Balancing Efficiency and Restoration: Lightweight Mamba-Based Model for CT Metal Artifact Reduction

TL;DR

The paper introduces MARMamba, a lightweight UNet-based model that effectively reduces metal artifacts in CT images while balancing computational efficiency and preserving anatomical details.

Contribution

It proposes a novel streamlined UNet architecture with multi-scale Mamba modules for efficient metal artifact reduction in CT imaging.

Findings

Outperforms existing models in artifact reduction quality.

Balances resource use and restoration efficiency effectively.

Code is publicly available at the provided GitHub URL.

Abstract

In computed tomography imaging, metal implants frequently generate severe artifacts that compromise image quality and hinder diagnostic accuracy. There are three main challenges in the existing methods: the deterioration of organ and tissue structures, dependence on sinogram data, and an imbalance between resource use and restoration efficiency. Addressing these issues, we introduce MARMamba, which effectively eliminates artifacts caused by metals of different sizes while maintaining the integrity of the original anatomical structures of the image. Furthermore, this model only focuses on CT images affected by metal artifacts, thus negating the requirement for additional input data. The model is a streamlined UNet architecture, which incorporates multi-scale Mamba (MS-Mamba) as its core module. Within MS-Mamba, a flip mamba block captures comprehensive contextual information by analyzing images from multiple orientations. Subsequently, the average maximum feed-forward network integrates critical features with average features to suppress the artifacts. This combination allows MARMamba to reduce artifacts efficiently. The experimental results demonstrate that our model excels in reducing metal artifacts, offering distinct advantages over other models. It also strikes an optimal balance between computational demands, memory usage, and the number of parameters, highlighting its practical utility in the real world. The code of the presented model is available at: https://github.com/RICKand-MORTY/MARMamba.