Potential of Metal Artifact Reduction (MAR) and Deep Learning-based Reconstruction (DLR) algorithms integration in CT Metal Artifact Correction: a review
Computed Tomography (CT) is essential for precise medical diagnostics, yet metal implants often induce disruptive image artifacts. Metal Artifact Reduction (MAR) algorithms have emerged to enhance CT image quality by mitigating these artifacts. This review emphasizes the significance of quantifyin...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
Elsevier
2024
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/110682/7/110682_Potential%20of%20Metal%20Artifact%20Reduction%20%28MAR%29.pdf http://irep.iium.edu.my/110682/13/110682_Potential%20of%20Metal%20Artifact%20Reduction%20%28MAR%29_Scopus.pdf http://irep.iium.edu.my/110682/ https://www.sciencedirect.com/science/article/abs/pii/S0969806X24000331?via%3Dihub https://doi.org/10.1016/j.radphyschem.2024.111541 |
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| Summary: | Computed Tomography (CT) is essential for precise medical diagnostics, yet metal implants often induce
disruptive image artifacts. Metal Artifact Reduction (MAR) algorithms have emerged to enhance CT image
quality by mitigating these artifacts. This review emphasizes the significance of quantifying MAR algorithms,
details common quantification metrics, and presents findings from diverse CT scanner studies. MAR techniques
effectively reduce metal artifacts and enhance CT imaging. Metrics like noise levels, Contrast-to-Noise ratio
(CNR), CT number accuracy, and Metal Artifact Index (MAI) quantify their efficacy. Varied CT scanner experiments
with diverse metal implants display improved CT number accuracy, noise reduction, and artifact management
through MAR algorithms. However, secondary artifacts and altered metal size accuracy are potential
drawbacks that need attention. Deep Learning-based Reconstruction (DLR) is an expanding approach using
Artificial Intelligence (AI) for CT image reconstruction. DLR generates low-dose CT images with high spatial
resolution. Recent clinical deployments highlight DLR’s potential in generating low-noise, texture-rich images,
and superior artifact reduction. Moreover, DLR techniques exhibit promise in addressing beam hardening artifacts.
While MAR algorithms have revolutionized CT imaging, DLR techniques are emerging as potential alternatives.
Current DLR implementations like TrueFidelity and Advanced Intelligent Clear-IQ Engine (AiCE)
demonstrate promising outcomes. However, challenges in implementation and machine learning model reliability
require further exploration. In conclusion, MAR algorithms enhance CT imaging quality by rectifying
artifacts near metal implants, while DLR methods offer a promising path for radiation dose reduction and image
refinement. Combining both approaches might pave the way for future CT imaging advancements. |
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