Understanding Metal Artifact Reduction in CT

Physics Pulse: Understanding Metal Artifact Reduction in CT

Metal implants are increasingly common in today's imaging population, and with them comes a familiar challenge: bright and dark streaks that obscure anatomy, distort Hounsfield Units (HU), and compromise quantitative workflows. In modern CT practice, metal artifact reduction (MAR) is no longer just a convenience feature. It is a core imaging physics issue with direct implications for diagnostic CT, radiation therapy planning, and attenuation correction in PET/CT and SPECT/CT.^1,2

Orthopedic prostheses, dental restorations, spinal hardware, pacemakers, vascular clips, and other metallic devices can all produce structured artifacts that reduce confidence in image interpretation. As CT becomes more quantitative and more closely tied to downstream clinical decisions, understanding MAR becomes increasingly important for both technologists and physicists.^1,2

Why metal causes artifacts in CT

Metal causes CT artifacts because it violates the assumptions built into conventional reconstruction models. CT reconstruction works best when attenuation measurements behave like reasonably well-sampled, linear line integrals through tissue. Dense, high-atomic-number materials do not behave that way.^2,3

Beam hardening

CT uses a polychromatic X-ray beam, meaning photons span a range of energies. When this beam passes through dense metal, lower-energy photons are preferentially absorbed, leaving a transmitted beam with a higher average energy. This is known as beam hardening. Since the reconstruction algorithm does not fully account for this nonlinear spectral shift, the result can be cupping, dark bands, and streak artifacts near or between metallic objects.^2,4

Photon starvation

When metal is sufficiently dense or thick, very few photons reach the detector along certain projection paths. This produces extremely noisy or unstable projection data, which reconstruct as alternating bright and dark streaks radiating from the implant. This effect is referred to as photon starvation and is a major cause of severe metal streak artifacts.^2,3,15

Scatter, partial volume, and detector limitations

Scatter contributes unwanted signal at the detector, while partial volume effects occur when a voxel contains a mixture of metal and adjacent tissue. In addition, detector saturation, lag, nonlinear response, and limited dynamic range can worsen the instability of heavily attenuated projection data. These departures from ideal conditions further increase structured artifacts in the reconstructed image.^3,5

Why this matters clinically

Metal artifacts are not just a cosmetic image quality issue. They can directly affect diagnosis, quantitation, and treatment planning.^1,2

In diagnostic CT, artifacts can obscure adjacent anatomy and make it more difficult to evaluate fractures, loosening, fluid collections, or other pathology near implanted hardware.^2,3

In radiation therapy planning, corrupted HU values can affect electron density mapping and dose calculation. Even when the implant itself is not in the target, artifact in the surrounding soft tissue can introduce uncertainty into treatment planning workflows.^1,8

In PET/CT and SPECT/CT, the CT dataset is often used to generate the attenuation map. If metal artifact distorts the CT numbers, those errors may propagate into attenuation correction and create false hot or cold regions on the nuclear medicine images.^1,2

For medical physicists, this is why MAR should be viewed not as a simple post-processing enhancement, but as a clinically relevant imaging physics tool that requires understanding and validation.

Main strategies for metal artifact reduction

Broadly, MAR approaches fall into three categories: acquisition/protocol optimization, projection-space MAR, and image-space or hybrid MAR.^2,6,7

1. Acquisition and protocol optimization

The first opportunity to reduce artifact is during image acquisition.

Using a higher tube potential can improve beam penetration and reduce the severity of beam hardening and photon starvation, although this may come with some loss of soft tissue contrast.^5,8

Adequate mAs can also help improve signal reliability in highly attenuated projections, particularly when paired with iterative reconstruction methods that help control image noise.⁵

A particularly important development is dual-energy or spectral CT, which can generate virtual monoenergetic images at higher keV. These high-keV images often reduce streaking around metal and improve visualization of adjacent structures. In many cases, virtual monoenergetic imaging is one of the most effective scanner-based tools for reducing metal artifact while preserving local anatomy.^2,8

2. Projection-space MAR

Projection-space MAR operates in the sinogram domain, where corrupted raw data are identified and corrected before final image reconstruction.^2,7

A typical workflow includes:

1. reconstructing an initial image,
2. segmenting the metal,
3. forward-projecting the metal mask into the sinogram,
4. identifying metal-affected measurements,
5. estimating replacement values using interpolation, model-based fitting, or prior-image guidance, and
6. reconstructing a corrected image with the true metal reinserted afterward.^7,9

These methods are especially effective for reducing streaks caused by photon starvation and are the foundation of many commercial vendor MAR solutions.^1,2,8

3. Image-space, hybrid, and deep learning MAR

Some MAR methods operate directly on the reconstructed image rather than the raw projection data. These image-space approaches may use filtering, iterative regularization, or hybrid strategies that combine image- and projection-domain corrections.^2,10

More recently, deep learning–based MAR has emerged as a major area of active development. Neural networks can be trained to map artifact-corrupted images or sinograms to cleaner outputs, often reducing complex streak patterns more effectively than conventional interpolation-only approaches.^9,11

Some of these methods are physics-informed, meaning they incorporate prior knowledge of beam hardening or CT system behavior into the learning process. These approaches are intended to reduce oversmoothing and improve robustness.^9,12

Still, caution is warranted. A cleaner image is not always a more accurate one. Deep learning MAR may behave unpredictably with unfamiliar implants or scanner conditions, so proper local validation remains essential before using these tools in quantitative or high-consequence workflows.^11–13

Vendor-specific MAR solutions

Several major CT vendors offer proprietary metal artifact reduction tools. Most of these fall into either the projection-space/hybrid MAR category or the spectral/acquisition-based MAR category. Because vendor implementations are often proprietary, the exact internal algorithm may not always be fully disclosed. However, their general operating principles can still be described in clinically useful terms.

