modelingradiography artifact

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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.