Journal of Nuclear Science, Engineering and Technology (JONSAT)
In cooperation with the Iranian Nuclear Society

Comparative study of analytical metal artifact reduction methods in CT imaging

Document Type : Research Paper

Authors

M. Ghorbanzadeh 1
S.A. Hosseini 1
B. Vosoughi-Vahdat 2
A. AkhavanAllaf 3
H. Arabi 3

1 Department of Nuclear Engineering, Energy Engineering Department, Sharif University of Technology, P.O.Box: 11155-1639, Tehran – Iran

2 Department of Medical Engineering, Electrical Engineering Department, Sharif University of Technology, P.O.Box: 11155-1639, Tehran – Iran

3 Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland

https://doi.org/10.24200/nst.2022.1426
Abstract
Over the past few decades, computed tomography (CT) imaging has been merged as one of the leading cross-sectional imaging techniques in a wide range of clinical applications in diagnostic radiology, oncology, and multimodal molecular imaging. Despite the recognized value of this imaging modality, the quality and accuracy of CT images can be compromised by a number of implants. The presence of metal objects such as dental fillings, hip or knee prostheses, heart pacemakers, war fragments, and spinal cages can cause severe image artifacts. These types of artifacts appear as black and white streaks in the CT images, obscuring the structures and tissues around the metal implant which decreases the diagnostic values of the images. Metal artifacts also affect the accuracy of radiation therapy treatment planning, which relies on X-ray images to determine electron density and estimate the relative stopping power of particles. In this regard, different algorithms of the Metal Artifact Reduction (MAR) have been proposed over the decades to address this issue. In this study, five commonly used MAR algorithms in clinical practice have been evaluated using simulated and clinical datasets. These algorithms include linear interpolation (LI_MAR) of the degraded data in the sinogram space, reduction of metal artifacts by normalization method (NMAR), metal deletion technique (MDT), Orthopedic metal artifact reduction (OMAR), and a method based on iteration algorithms (MAP). Clinical CT images in different anatomical regions of the body, with different dimensions and types of metal implants, have been studied to evaluate the performance of the MAR algorithms. In order to quantitatively evaluate the quality of CT images corrected by the different MAR algorithms, the Normalized Root Mean Square Error (NRMSE) metric was employed. The quantitative analysis demonstrated the overall superior performance of the NMAR algorithm in effective metal artifact reduction compared to the other algorithms. The NMAR method exhibited relatively less signal distortion and reasonable processing time which make it a dependable solution in clinical practice.

Highlights

1. W.A. Kalender, X-ray computed tomography, Phys Med Biol, 51(13),  R29-43, (Jul 7 2006).

 

2.             B. Ghane, et al, Quantitative analysis of image quality in low-dose CT imaging for Covid-19 patients, arXiv preprint arXiv:2102.08128, (2021).

 

3.             H. Arabi, H. Zaidi, Truncation compensation and metallic dental implant artefact reduction in PET/MRI attenuation correction using deep learning-based object completion, Phys Med Biol, 65(19), 195002, (Sep 25 2020).

 

4.             A. Mehranian, et al, X-ray CT metal artifact reduction using wavelet domain $ L_ {0} $ sparse regularization, IEEE Transactions on Medical Imaging, 32(9), 1707-1722 (2013).

 

5.             Giantsoudi, et al, Metal artifacts in computed tomography for radiation therapy planning: dosimetric effects and impact of metal artifact reduction, Physics in Medicine & Biology, 62(8), R49 (2017).

 

6. T.C. Kwee, R.M. Kwee, Chest CT in COVID-19: What the Radiologist Needs to Know, Radiographics, 40(7), 1848-1865, (Nov-Dec 2020).

 

7.             H. Arabi, A.R.K. Asl, Feasibility study of a new approach for reducing of partial volume averaging artifact in CT scanner, In 2010 17th Iranian Conference of Biomedical Engineering (ICBME),

1-4 (2010).

 

8. L. Gjesteby, et al., Metal artifact reduction in CT: where are we after four decades?, Ieee Access, 4,  5826-5849 (2016).

 

9. F.E. Boas, D. Fleischmann, CT artifacts: causes and reduction techniques, Imaging Med, 4(2), 229-240 (2012).

 

10. H. Arabi, H. Zaidi, Deep learning-based metal artefact reduction in PET/CT imaging, Eur Radiol, (Feb 10 2021).

 

11.          C. Xu, et al, An algorithm for efficient metal artifact reductions in permanent seed, Med Phys, 38(1),

47-56 (Jan 2011).

 

12. W.A. Kalender, R. Hebel, J. Ebersberger, Reduction of CT artifacts caused by metallic implants, Radiology, 164(2), 576-7, (Aug 1987).

 

13. M. Abdoli, et al, Reduction of dental filling metallic artifacts in CT-based attenuation correction of PET data using weighted virtual sinograms optimized by a genetic algorithm, Med Phys, 37(12), 6166-77,

(Dec 2010).

 

14. E. Meyer, et al, Normalized metal artifact reduction (NMAR) in computed tomography, Med Phys, 37(10), 5482-93, (Oct 2010).

