A synergistic framework for geometric calibration and reconstruction in dual-arm robotic cone-beam computed...

Dual-arm robotic cone-beam computed tomography (CBCT) systems are susceptible to geometric instability due to their decoupled kinematics, which can limit their practical application.

This study aims to develop and evaluate a synergistic framework for improving geometric calibration and reconstruction consistency in flexible dual-arm robotic CBCT platforms.

We introduce the Synergistic Calibration and Reconstruction (SyCaR) framework. It uses a two-stage geometric calibration strategy that combines real-time external metrology for projection-wise pose estimation with an optional reference-free projection-consistency refinement for residual geometric correction. For reconstruction, we developed a Pose-Driven Feldkamp-Davis-Kress (PD-FDK) algorithm that operates directly on per-projection pose data to account for detector misalignments and non-ideal source-detector trajectory deviations.

Physical phantom experiments showed that encoder geometry produced severe streaking artifacts and structural inconsistency, whereas motion-capture geometry restored clearer phantom structures. In the numerical study, PD-FDK achieved the best PSNR and RMSE among the evaluated analytical methods, improving from 28.99 dB and 0.036 with standard FDK to 32.72 dB and 0.023. Across numerical and physical experiments, PD-FDK reduced geometry-related artifacts and showed consistent performance among FDK type methods under the tested conditions.

The proposed SyCaR framework provides a feasible computational approach for mitigating geometric instability in dual-arm robotic CBCT. The combination of geometric calibration and PD-FDK reconstruction improved reconstruction consistency and reduced geometry-related artifacts under the evaluated numerical and physical settings, while retaining the computational efficiency of analytical reconstruction. This work supports the further development of flexible dual-arm robotic CBCT for biomedical imaging applications, although broader validation with more diverse trajectories, objects, and acquisition conditions remains necessary.