# Feature-Oriented CBCT Self-Calibration Parameter Estimator for Arbitrary Trajectories: FORCAST-EST
## 3 Results
Values marked with an \({}^{\star}\) had significant (\(p<0.05\)) changes compared to the refined estimate. Figure 8: Example projections at a point where the parameter estimation fails. From left to right is as follows: acquired projection, simulated projection from the calibration with starting parameters, simulated projection from the calibration with estimated starting parameters and FORCASTER, and the projections from estimated parameters with CMA-ES calibration. Figure 7: Two slices out of the sinogram of the sinusoidal trajectory from the acquired data. The FORCASTER calibration used nearly accurate starting parameters. The calibration with refined estimates and FORCASTER, as well as the refined estimates with CMA-ES and NGI objective are shown. The left column (**a**) is ordered from top to bottom as follows: acquired data, simulated projections from the calibration with starting parameters, simulated projections from the FORCASTER calibration with estimated starting parameters, and CMA-ES calibration with the estimated parameters. The right columns (**b**) have the same order, also from left to right. The values obtained from evaluating the metrics are shown in Table 3. Similar to the calibration of the CBCT trajectory, the metrics improved with every step. The final calibration produced lower results than the CBCT calibration, which is in line with the distortions visible in the sinogram. The runtime was much shorter because the sinusoidal trajectory contained only 161 projections in comparison to the 496 contained in the CBCT trajectory. The results for the continuous arc trajectory are shown in Table 4. Because there was no correct calibration for this trajectory (since there were no starting parameters for the individual projections), the metrics were not evaluated on the simulated projections but were instead evaluated on the acquired images. The metric values were much lower there than for the ones for the two previous trajectories. Still, they significantly improved with each step, and there were no significant differences in the SSIM (\(p=0.5\)) and NRMSE (\(p=0.9\)) metrics between the two state-of-the-art algorithms. For comparison, the calibrations for the other two trajectories on the acquired images were evaluated and are included in the results located in the lower part of the table. In Figure 9, the acquired sinogram and the simulated sinogram that used the estimated parameters, which was also calibrated by the CMA-ES with the NGI objective, are shown. Similar to the sinusoidal trajectory, there were some projections where the estimated parameters were wrong. These can be seen in the lower half of column (a), and on the right of column (b). Apart from these few slices, no major misalignment can be seen. The reconstructed image that used the continuous arc and the calibration is in Figure 10. The image was reconstructed using the FDK algorithm from the astra toolbox without any postprocessing. The images show that the object was reconstructed with just a few artifacts (which came from miscalibration). The results of the metrics are in Table 5. Since there exists no correct calibration and reconstruction of this set of projections, the image was compared with the image obtained by reconstructing the CBCT trajectory. The NGI, SSIM, and NRMSE metrics all increased significantly with each step. There was no significant increase for the Dice value in the reconstruction with refined estimates and the reconstruction after applying the two calibration algorithms. Still, the high Dice score shows that the metal object was reconstructed with a good quality and at the correct position. The SSIM and