Methods Research on Correction for Intracranial Metal Artifacts Based on Prior Image Utilization
Objective: A metal artifact correction algorithm based on prior image is proposed to suppress the effect of intracranial metal artifacts on postoperative review. Method: The computed tomography (CT) image compensating for the intracranial metal artifact is obtained by adaptive filtering, threshold s...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Editorial Office of Computerized Tomography Theory and Application
2025-01-01
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| Series: | CT Lilun yu yingyong yanjiu |
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| Online Access: | https://www.cttacn.org.cn/cn/article/doi/10.15953/j.ctta.2024.002 |
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| _version_ | 1832592373203337216 |
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| author | Zonggui CHEN Ningning WEI Zhoufan LI Guihong PAN Minjiang HUANG |
| author_facet | Zonggui CHEN Ningning WEI Zhoufan LI Guihong PAN Minjiang HUANG |
| author_sort | Zonggui CHEN |
| collection | DOAJ |
| description | Objective: A metal artifact correction algorithm based on prior image is proposed to suppress the effect of intracranial metal artifacts on postoperative review. Method: The computed tomography (CT) image compensating for the intracranial metal artifact is obtained by adaptive filtering, threshold segmentation, and K mean clustering. In projecting the trajectory of metallic matter, the interpolation correction of the original image’s projection data is obtained using the prior image as a reference, and the image is fused with the metal-containing material only image to produce the final corrected image. To verify the efficacy of this algorithm in suppressing CT metal artifacts, artifact correction was performed using CT images of simulated isolated metal artifacts and intracranial metal artifacts. Results: Compared with linear interpolation and the ADN algorithms, this algorithm achieves the highest structure similarity and peak signal-to-noise ratio and lowest root mean square error in corrected images. Two senior doctors subjectively scored this algorithm higher than the linear interpolation and ADN algorithms for removing CT metal artifacts. Conclusion: This algorithm effectively suppresses metal artifacts while preserving image edge information. |
| format | Article |
| id | doaj-art-ad315570da504c389c013f0fcb5d9802 |
| institution | Kabale University |
| issn | 1004-4140 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Editorial Office of Computerized Tomography Theory and Application |
| record_format | Article |
| series | CT Lilun yu yingyong yanjiu |
| spelling | doaj-art-ad315570da504c389c013f0fcb5d98022025-01-21T09:14:43ZengEditorial Office of Computerized Tomography Theory and ApplicationCT Lilun yu yingyong yanjiu1004-41402025-01-0134114114810.15953/j.ctta.2024.0022024.002Methods Research on Correction for Intracranial Metal Artifacts Based on Prior Image UtilizationZonggui CHEN0Ningning WEI1Zhoufan LI2Guihong PAN3Minjiang HUANG4College of Medical, Hunan University of Medicine, Huaihua 418000, ChinaCollege of Medical, Hunan University of Medicine, Huaihua 418000, ChinaCollege of Medical, Hunan University of Medicine, Huaihua 418000, ChinaCollege of Medical, Hunan University of Medicine, Huaihua 418000, ChinaCollege of Medical, Hunan University of Medicine, Huaihua 418000, ChinaObjective: A metal artifact correction algorithm based on prior image is proposed to suppress the effect of intracranial metal artifacts on postoperative review. Method: The computed tomography (CT) image compensating for the intracranial metal artifact is obtained by adaptive filtering, threshold segmentation, and K mean clustering. In projecting the trajectory of metallic matter, the interpolation correction of the original image’s projection data is obtained using the prior image as a reference, and the image is fused with the metal-containing material only image to produce the final corrected image. To verify the efficacy of this algorithm in suppressing CT metal artifacts, artifact correction was performed using CT images of simulated isolated metal artifacts and intracranial metal artifacts. Results: Compared with linear interpolation and the ADN algorithms, this algorithm achieves the highest structure similarity and peak signal-to-noise ratio and lowest root mean square error in corrected images. Two senior doctors subjectively scored this algorithm higher than the linear interpolation and ADN algorithms for removing CT metal artifacts. Conclusion: This algorithm effectively suppresses metal artifacts while preserving image edge information.https://www.cttacn.org.cn/cn/article/doi/10.15953/j.ctta.2024.002ct metal artifact simulationprior imagemetal artifactadaptive filter |
| spellingShingle | Zonggui CHEN Ningning WEI Zhoufan LI Guihong PAN Minjiang HUANG Methods Research on Correction for Intracranial Metal Artifacts Based on Prior Image Utilization CT Lilun yu yingyong yanjiu ct metal artifact simulation prior image metal artifact adaptive filter |
| title | Methods Research on Correction for Intracranial Metal Artifacts Based on Prior Image Utilization |
| title_full | Methods Research on Correction for Intracranial Metal Artifacts Based on Prior Image Utilization |
| title_fullStr | Methods Research on Correction for Intracranial Metal Artifacts Based on Prior Image Utilization |
| title_full_unstemmed | Methods Research on Correction for Intracranial Metal Artifacts Based on Prior Image Utilization |
| title_short | Methods Research on Correction for Intracranial Metal Artifacts Based on Prior Image Utilization |
| title_sort | methods research on correction for intracranial metal artifacts based on prior image utilization |
| topic | ct metal artifact simulation prior image metal artifact adaptive filter |
| url | https://www.cttacn.org.cn/cn/article/doi/10.15953/j.ctta.2024.002 |
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