Data for the Paper: "Feature-Oriented CBCT Self-Calibration Parameter Estimator for Arbitrary Trajectories: FORCAST-EST"
https://doi.org/10.3390/app13169179
Abstract:
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.
