[Motion parameter decoupling and motion constraint-driven optimization for correcting rigid motion artifacts in cone-beam computed tomography].

objectivesTo solve the problem of rigid motion artifacts caused by patient movement and gantry vibration during long-duration cone-beam computed tomography (CBCT) using geometric parameter decoupling and motion-constrained optimization.

methodsUsing a motion estimation framework based on 3D-2D rigid registration, the rigid motion parameters were categorized into out-of-plane and in-plane motions, and a stepwise optimization sequence was designed to decouple the mutual interference among the parameters. In response to dynamic evolution of artifact characteristics from multi-contour overlap to edge blurring during the iterative process, a progressive cost function was formulated to facilitate an adaptive transition of the optimization objective from projection data consistency constraints to structural detail recovery. To address the issue of multi-solution in 3D-2D registration and the potential spatial misalignment introduced during iteration, a motion estimation constraint mechanism was incorporated to eliminate global bias, thereby enhancing the algorithm's convergence.

resultsThe proposed algorithm accurately estimated rigid motion trajectories and restored the images via motion compensation. On head simulation data, this model achieved optimal quantitative metrics across 3 motion levels, improved PSNR by 2.1% and SSIM by 2.9%, and reduced RMSE by 6.5% compared to the suboptimal methods. Additional validation on knee simulation and real porcine data further demonstrated its efficacy and generalization capability.

conclusionsThe proposed rigid motion artifact correction algorithm demonstrates good performance in estimating motion trajectories and compensating for image artifacts, thus providing a viable and robust solution for suppressing rigid motion artifacts in clinical CBCT imaging.