Cover Image

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...

Full description

Saved in:
Bibliographic Details
Main Authors: Mohammed Musta, Njiti, Osman, Noor Diyana, Mansor, Mohd Syahir, Rabaiee, Nor Ain, Abdul Aziz, Mohd Zahri
Format: Article
Language:English
English
Published: Elsevier 2024
Subjects:
QC Physics
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
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.iium.irep.110682
record_format dspace
spelling my.iium.irep.1106822024-02-20T04:36:59Z http://irep.iium.edu.my/110682/ Potential of Metal Artifact Reduction (MAR) and Deep Learning-based Reconstruction (DLR) algorithms integration in CT Metal Artifact Correction: a review Mohammed Musta, Njiti Osman, Noor Diyana Mansor, Mohd Syahir Rabaiee, Nor Ain Abdul Aziz, Mohd Zahri QC Physics 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. Elsevier 2024-01-23 Article PeerReviewed application/pdf en http://irep.iium.edu.my/110682/7/110682_Potential%20of%20Metal%20Artifact%20Reduction%20%28MAR%29.pdf application/pdf en http://irep.iium.edu.my/110682/13/110682_Potential%20of%20Metal%20Artifact%20Reduction%20%28MAR%29_Scopus.pdf Mohammed Musta, Njiti and Osman, Noor Diyana and Mansor, Mohd Syahir and Rabaiee, Nor Ain and Abdul Aziz, Mohd Zahri (2024) Potential of Metal Artifact Reduction (MAR) and Deep Learning-based Reconstruction (DLR) algorithms integration in CT Metal Artifact Correction: a review. Radiation Physics and Chemistry, 218. pp. 1-15. ISSN 0969-806X E-ISSN 1879-0895 https://www.sciencedirect.com/science/article/abs/pii/S0969806X24000331?via%3Dihub https://doi.org/10.1016/j.radphyschem.2024.111541
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
topic QC Physics
spellingShingle QC Physics
Mohammed Musta, Njiti
Osman, Noor Diyana
Mansor, Mohd Syahir
Rabaiee, Nor Ain
Abdul Aziz, Mohd Zahri
Potential of Metal Artifact Reduction (MAR) and Deep Learning-based Reconstruction (DLR) algorithms integration in CT Metal Artifact Correction: a review
description 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.
format Article
author Mohammed Musta, Njiti
Osman, Noor Diyana
Mansor, Mohd Syahir
Rabaiee, Nor Ain
Abdul Aziz, Mohd Zahri
author_facet Mohammed Musta, Njiti
Osman, Noor Diyana
Mansor, Mohd Syahir
Rabaiee, Nor Ain
Abdul Aziz, Mohd Zahri
author_sort Mohammed Musta, Njiti
title Potential of Metal Artifact Reduction (MAR) and Deep Learning-based Reconstruction (DLR) algorithms integration in CT Metal Artifact Correction: a review
title_short Potential of Metal Artifact Reduction (MAR) and Deep Learning-based Reconstruction (DLR) algorithms integration in CT Metal Artifact Correction: a review
title_full Potential of Metal Artifact Reduction (MAR) and Deep Learning-based Reconstruction (DLR) algorithms integration in CT Metal Artifact Correction: a review
title_fullStr Potential of Metal Artifact Reduction (MAR) and Deep Learning-based Reconstruction (DLR) algorithms integration in CT Metal Artifact Correction: a review
title_full_unstemmed Potential of Metal Artifact Reduction (MAR) and Deep Learning-based Reconstruction (DLR) algorithms integration in CT Metal Artifact Correction: a review
title_sort potential of metal artifact reduction (mar) and deep learning-based reconstruction (dlr) algorithms integration in ct metal artifact correction: a review
publisher Elsevier
publishDate 2024
url 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
_version_ 1792146442468458496
score 13.211869

Similar Items