MambaCT: Feature Enhancement-Based Low-Dose CT Image Denoising Using Vision Mamba and a Scaling Adapter

With the rapid development of Mamba models, Vision Mamba is replacing convolutional neural networks (CNNs) and vision transformers (ViTs), emerging as the dominant trend in computer vision tasks. After achieving remarkable success in natural language processing, Mamba models have garnered increasing interest in the medical imaging community for their ability to understand global context. However, there has been limited research on medical image denoising based on the Vision Mamba architecture. In this paper, we propose MambaCT, a model that integrates the key features of UNet and Mamba with a Scaling Adapter in the visual state space (VSS) Block. Additionally, we propose skip connection spatial-channel processing attention (SCSPA) to enhance feature integration and robustness as a pathway in place of traditional skip connections. MambaCToutperforms previous state-of-the-art (SOTA) models across various architectures in both visual quality and quantitative performance, requiring only 0.83G MACs and achieving an SSIM of 0.9104 and RMSE of 9.3423. The model was evaluated on the AAPM-Mayo Clinic low-dose computed tomography (LDCT) Grand Challenge Dataset.