Development of Residual Dense U-Net (RDU-Net)-Based Metal Artefacts Reduction Technique Using Spectral Photon Counting CT
Metal induced artefacts in computed tomography (CT) images are primarily caused by beam hardening, scatter effects, and photon starvation. These artefacts impede the characterization of fine anatomical structures and compromise the diagnostic value of the CT images. We aim to develop an innovative m...
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2024-01-01
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| author | Osama Khan Briya Tariq Nadine Francis Nabil Maalej Abderaouf Behouch Amer Kashif Asim Waris Aamir Raja |
| author_facet | Osama Khan Briya Tariq Nadine Francis Nabil Maalej Abderaouf Behouch Amer Kashif Asim Waris Aamir Raja |
| author_sort | Osama Khan |
| collection | DOAJ |
| description | Metal induced artefacts in computed tomography (CT) images are primarily caused by beam hardening, scatter effects, and photon starvation. These artefacts impede the characterization of fine anatomical structures and compromise the diagnostic value of the CT images. We aim to develop an innovative machine learning-based technique called residual dense U-Net (RDU-Net), specifically for spectral photon-counting CT (SPCCT), to mitigate metal artefacts across all energy bins. The proposed model was quantitatively evaluated, with and without the metal artefact reduction (MAR) algorithm, using line profiles, histogram analysis, signal-to-noise ratio (SNR), root mean squared error (RMSE), and structural similarity index measure (SSIM). The results show significant improvements with the average SNR increasing from 3.37 to 17.40 across the five energy bins after the application of the MAR algorithm. The average RMSE decreased from 0.016 to 0.001, and the average SSIM increased by 34.9%. The study also evaluated material density images of hydroxyapatite (HA) and iodine, with and without the MAR algorithm, using the receiver operating characteristic (ROC) paradigm. The results show improved accuracy in the material identification for HA (86% to 91%) and iodine (84% to 93%) after MAR. Overall, the evaluation of the model show promising results and the potential to significantly decrease the metal artefacts in all the parameters used in the energy analysis at p < 0.0001, while preserving the attenuation profile of SPCCT images. |
| format | Article |
| id | doaj-art-0e4116dc1efa4e30aa94fddefd22ac6c |
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| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-0e4116dc1efa4e30aa94fddefd22ac6c2025-08-20T01:58:12ZengIEEEIEEE Access2169-35362024-01-011210973510974910.1109/ACCESS.2024.343986110630472Development of Residual Dense U-Net (RDU-Net)-Based Metal Artefacts Reduction Technique Using Spectral Photon Counting CTOsama Khan0https://orcid.org/0009-0009-2233-0856Briya Tariq1https://orcid.org/0009-0006-7881-4399Nadine Francis2https://orcid.org/0009-0005-6859-0144Nabil Maalej3https://orcid.org/0000-0002-6633-6223Abderaouf Behouch4Amer Kashif5Asim Waris6https://orcid.org/0000-0002-0190-0700Aamir Raja7https://orcid.org/0000-0002-0040-1723Department of Physics, Khalifa University, Abu Dhabi, United Arab EmiratesDepartment of Physics, Khalifa University, Abu Dhabi, United Arab EmiratesDepartment of Physics, Khalifa University, Abu Dhabi, United Arab EmiratesDepartment of Physics, Khalifa University, Abu Dhabi, United Arab EmiratesDepartment of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi, United Arab EmiratesDepartment of Biomedical Engineering and Sciences, National University of Sciences & Technology, Islamabad, PakistanDepartment of Biomedical Engineering and Sciences, National University of Sciences & Technology, Islamabad, PakistanDepartment of Physics, Khalifa University, Abu Dhabi, United Arab EmiratesMetal induced artefacts in computed tomography (CT) images are primarily caused by beam hardening, scatter effects, and photon starvation. These artefacts impede the characterization of fine anatomical structures and compromise the diagnostic value of the CT images. We aim to develop an innovative machine learning-based technique called residual dense U-Net (RDU-Net), specifically for spectral photon-counting CT (SPCCT), to mitigate metal artefacts across all energy bins. The proposed model was quantitatively evaluated, with and without the metal artefact reduction (MAR) algorithm, using line profiles, histogram analysis, signal-to-noise ratio (SNR), root mean squared error (RMSE), and structural similarity index measure (SSIM). The results show significant improvements with the average SNR increasing from 3.37 to 17.40 across the five energy bins after the application of the MAR algorithm. The average RMSE decreased from 0.016 to 0.001, and the average SSIM increased by 34.9%. The study also evaluated material density images of hydroxyapatite (HA) and iodine, with and without the MAR algorithm, using the receiver operating characteristic (ROC) paradigm. The results show improved accuracy in the material identification for HA (86% to 91%) and iodine (84% to 93%) after MAR. Overall, the evaluation of the model show promising results and the potential to significantly decrease the metal artefacts in all the parameters used in the energy analysis at p < 0.0001, while preserving the attenuation profile of SPCCT images.https://ieeexplore.ieee.org/document/10630472/Computed tomography (CT)metal artefacts reduction (MAR)spectral photon-counting CT (SPCCT) |
| spellingShingle | Osama Khan Briya Tariq Nadine Francis Nabil Maalej Abderaouf Behouch Amer Kashif Asim Waris Aamir Raja Development of Residual Dense U-Net (RDU-Net)-Based Metal Artefacts Reduction Technique Using Spectral Photon Counting CT IEEE Access Computed tomography (CT) metal artefacts reduction (MAR) spectral photon-counting CT (SPCCT) |
| title | Development of Residual Dense U-Net (RDU-Net)-Based Metal Artefacts Reduction Technique Using Spectral Photon Counting CT |
| title_full | Development of Residual Dense U-Net (RDU-Net)-Based Metal Artefacts Reduction Technique Using Spectral Photon Counting CT |
| title_fullStr | Development of Residual Dense U-Net (RDU-Net)-Based Metal Artefacts Reduction Technique Using Spectral Photon Counting CT |
| title_full_unstemmed | Development of Residual Dense U-Net (RDU-Net)-Based Metal Artefacts Reduction Technique Using Spectral Photon Counting CT |
| title_short | Development of Residual Dense U-Net (RDU-Net)-Based Metal Artefacts Reduction Technique Using Spectral Photon Counting CT |
| title_sort | development of residual dense u net rdu net based metal artefacts reduction technique using spectral photon counting ct |
| topic | Computed tomography (CT) metal artefacts reduction (MAR) spectral photon-counting CT (SPCCT) |
| url | https://ieeexplore.ieee.org/document/10630472/ |
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