Feature-based CBCT self-calibration for arbitrary trajectories

• Verfasst von: Tönnes, Christian [VerfasserIn]
• Verfasst von: Russ, Tom [VerfasserIn]
• Verfasst von: Schad, Lothar R. [VerfasserIn]
• Verfasst von: Zöllner, Frank G. [VerfasserIn]
• Titel: Feature-based CBCT self-calibration for arbitrary trajectories
• Verf.angabe: Christian Tönnes, Tom Russ, Lothar R. Schad, Frank G. Zöllner
• E-Jahr: 2022
• Jahr: 20 May 2022
• Umfang: 9 S.
• Illustrationen: Illustrationen
• Fussnoten: Gesehen am 31.07.2023
• Titel Quelle: Enthalten in: International journal of computer assisted radiology and surgery
• Ort Quelle: Berlin : Springer, 2006
• Jahr Quelle: 2022
• Band/Heft Quelle: 17(2022), 11 vom: Nov., Seite 2151-2159
• ISSN Quelle: 1861-6429
• DOI: doi:10.1007/s11548-022-02645-9
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Alignment
• Sach-SW: Calibration
• Sach-SW: CBCT
• Sach-SW: Minimizer
• Sach-SW: Registration
• K10plus-PPN: 1853912433
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext

Abstract

Purpose: Development of an algorithm to self-calibrate arbitrary CBCT trajectories which can be used to reduce metal artifacts. By using feature detection and matching we want to reduce the amount of parameters for the BFGS optimization and thus reduce the runtime. Methods: Each projection is 2D-3D registered on a prior image with AKAZE feature detection and brute force matching. Translational misalignment is calculated directly from the misalignment of feature positions, rotations are aligned using a minimization algorithm that fits a quartic function and determines the minimum of this function. Evaluation: We did three experiments to compare how well the algorithm can handle noise on the different degrees of freedom. Our algorithms are compared to Broyden–Fletcher–Goldfarb–Shanno (BFGS) minimizer with Normalized Gradient Information (NGI) objective function, and BFGS with distance between features objective function using SSIM, nRMSE, and the Dice coefficient of segmented metal object. Results: Our algorithm (Feature ORiented Calibration for Arbitrary Scan Trajectories with Enhanced Reliability (FORCASTER)) performs on par with the state-of-the-art algorithms (BFGS with NGI objective). nRMSE: FORCASTER = 0.3390, BFGS+NGI = 0.3441; SSIM: FORCASTER = 0.83, BFGS + NGI = 0.79; Dice: FORCASTER = 0.86, BFGS + NGI = 0.87. Conclusion: The proposed algorithm can determine the parameters of the projection orientations for arbitrary trajectories with calibration quality comparable to state-of-the-art algorithms, but faster and with higher tolerance to errors in the initially guessed parameters.