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On cone-beam computed tomography artifacts induced by titanium implants.

Ralf Kurt Willy Schulze¹, Dorothea Berndt, Bernd d'Hoedt

¹Department of Oral Surgery (and Oral Radiology), University Medical Center of the Johannes Gutenberg-University, Augustusplatz 2, D-55131 Mainz, Germany. rschulze@mail.uni-mainz.de

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|October 23, 2009

Summary

Beam-hardening artifacts in cone-beam computed tomography (CBCT) are significant with titanium implants. Improved reconstruction algorithms are needed to reduce these errors, as current methods are insufficient.

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Area of Science:

Medical Imaging
Radiological Physics
Computational Imaging

Background:

Beam-hardening artifacts are a common issue in cone-beam computed tomography (CBCT).
Titanium implants, frequently used in medical procedures, can exacerbate these artifacts due to their high density.
Understanding the geometrical and physical properties of CBCT acquisition is crucial for artifact mitigation.

Purpose of the Study:

To review the mathematical basis of beam-hardening artifacts in CBCT reconstruction.
To investigate the geometrical factors influencing these artifacts.
To evaluate beam-hardening effects from titanium implants using simulations and experiments.

Main Methods:

Investigated geometrical and physical properties of CBCT projection acquisition.
Derived the impact of these properties on CBCT images with titanium implants.
Computed beam-hardening effects for simplified polychromatic scenarios (80 and 110 kV) and compared with experimental phantom data.

Main Results:

Simulations showed massive absorption of low-energy X-rays by titanium implants (e.g., 99.7% at 80 kV).
Experimental data revealed significantly reduced gray values (-46% to -55%) and increased noise (+67% to +73%) in artifact-affected regions.
These effects were observed in two different CBCT systems.

Conclusions:

Massive beam-hardening artifacts are confirmed for typical titanium implant diameters and CBCT energies.
Current reconstruction algorithms are too crude to adequately address these artifacts.
Effective artifact reduction requires advanced mathematical modeling of the physical image acquisition process.