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

arXiv: https://arxiv.org/abs/2503.21259 · HTML: https://mlsanity.com/paper/2503.21259/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 52 references that are in the map.
- First posted on arXiv 27 Mar 2025.

## Builds on (most cited first, 14 of 14)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [DuDoNet: Dual Domain Network for CT Metal Artifact Reduction](https://mlsanity.com/paper/1907.00273/) (2019)
- [Deep Sinogram Completion with Image Prior for Metal Artifact Reduction in CT Images](https://mlsanity.com/paper/2009.07469/) (2020)
- [ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction](https://mlsanity.com/paper/1908.01104/) (2019)
- [InDuDoNet: An Interpretable Dual Domain Network for CT Metal Artifact Reduction](https://mlsanity.com/paper/2109.05298/) (2021)
- [CT sinogram-consistency learning for metal-induced beam hardening correction](https://mlsanity.com/paper/1708.00607/) (2017)
- [InDuDoNet+: A Deep Unfolding Dual Domain Network for Metal Artifact Reduction in CT Images](https://mlsanity.com/paper/2112.12660/) (2021)
- [Adaptive Convolutional Dictionary Network for CT Metal Artifact Reduction](https://mlsanity.com/paper/2205.07471/) (2022)
- [Generative Mask Pyramid Network for CT/CBCT Metal Artifact Reduction with Joint Projection-Sinogram Correction](https://mlsanity.com/paper/1907.00294/) (2019)
- [Unsupervised CT Metal Artifact Reduction by Plugging Diffusion Priors in Dual Domains](https://mlsanity.com/paper/2308.16742/) (2023)
- [Orientation-Shared Convolution Representation for CT Metal Artifact Learning](https://mlsanity.com/paper/2212.13166/) (2022)
- [MEPNet: A Model-Driven Equivariant Proximal Network for Joint Sparse-View Reconstruction and Metal Artifact Reduction in CT Images](https://mlsanity.com/paper/2306.14274/) (2023)

## Abstract (arXiv)

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/