Wave-MambaCT: Low-dose CT Artifact Suppression Method Based on Wavelet Mamba

CUI Xueying; WANG Yuhang; LIU Bin; SHANGGUAN Hong; ZHANG Xiong

doi:10.11999/JEIT250489

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CUI Xueying, WANG Yuhang, LIU Bin, SHANGGUAN Hong, ZHANG Xiong. Wave-MambaCT: Low-dose CT Artifact Suppression Method Based on Wavelet Mamba[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250489

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CUI Xueying, WANG Yuhang, LIU Bin, SHANGGUAN Hong, ZHANG Xiong. Wave-MambaCT: Low-dose CT Artifact Suppression Method Based on Wavelet Mamba[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250489

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Wave-MambaCT: Low-dose CT Artifact Suppression Method Based on Wavelet Mamba

doi: 10.11999/JEIT250489 cstr: 32379.14.JEIT250489

1.
School of Applied Sciences, Taiyuan University of Science and Technology, Taiyuan 030024, China
2.
School of Computer Sciences and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, China
3.
School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China

Funds: The Natural Science for Youth Foundation (62001321), The Natural Science Foundation of Shanxi Province (202303021221144, 202403021221140, 202403021221139)

Received Date: 2025-06-03
Rev Recd Date: 2033-05-10

Available Online: 2025-10-23

Abstract

Abstract

Objective Low-Dose Computed Tomography (LDCT) reduces patient radiation exposure but introduces substantial noise and artifacts into reconstructed images. Convolutional Neural Network (CNN)-based denoising approaches are limited by local receptive fields, which restrict their abilities to capture long-range dependencies. Transformer-based methods alleviate this limitation but incur quadratic computational complexity relative to image size. In contrast, State Space Model (SSM)–based Mamba frameworks achieve linear complexity for long-range interactions. However, existing Mamba-based methods often suffer from information loss and insufficient noise suppression. To address these limitations, we propose the Wave-MambaCT model. Methods The proposed Wave-MambaCT model adopts a multi-scale framework that integrates Discrete Wavelet Transform (DWT) with a Mamba module based on the SSM. First, DWT performs a two-level decomposition of the LDCT image, decoupling noise from Low-Frequency (LF) content. This design directs denoising primarily toward the High-Frequency (HF) components, facilitating noise suppression while preserving structural information. Second, a residual module combined with a Spatial-Channel Mamba (SCM) module extracts both local and global features from LF and HF bands at different scales. The noise-free LF features are then used to correct and enhance the corresponding HF features through an attention-based Cross-Frequency Mamba (CFM) module. Finally, inverse wavelet transform is applied in stages to progressively reconstruct the image. To further improve denoising performance and network stability, multiple loss functions are employed, including L1 loss, wavelet-domain LF loss, and adversarial loss for HF components. Results and Discussions Extensive experiments on the simulated Mayo Clinic datasets, the real Piglet datasets, and the hospital clinical dataset DeepLesion show that Wave-MambaCT provides superior denoising performance and generalization. On the Mayo dataset, a PSNR of 31.6528 is achieved, which is higher than that of the suboptimal method DenoMamba (PSNR 31.4219), while MSE is reduced to 0.00074 and SSIM and VIF are improved to 0.8851 and 0.4629, respectively (Table 1). Visual results (Figs. 4–6) demonstrate that edges and fine details such as abdominal textures and lesion contours are preserved, with minimal blurring or residual artifacts compared with competing methods. Computational efficiency analysis (Table 2) indicates that Wave-MambaCT maintains low FLOPs (17.2135 G) and parameters (5.3913 M). FLOPs are lower than those of all networks except RED-CNN, and the parameter count is higher only than those of RED-CNN and CTformer. During training, 4.12 minutes per epoch are required, longer only than RED-CNN. During testing, 0.1463 seconds are required per image, which is at a medium level among the compared methods. Generalization tests on the Piglet datasets (Figs. 7, 8, Tables 3, 4) and DeepLesion (Fig. 9) further confirm the robustness and generalization capacity of Wave-MambaCT.In the proposed design, HF sub-bands are grouped, and noise-free LF information is used to correct and guide their recovery. This strategy is based on two considerations. First, it reduces network complexity and parameter count. Second, although the sub-bands correspond to HF information in different orientations, they are correlated and complementary as components of the same image. Joint processing enhances the representation of HF content, whereas processing them separately would require a multi-branch architecture, inevitably increasing complexity and parameters. Future work will explore approaches to reduce complexity and parameters when processing HF sub-bands individually, while strengthening their correlations to improve recovery. For structural simplicity, SCM is applied to both HF and LF feature extraction. However, redundancy exists when extracting LF features, and future studies will explore the use of different Mamba modules for HF and LF features to further optimize computational efficiency. Conclusions Wave-MambaCT integrates DWT for multi-scale decomposition, a residual module for local feature extraction, and an SCM module for efficient global dependency modeling to address the denoising challenges of LDCT images. By decoupling noise from LF content through DWT, the model enables targeted noise removal in the HF domain, facilitating effective noise suppression. The designed RSCM, composed of residual blocks and SCM modules, captures fine-grained textures and long-range interactions, enhancing the extraction of both local and global information. In parallel, the Cross-band Enhancement Module (CEM) employs noise-free LF features to refine HF components through attention-based CFM, ensuring structural consistency across scales. Ablation studies (Table 5) confirm the essential contributions of both SCM and CEM modules to maintaining high performance. Importantly, the model’s staged denoising strategy achieves a favorable balance between noise reduction and structural preservation, yielding robustness to varying radiation doses and complex noise distributions.
- Low-dose CT,
- Artifact suppression,
- Wavelet transform,
- Mamba

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References(23)

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