Model-based iterative reconstruction with adaptive regularization for artifact reduction in electron tomography
Abstract Obtaining high-quality 3D reconstructions from electron tomography of crystalline particles embedded in lighter support elements is crucial for various material systems such as catalysts for fuel cell applications. However, significant challenges arise due to the limited tilt range, sparse...
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-86639-y |
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| author | Singanallur Venkatakrishnan Obaidullah Rahman Lynda Amichi Jose D. Arregui-Mena Haoran Yu David A. Cullen Amirkoushyar Ziabari |
| author_facet | Singanallur Venkatakrishnan Obaidullah Rahman Lynda Amichi Jose D. Arregui-Mena Haoran Yu David A. Cullen Amirkoushyar Ziabari |
| author_sort | Singanallur Venkatakrishnan |
| collection | DOAJ |
| description | Abstract Obtaining high-quality 3D reconstructions from electron tomography of crystalline particles embedded in lighter support elements is crucial for various material systems such as catalysts for fuel cell applications. However, significant challenges arise due to the limited tilt range, sparse and low signal-to-noise ratio of the measurements. In addition, small metal particles can cause strong streaking and shading artifacts in the 3D reconstructions when using conventional reconstruction algorithms due to the presence of Bragg diffraction and the large scattering cross-section difference between the materials of the particles and the background support regions. These artifacts lead to errors in the downstream characterization affecting extraction of critical features such as the size of the metal particles, their distribution and the volume of the lighter support regions. In this paper, we present a two-stage algorithm based on metal artifact reduction, utilizing model-based iterative reconstruction methods with adaptive adjustment of regularization parameters. Our approach yields high-quality 3D reconstructions compared to traditional algorithms, accurately capturing both the metal particles as well as the background support. We demonstrate the effectiveness of our algorithm through simulated and experimental bright-field electron tomography data, showing significant improvements in reconstruction quality compared to traditional methods. |
| format | Article |
| id | doaj-art-5015b8e7c82f4b2f877a06c4dae5f5ee |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-5015b8e7c82f4b2f877a06c4dae5f5ee2025-08-20T02:15:11ZengNature PortfolioScientific Reports2045-23222025-02-0115111210.1038/s41598-025-86639-yModel-based iterative reconstruction with adaptive regularization for artifact reduction in electron tomographySinganallur Venkatakrishnan0Obaidullah Rahman1Lynda Amichi2Jose D. Arregui-Mena3Haoran Yu4David A. Cullen5Amirkoushyar Ziabari6Multi-modal Sensor Analytics Group, Oak Ridge National LaboratoryMulti-modal Sensor Analytics Group, Oak Ridge National LaboratoryElectron Microscopy and Microanalysis Group, Oak Ridge National LaboratoryNuclear Energy Materials Microanalysis Group, Oak Ridge National LaboratoryElectron Microscopy and Microanalysis Group, Oak Ridge National LaboratoryElectron Microscopy and Microanalysis Group, Oak Ridge National LaboratoryMulti-modal Sensor Analytics Group, Oak Ridge National LaboratoryAbstract Obtaining high-quality 3D reconstructions from electron tomography of crystalline particles embedded in lighter support elements is crucial for various material systems such as catalysts for fuel cell applications. However, significant challenges arise due to the limited tilt range, sparse and low signal-to-noise ratio of the measurements. In addition, small metal particles can cause strong streaking and shading artifacts in the 3D reconstructions when using conventional reconstruction algorithms due to the presence of Bragg diffraction and the large scattering cross-section difference between the materials of the particles and the background support regions. These artifacts lead to errors in the downstream characterization affecting extraction of critical features such as the size of the metal particles, their distribution and the volume of the lighter support regions. In this paper, we present a two-stage algorithm based on metal artifact reduction, utilizing model-based iterative reconstruction methods with adaptive adjustment of regularization parameters. Our approach yields high-quality 3D reconstructions compared to traditional algorithms, accurately capturing both the metal particles as well as the background support. We demonstrate the effectiveness of our algorithm through simulated and experimental bright-field electron tomography data, showing significant improvements in reconstruction quality compared to traditional methods.https://doi.org/10.1038/s41598-025-86639-yElectron tomographyDiffractionModel-based reconstructionArtifact reduction |
| spellingShingle | Singanallur Venkatakrishnan Obaidullah Rahman Lynda Amichi Jose D. Arregui-Mena Haoran Yu David A. Cullen Amirkoushyar Ziabari Model-based iterative reconstruction with adaptive regularization for artifact reduction in electron tomography Scientific Reports Electron tomography Diffraction Model-based reconstruction Artifact reduction |
| title | Model-based iterative reconstruction with adaptive regularization for artifact reduction in electron tomography |
| title_full | Model-based iterative reconstruction with adaptive regularization for artifact reduction in electron tomography |
| title_fullStr | Model-based iterative reconstruction with adaptive regularization for artifact reduction in electron tomography |
| title_full_unstemmed | Model-based iterative reconstruction with adaptive regularization for artifact reduction in electron tomography |
| title_short | Model-based iterative reconstruction with adaptive regularization for artifact reduction in electron tomography |
| title_sort | model based iterative reconstruction with adaptive regularization for artifact reduction in electron tomography |
| topic | Electron tomography Diffraction Model-based reconstruction Artifact reduction |
| url | https://doi.org/10.1038/s41598-025-86639-y |
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