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

Wencheng Han*1, Dongqian Guo*1, Xiao Chen2, Pang Lyu3, Yi Jin2, Jianbing Shen†1,
1University of Macau, Macau, 2Department of Orthopedics, People's Hospital of Zhengzhou University, Henan Provincial People's Hospital, 3Zhongshan Hospital, Fudan University, Shanghai, China
* Equal contribution, † Corresponding author
arXiv Code

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.

Method

CT Denoising Method
Figure.1 Comparison of Artifacts Reduction Pipelines. (a) Most previous methods rely on synthetic artifact data derived from clean CT sequences of patients without implants. Additionally, many methods use image generation algorithms, which may introduce extraneous information, potentially compromising the reliability of the resulting CT sequences. (b) In contrast, our method utilizes real artifact CT pairs for training, effectively bridging the domain gap. Our approach employs a representation alignment algorithm, maintaining information consistency. (c) We provide a comparison of inference results between our method and previous methods to illustrate the effectiveness of our approach.
CT Denoising Method
Figure.2 Illustration of the Proposed Latent Space Alignment Framework. The pipeline consists of four stages: Data Processing, VAE encoding, Latent Space Alignment, and VAE decoding.

Visualization

CT Denoising Method
Figure.3 Qualitative Comparisons. (a) Comparison on the Test Set: Images of patients with hip prostheses used in total hip arthroplasty, fracture internal fixation, and spinal internal fixation. (b) Comparison on the Generalization Set: Evaluation on data from unseen CT machines (SpineWeb dataset, Siemens, Philips, and UIH CT machines) to demonstrate generalization.