 

15. A. Mehranian, et al, 3D prior image constrained projection completion for X-ray CT metal artifact reduction, IEEE Transactions on Nuclear Science, 60(5), 3318-3332 (2013).

 

16.          F.E. Boas, D. Fleischmann, Evaluation of two iterative techniques for reducing metal artifacts in computed tomography, Radiology, 259(3), 894-902 (Jun 2011).

 

17. P. Healthcare, Metal artifact reduction for orthopedic implants (O-MAR), White Paper, Philips CT Clinical Science, Andover, Massachusetts, (2012).

 

18. M. Sakamoto, et al., Automated segmentation of hip and thigh muscles in metal artifact contaminated CT using CNN, In International Forum on Medical Imaging in Asia 2019, 11050, 110500S: International Society for Optics and Photonics (2019).

 

19. M. Bal, L. Spies, Metal artifact reduction in CT using tissue-class modeling and adaptive prefiltering, Med Phys, 33(8), 2852-9 (Aug 2006).

 

20. N.D. Osman, et al, Evaluation of Two Sinogram Interpolation Methods for Metal Artefacts Reduction in Computed Tomography, In 2018 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), 137-139: IEEE (2018).

Keywords

Medical imaging
Metal artifact
CT
MAR
1. W.A. Kalender, X-ray computed tomography, Phys Med Biol, 51(13),  R29-43, (Jul 7 2006).
 
2.             B. Ghane, et al, Quantitative analysis of image quality in low-dose CT imaging for Covid-19 patients, arXiv preprint arXiv:2102.08128, (2021).
 
3.             H. Arabi, H. Zaidi, Truncation compensation and metallic dental implant artefact reduction in PET/MRI attenuation correction using deep learning-based object completion, Phys Med Biol, 65(19), 195002, (Sep 25 2020).
 
4.             A. Mehranian, et al, X-ray CT metal artifact reduction using wavelet domain $ L_ {0} $ sparse regularization, IEEE Transactions on Medical Imaging, 32(9), 1707-1722 (2013).
 
5.             Giantsoudi, et al, Metal artifacts in computed tomography for radiation therapy planning: dosimetric effects and impact of metal artifact reduction, Physics in Medicine & Biology, 62(8), R49 (2017).
 
6. T.C. Kwee, R.M. Kwee, Chest CT in COVID-19: What the Radiologist Needs to Know, Radiographics, 40(7), 1848-1865, (Nov-Dec 2020).
 
7.             H. Arabi, A.R.K. Asl, Feasibility study of a new approach for reducing of partial volume averaging artifact in CT scanner, In 2010 17th Iranian Conference of Biomedical Engineering (ICBME),
1-4 (2010).
 
8. L. Gjesteby, et al., Metal artifact reduction in CT: where are we after four decades?, Ieee Access, 4,  5826-5849 (2016).
 
9. F.E. Boas, D. Fleischmann, CT artifacts: causes and reduction techniques, Imaging Med, 4(2), 229-240 (2012).
 
10. H. Arabi, H. Zaidi, Deep learning-based metal artefact reduction in PET/CT imaging, Eur Radiol, (Feb 10 2021).
 
11.          C. Xu, et al, An algorithm for efficient metal artifact reductions in permanent seed, Med Phys, 38(1),
47-56 (Jan 2011).
 
12. W.A. Kalender, R. Hebel, J. Ebersberger, Reduction of CT artifacts caused by metallic implants, Radiology, 164(2), 576-7, (Aug 1987).
 
13. M. Abdoli, et al, Reduction of dental filling metallic artifacts in CT-based attenuation correction of PET data using weighted virtual sinograms optimized by a genetic algorithm, Med Phys, 37(12), 6166-77,
(Dec 2010).
 
14. E. Meyer, et al, Normalized metal artifact reduction (NMAR) in computed tomography, Med Phys, 37(10), 5482-93, (Oct 2010).
 
15. A. Mehranian, et al, 3D prior image constrained projection completion for X-ray CT metal artifact reduction, IEEE Transactions on Nuclear Science, 60(5), 3318-3332 (2013).
 
16.          F.E. Boas, D. Fleischmann, Evaluation of two iterative techniques for reducing metal artifacts in computed tomography, Radiology, 259(3), 894-902 (Jun 2011).
 
17. P. Healthcare, Metal artifact reduction for orthopedic implants (O-MAR), White Paper, Philips CT Clinical Science, Andover, Massachusetts, (2012).
 
18. M. Sakamoto, et al., Automated segmentation of hip and thigh muscles in metal artifact contaminated CT using CNN, In International Forum on Medical Imaging in Asia 2019, 11050, 110500S: International Society for Optics and Photonics (2019).
 
19. M. Bal, L. Spies, Metal artifact reduction in CT using tissue-class modeling and adaptive prefiltering, Med Phys, 33(8), 2852-9 (Aug 2006).
 
20. N.D. Osman, et al, Evaluation of Two Sinogram Interpolation Methods for Metal Artefacts Reduction in Computed Tomography, In 2018 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), 137-139: IEEE (2018).

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