F2IFlow for CT Metal Artifact Reduction

Computed Tomography (CT) has been extensively utilized for medical diagnosis, assessment, as well as treatment planning and guidance. However, the image quality will be significantly compromised when metallic implants are present in patients' bodies, consequently affecting the clinical diagnosis or radiation therapy dose calculation. Previous Metal Artifact Reduction (MAR) methods either require prior knowledge about metallic implants or exhibit modeling bias in the mechanism of artifact formation, which restricts the capability to acquire high-quality CT images and increases the complexity of practical applications. In this paper, we propose a novel MAR method based on a feature-to-image conditional normalization flow, named F2IFlow, to address the problem. Specifically, we initially design an inherent feature extraction to get the inherent anatomical features of CT images. Then, a feature-to-image flow module is used for completing the metal-artifact-free CT images progressively through a series of reversible transformations. Incorporating these designs into F2IFlow, the coarse-to-fine strategy equips our model with the capability to deliver exceptional performance. Experimental results on both simulated and clinical datasets demonstrate that our method achieves superior performance in both quantitative and qualitative outcomes, exhibiting better visual effects in terms of artifact reduction and image fidelity.