Joint Estimation of Anatomy and Implants in X-ray CT using a Mixed Prior Model.

Medical implants are often made of dense materials and pose great challenges to accurate CT reconstruction and visualization, especially in regions close to or surrounding implants. Moreover, it is common that diagnostics involving implanted patients require distinct visualization strategies for implants and anatomy indvidually. In this work, we propose a novel approach for joint estimation of anatomy and implants as separate image volumes using a mixed prior model. This prior model leverages a learning-based diffusion prior for the anatomy image and a simple 0-norm sparsity prior for implants to decouple the two volumes. Additionally, a hybrid mono-polyenergetic forward model is employed to effectively accommodate the spectral effects of implants. The proposed reconstruction process alternates between two steps: Diffusion posterior sampling is used to update the anatomy image, and classic optimization updates to the implant image and associated spectral coefficients. Evaluation in spine imaging with metal pedicle screw implants demonstrates that the proposed algorithm can achieve accurate decompositions. Moreover, anatomy reconstruction between the two pedicle screws, an area where all competing algorithms typically fail, is successful in visualizing details. The proposed algorithm also effectively avoids streaking and beam hardening artifacts in soft tissue, achieving 15.25% higher PSNR and 24.29% higher SSIM compared to normalized metal artifacts reduction (NMAR). These results suggest that mixed prior models can help to separate spatially and spectrally distinct objects that differ from standard anatomical features in ordinary single-energy CT to not only improve image quality but to enhance visualization of the two distinct image volumes.