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Conference Paper
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Year 2023, Volume: 26, 770 - 779, 30.12.2023

Abdessalem Benammar Aicha Allag Imad Araar Ahmed Benyahıa Redouane Draı

https://doi.org/10.55549/epstem.1412520

Abstract

References

  • Al-Ameen, Z., (2018). An improved contrast equalization technique for contrast enhancement in scanning electron microscopy images, Wiley Periodicals, Inc. Microsc Res Tech.1–11.
  • Amorim, R.C., & Makarenkov, V., (2016). Applying subclustering and Lp distance in weighted K-means with distributed centroids, Neurocomputing, (173), 700–707.
  • Arabi H. & Zaidi H., (2021). Deep learning–based metal artefact reduction in PET/CT imaging, European Radiology, https://doi.org/10.1007/s00330-021-07709-z.
  • Bamberg, F., Dierks, A., Nikolaou, K., Reiser, M.F., Becker, C.R., Johnson T.R., (2011). Metal artefact reduction by dual energy computed tomography using monoenergetic extrapolation, Eur Radiol, 7, 1424-1429.

Metal Artifact Reduction in CT Images through Sinogram Data Inpainting

Year 2023, Volume: 26, 770 - 779, 30.12.2023

Abdessalem Benammar Aicha Allag Imad Araar Ahmed Benyahıa Redouane Draı

https://doi.org/10.55549/epstem.1412520

Abstract

When metallic implants are present within the human body, they frequently introduce metallic artifacts into X-ray CT images. These artifacts can lead to significant distortions, obscuring critical information and potentially degrading the quality of the CT images, thereby impacting diagnostic accuracy for clinicians. In recent years, there has been extensive research aimed at mitigating the challenges posed by metallic artifacts, resulting in the development of multiple solutions to address this issue. In this study, we present an efficient approach for artifact removal. Our method involves utilizing the image reconstructed from a sinogram affected by artifacts to generate a synthesized sinogram, deviating from the conventional acquisition of sinogram data. The key stages of our approach encompass segmentation, sinogram gap-filling, and subsequent image enhancement. To achieve rapid segmentation, we employed a K-means classification method. For the retrieval of missing data, we utilized an interpolation algorithm based on a penalized least squares method. In the final phase of image reconstruction enhancement, we implemented an advanced contrast equalization technique to restore image intensities to their inherent dynamic range. Through rigorous verification using both simulated and clinical data, our method consistently demonstrates a remarkable improvement in image quality.

Keywords

Computed tomography Image reconstruction Metal artifact Segmentation Inpainting sinogram Contrast enhancement

References

  • Al-Ameen, Z., (2018). An improved contrast equalization technique for contrast enhancement in scanning electron microscopy images, Wiley Periodicals, Inc. Microsc Res Tech.1–11.
  • Amorim, R.C., & Makarenkov, V., (2016). Applying subclustering and Lp distance in weighted K-means with distributed centroids, Neurocomputing, (173), 700–707.
  • Arabi H. & Zaidi H., (2021). Deep learning–based metal artefact reduction in PET/CT imaging, European Radiology, https://doi.org/10.1007/s00330-021-07709-z.
  • Bamberg, F., Dierks, A., Nikolaou, K., Reiser, M.F., Becker, C.R., Johnson T.R., (2011). Metal artefact reduction by dual energy computed tomography using monoenergetic extrapolation, Eur Radiol, 7, 1424-1429.
There are 4 citations in total.

Details

Primary Language English
Subjects Environmental and Sustainable Processes
Journal Section Articles
Authors

Abdessalem Benammar

Aicha Allag

Imad Araar

Ahmed Benyahıa

Redouane Draı

Early Pub Date December 31, 2023
Publication Date December 30, 2023
Published in Issue Year 2023 Volume: 26

Cite

APA Benammar, A., Allag, A., Araar, I., … Benyahıa, A. (2023). Metal Artifact Reduction in CT Images through Sinogram Data Inpainting. The Eurasia Proceedings of Science Technology Engineering and Mathematics, 26, 770-779. https://doi.org/10.55549/epstem.1412520
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