A comprehensive dual energy method for CBCT metal artifact reduction

Objective.A major limitation in cone beam CT (CBCT) application is the presence of metal artifacts when scanning metal-embedded objects or high attenuation materials. This study aims to develop a dual-energy based method for effective metal artifact reduction.Approach.The proposed method comprised three steps. Initially, the virtual monoenergetic (VM) projections were generated by combining high- and low-energy projections to mitigate metal artifacts caused by the beam hardening effect. Subsequently, the normalized metal artifact reduction (NMAR) projections were created using the VM projections through the NMAR method. Then, the NMAR CBCT was produced by reintegrating metal into the CBCT reconstructed from NMAR projections. Finally, the iterative reconstruction was employed to obtain the final CBCT, utilizing VM projections and the NMAR CBCT as the initial input. Validation of the proposed method was achieved through Monte Carlo (MC) simulations on digital dental and abdominal phantoms, and CBCT scanning on CIRS Model 062M head and body phantoms. The structural similarity index measurement (SSIM) and the root mean square error (RMSE) calculated within a metal-containing ROI were employed for image quality evaluation.Main Results.Both the MC simulation and phantom scanning demonstrated that the proposed method was superior to the frequency split metal artifact reduction (FSMAR) method in mitigating artifacts and preserving anatomic details around metal. Averaged over four phantoms, the SSIM was enhanced from 99.48% with FSMAR to 99.86% with our proposed method, and the RMSE was reduced from 93.62 HU to 70.75 HU. Furthermore, the proposed method could be implemented with less than two minutes after GPU acceleration.Significance.The proposed dual-energy based metal artifact correction method effectively corrects metal artifacts and preserves tissue details surrounding the metal region by leveraging the strengths of VM, projection interpolation and iterative reconstruction techniques. It has strong potential of clinical implementation due to the superior performance in image quality and process efficiency.