计算机与现代化Issue(5)：9-16,24,9.DOI:10.3969/j.issn.1006-2475.2026.05.002
HDMamba:基于状态空间模型的低剂量CT去噪方法
HDMamba:State-space Model-based Denoising Method for Low-dose CT
摘要
Abstract
In low-dose CT denoising tasks,to address the issues of excessive GPU memory consumption caused by the high com-putational complexity of the quadratic calculation in self-attention mechanism-based models and loss of high-frequency details due to lack of adaptability in multi-scale feature fusion,this paper proposes a novel denoising model called HDMamba.The model adopts an encoder-decoder architecture with a Haar wavelet-based feature enhancement(HWFE)module deployed at the front end to provide richer feature representations for subsequent processing.Within each encoder-decoder layer,a multi-scale feature-aware state space module(MFSSM)is introduced to replace traditional self-attention mechanisms,employing spatial SSMs to achieve linear-complexity long-range dependency capture.Additionally,learnable parameters α and β are incorporated after the dilated convolution branch in the multi-scale feed-forward network to dynamically weight features at different scales.Experimental results on the AAPM dataset demonstrate that the proposed model maintains a low parameter count(31.13 M)while significantly outperforming existing denoising methods in all other evaluation metrics.关键词
低剂量CT去噪/状态空间模型/空洞卷积/小波变换/多尺度特征融合Key words
low-dose CT denoising/state space model/dilated convolution/wavelet transform/multi-scale feature fusion分类
信息技术与安全科学引用本文复制引用
张智轩,强彦,李青,闫心怡,段惠中..HDMamba:基于状态空间模型的低剂量CT去噪方法[J].计算机与现代化,2026,(5):9-16,24,9.基金项目
中国博士后科学基金面上项目(2025M772904) （2025M772904）
国家资助博士后研究人员计划(GZC20241586) （GZC20241586）
山西省基础研究计划资助项目(202403021212184) （202403021212184）
山西省高等学校科技创新项目(2024L181) （2024L181）
