AS-Mamba: Asymmetric Self-Guided Mamba Decoupled Iterative Network for Metal Artifact Reduction

• URL: http://arxiv.org/abs/2602.06350v1
• Date: Fri, 06 Feb 2026 03:27:28 GMT
• Title: AS-Mamba: Asymmetric Self-Guided Mamba Decoupled Iterative Network for Metal Artifact Reduction
• Authors: Bowen Ning, Zekun Zhou, Xinyi Zhong, Zhongzhen Wang, HongXin Wu, HaiTao Wang, Liu Shi, Qiegen Liu,
• Abstract summary: Metal artifact significantly degrades Computed Tomography (CT) image quality.<n>Existing deep learning approaches, such as CNN and Transformer, often fail to explicitly capture the directional geometric features of artifacts.<n>We propose the Asymmetric Self-Guided Mamba (AS-Mamba) for metal artifact reduction.
• Score: 6.255398219368993
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Metal artifact significantly degrades Computed Tomography (CT) image quality, impeding accurate clinical diagnosis. However, existing deep learning approaches, such as CNN and Transformer, often fail to explicitly capture the directional geometric features of artifacts, leading to compromised structural restoration. To address these limitations, we propose the Asymmetric Self-Guided Mamba (AS-Mamba) for metal artifact reduction. Specifically, the linear propagation of metal-induced streak artifacts aligns well with the sequential modeling capability of State Space Models (SSMs). Consequently, the Mamba architecture is leveraged to explicitly capture and suppress these directional artifacts. Simultaneously, a frequency domain correction mechanism is incorporated to rectify the global amplitude spectrum, thereby mitigating intensity inhomogeneity caused by beam hardening. Furthermore, to bridge the distribution gap across diverse clinical scenarios, we introduce a self-guided contrastive regularization strategy. Extensive experiments on public andclinical dental CBCT datasets demonstrate that AS-Mamba achieves superior performance in suppressing directional streaks and preserving structural details, validating the effectiveness of integrating physical geometric priors into deep network design.

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