Feature-oriented CBCT self-calibration parameter estimator for arbitrary trajectories: FORCAST-EST
Background: For the reconstruction of Cone-Beam CT volumes, the exact position of each projection is needed; however, in some situations, this information is missing. Purpose: The development of a self-calibration algorithm for arbitrary CBCT trajectories that does not need initial positions. Method...
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| Hauptverfasser: | , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
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11 August 2023
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| In: |
Applied Sciences
Year: 2023, Jahrgang: 13, Heft: 16, Pages: 1-14 |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app13169179 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.3390/app13169179 Verlag, kostenfrei, Volltext: https://www.mdpi.com/2076-3417/13/16/9179 
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| Verfasserangaben: | Christian Tönnes, Frank G. Zöllner |
MARC
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| 245 | 1 | 0 | |a Feature-oriented CBCT self-calibration parameter estimator for arbitrary trajectories |b FORCAST-EST |c Christian Tönnes, Frank G. Zöllner |
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| 520 | |a Background: For the reconstruction of Cone-Beam CT volumes, the exact position of each projection is needed; however, in some situations, this information is missing. Purpose: The development of a self-calibration algorithm for arbitrary CBCT trajectories that does not need initial positions. Methods: Projections are simulated in a spherical grid around the center of rotation. Through using feature detection and matching, an acquired projection is compared to each simulated image in this grid. The position with the most matched features was used as a starting point for a fine calibration with a state-of-the-art algorithm. Evaluation: This approach is compared with the calibration of nearly correct starting positions when using FORCASTER and CMA-ES minimization with a normalized gradient information (NGI) objective function. The comparison metrics were the normalized root mean squared error, structural similarity index, and the dice coefficient, which were evaluated on the segmentation of a metal object. Results: The parameter estimation for a regular Cone-Beam CT with a 496 projection took 1:26 h with the following metric values: NRMSE = 0.0669; SSIM = 0.992; NGI = 0.75; and Dice = 0.96. FORCASTER with parameter estimation took 3:28 h with the following metrics: NRMSE = 0.0190; SSIM = 0.999; NGI = 0.92; and Dice = 0.99. CMA-ES with parameter estimation took 5:39 h with the following metrics: NRMSE = 0.0037; SSIM = 1.0; NGI = 0.98; and Dice = 1.0. Conclusions: The proposed algorithm can determine the parameters of the projection orientations for arbitrary trajectories with enough accuracy to reconstruct a 3D volume with low errors. | ||
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| 650 | 4 | |a CBCT | |
| 650 | 4 | |a computed tomography | |
| 650 | 4 | |a cone-beam CT | |
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