An Adaptive View Selection Algorithm for Large-scale Cone-Beam CT Reconstruction
Industrial cone-beam X-ray computed tomography (CT) produces 3D reconstructions of objects using projection measurements taken at multiple predetermined rotation angles around a single axis. Achieving high-quality reconstructions with traditional analytic reconstruction algorithms typically require...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | deu |
| Published: |
NDT.net
2025-02-01
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| Series: | e-Journal of Nondestructive Testing |
| Online Access: | https://www.ndt.net/search/docs.php3?id=30740 |
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| Summary: | Industrial cone-beam X-ray computed tomography (CT) produces 3D reconstructions of objects using projection measurements taken at multiple predetermined rotation angles around a single axis. Achieving high-quality reconstructions with traditional analytic reconstruction algorithms typically requires a large number of projections, which can be both time-consuming and costly. State-of-the-art reconstruction algorithms, such as model-based iterative reconstruction (MBIR), have made it possible to achieve high-quality reconstructions using significantly fewer projections. However, the process of acquiring these sparse projections often fails to account for the specific geometry of the scanned object. In this paper, we propose an algorithm to optimize the scanning process by using geometric information about the object to be scanned. Our approach strategically selects the most informative views by assessing their alignment with the object’s long edges while ensuring that the selected projections maintain sufficient diversity. To make the algorithm practical for large 3D volumes, we developed a two-stage method. The first stage uses a low-resolution version of the voxelized CAD model to obtain edge information and combines it with view diversity information for the optimization in the initial iterations, while the second stage relies on a single low-resolution reconstruction based on measurements from the first stage to gather edge and view diversity information during the later iterations. During the view selection process, all reconstructions are performed using MBIRJAX, a novel software library that enables fast, high-quality cone-beam CT reconstructions. Through simulations and measured datasets, we demonstrate that our algorithm produces higher quality reconstructions, particularly in preserving sharp edges, while requiring fewer measurements compared to the traditional method.
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| ISSN: | 1435-4934 |