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arXiv · 2503.21259

Reducing CT Metal Artifacts by Learning Latent Space Alignment with Gemstone Spectral Imaging Data

Wencheng Han·Dongqian Guo·Xiao Chen·Pang Lyu·Yi Jin·Jianbing Shen
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Abstract

Metal artifacts in CT slices have long posed challenges in medical diagnostics. These artifacts degrade image quality, resulting in suboptimal visualization and complicating the accurate interpretation of tissues adjacent to metal implants. To address these issues, we introduce the Latent Gemstone Spectral Imaging (GSI) Alignment Framework, which effectively reduces metal artifacts while avoiding the introduction of noise information. Our work is based on a key finding that even artifact-affected ordinary CT sequences contain sufficient information to discern detailed structures. The challenge lies in the inability to clearly represent this information. To address this issue, we developed an Alignment Framework that adjusts the representation of ordinary CT images to match GSI CT sequences. GSI is an advanced imaging technique using multiple energy levels to mitigate artifacts caused by metal implants. By aligning the representation to GSI data, we can effectively suppress metal artifacts while clearly revealing detailed structure, without introducing extraneous information into CT sequences. To facilitate the application, we propose a new dataset, Artifacts-GSI, captured from real patients with metal implants, and establish a new benchmark based on this dataset. Experimental results show that our method significantly reduces metal artifacts and greatly enhances the readability of CT slices. All our code and data are available at: https://um-lab.github.io/GSI-MAR/

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Wencheng Han, Dongqian Guo, Xiao Chen, Pang Lyu, Yi Jin, Jianbing Shen. 2025-03-27. Reducing CT Metal Artifacts by Learning Latent Space Alignment with Gemstone Spectral Imaging Data. https://arxiv.org/abs/2503.21259

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