Navigation überspringen
Universitätsbibliothek Heidelberg
Standort: ---
Exemplare: ---
heiBIB
 Online-Ressource
Verfasst von:Tönnes, Christian [VerfasserIn]   i
 Russ, Tom [VerfasserIn]   i
 Schad, Lothar R. [VerfasserIn]   i
 Zöllner, Frank G. [VerfasserIn]   i
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
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.
DOI:doi:10.1007/s11548-022-02645-9
URL:kostenfrei: Volltext: https://doi.org/10.1007/s11548-022-02645-9
 kostenfrei: Volltext: https://link.springer.com/article/10.1007/s11548-022-02645-9
 DOI: https://doi.org/10.1007/s11548-022-02645-9
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:Alignment
 Calibration
 CBCT
 Minimizer
 Registration
K10plus-PPN:1853912433
Verknüpfungen:→ Zeitschrift
 
 
Lokale URL UB: Zum Volltext

Permanenter Link auf diesen Titel (bookmarkfähig):  https://katalog.ub.uni-heidelberg.de/titel/69102633   QR-Code
zum Seitenanfang