The important practical point is that not all MAR is a single button. Some artifact reduction comes from a dedicated reconstruction algorithm, some comes from dual-energy or spectral reconstruction choices, and some workflows combine both.

Practical tips for technologists

For technologists, patients with metallic implants often require a more deliberate scanning strategy.

• Expect artifact when dense hardware is present, particularly with large orthopedic implants, spinal rods, dental hardware, or multiple metallic objects in the scan field.^2,3
• Use the appropriate protocol, including higher kVp or optimized technique factors when clinically indicated.^5,8
• Know what MAR tools are available on your scanner and how they affect image appearance. MAR may improve visualization, but it can also change the appearance of tissues adjacent to hardware.^1,2
• In workflows such as PET/CT, SPECT/CT, or RT simulation, it may be useful to review both MAR and non-MAR images, since one may look cleaner while the other preserves more of the original data characteristics.^1,8
• Communicate with the interpreting physician or physicist when artifact is severe enough to compromise anatomy, attenuation correction, or planning confidence.

Practical tips for physicists

For medical physicists, MAR should be approached as a commissioning, QA, and workflow issue rather than a purely cosmetic reconstruction choice.

Local validation should include both qualitative artifact assessment and quantitative testing, especially when MAR-corrected images may be used in HU-sensitive applications.^1,2

Representative phantom testing with hardware similar to what is encountered clinically can help evaluate:

• degree of artifact suppression,
• restoration of local HU accuracy,
• preservation of adjacent anatomy, and
• impact on downstream tasks such as attenuation correction or dose calculation.^1,8

This is especially important for RT simulation, PET/CT, and SPECT/CT, where CT inaccuracies may directly affect treatment planning or quantitation.¹

DL-based MAR tools deserve special scrutiny. Although they may substantially reduce visible artifact, physicists should be cautious about potential subtle image alterations, especially for unfamiliar implant types or out-of-distribution cases.^11–13

Bottom line

Metal artifact reduction in CT sits at the intersection of X-ray physics, image reconstruction, and clinical workflow. As implanted hardware becomes more common and as quantitative imaging expands, MAR is becoming an increasingly important competency for technologists and medical physicists alike.

The goal is not simply to make the image look cleaner. The real goal is to improve anatomical visibility, preserve quantitative accuracy, and support better downstream clinical decisions in patients with metal hardware.^1,2,14

References

1. Boas FE, Fleischmann D. The application of metal artifact reduction methods on computed tomography scans for radiotherapy applications: A literature review. Front Oncol. 2021. Available at: https://pmc.ncbi.nlm.nih.gov/articles/PMC8200502/
2. Gjesteby L, De Man B, Jin Y, et al. Current and novel techniques for metal artifact reduction at CT. RadioGraphics. 2016;36(6):1770-1791. Available at: https://pubs.rsna.org/doi/abs/10.1148/rg.2018170102
3. Yu L, Li H, Mueller J, et al. Reduction of metal artifacts: beam hardening and photon starvation. Proc SPIE. 2014. Available at: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/9033/90332V/Reduction-of-metal-artifacts--beam-hardening-and-photon-starvation/10.1117/12.2043661.short
4. Rigaku. What Is Beam Hardening in CT? Available at: https://rigaku.com/products/imaging-ndt/x-ray-ct/learning/blog/what-is-beam-hardening-in-ct
5. Barrett JF, Keat N. CT artifacts: causes and reduction techniques. Available at: https://www.openaccessjournals.com/articles/ct-artifacts-causes-and-reduction-techniques.html
6. Meyers J, et al. Advances in metal artifact reduction in CT images. Diagn Interv Imaging. 2023. Available at: https://www.sciencedirect.com/science/article/abs/pii/S0720048X23005909
7. Meyer E, Raupach R, Lell M, Schmidt B, Kachelrieß M. CT metal artifact reduction algorithms: Toward a framework for comparing performance. Available at: https://pmc.ncbi.nlm.nih.gov/articles/PMC7496341/
8. Wellenberg RHH, Boomsma MF, van Osch JAC, et al. Metal artifact reduction techniques for single-energy CT and dual-energy CT: a review. Br J Radiol Open. 2018. Available at: https://academic.oup.com/bjro/article/1/1/bjro.20180045/7239077
9. Ghazi P, et al. Physics-informed sinogram completion for metal artifact reduction in CT. Med Phys. 2023. Available at: https://pubmed.ncbi.nlm.nih.gov/36808913/
10. Siemens Healthineers educational overview. Understanding CT Artifacts: A Comprehensive Guide. Available at: https://www.medical-professionals.com/en/understanding-ct-artifacts-a-comprehensive-guide/
11. Jeon HG, et al. Metal artifacts reduction in CT scans using convolutional neural network. Available at: https://pubmed.ncbi.nlm.nih.gov/33018231/
12. Residual Metal Artifact Reduction in CT Images: An Unsupervised Deep Learning Approach. Med Phys. Available at: https://aapm.onlinelibrary.wiley.com/doi/10.1002/mp.70078
13. Haneda S, et al. AAPM CT metal artifact reduction grand challenge. Med Phys. 2025. Available at: https://aapm.onlinelibrary.wiley.com/doi/full/10.1002/mp.70050
14. AAPM. CT Metal Artifact Reduction (CT-MAR) Grand Challenge. Available at: https://www.aapm.org/GrandChallenge/CT-MAR/
15. Yu L, et al. Reduction of metal artifacts: beam hardening and photon starvation. Proc SPIE. Available at: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/9033/90332V/Reduction-of-metal-artifacts--beam-hardening-and-photon-starvation/10.1117/12.2043661.short