# DenoMamba: A fused state-space model for low-dose CT denoising

arXiv: https://arxiv.org/abs/2409.13094 · HTML: https://mlsanity.com/paper/2409.13094/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 61 of its 107 references that are in the map.
- First posted on arXiv 19 Sep 2024.

## Builds on (most cited first, 30 of 61)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [SwinIR: Image Restoration Using Swin Transformer](https://mlsanity.com/paper/2108.10257/) (2021)
- [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021)
- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model](https://mlsanity.com/paper/2401.09417/) (2024)
- [VMamba: Visual State Space Model](https://mlsanity.com/paper/2401.10166/) (2024)
- [Pre-Trained Image Processing Transformer](https://mlsanity.com/paper/2012.00364/) (2020)
- [Uformer: A General U-Shaped Transformer for Image Restoration](https://mlsanity.com/paper/2106.03106/) (2021)
- [U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation](https://mlsanity.com/paper/2401.04722/) (2024)
- [MambaIR: A Simple Baseline for Image Restoration with State-Space Model](https://mlsanity.com/paper/2402.15648/) (2024)
- [VM-UNet: Vision Mamba UNet for Medical Image Segmentation](https://mlsanity.com/paper/2402.02491/) (2024)
- [SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation](https://mlsanity.com/paper/2401.13560/) (2024)
- [Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)](https://mlsanity.com/paper/1702.00288/) (2017)
- [Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index](https://mlsanity.com/paper/1308.3052/) (2013)
- [Unsupervised Medical Image Translation with Adversarial Diffusion Models](https://mlsanity.com/paper/2207.08208/) (2022)
- [Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining](https://mlsanity.com/paper/2402.03302/) (2024)
- [ResViT: Residual vision transformers for multi-modal medical image synthesis](https://mlsanity.com/paper/2106.16031/) (2021)
- [Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images](https://mlsanity.com/paper/2101.02824/) (2021)
- [Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss](https://mlsanity.com/paper/1708.00961/) (2017)
- [Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networks](https://mlsanity.com/paper/1802.01221/) (2018)
- [Adaptive Diffusion Priors for Accelerated MRI Reconstruction](https://mlsanity.com/paper/2207.05876/) (2022)
- [VmambaIR: Visual State Space Model for Image Restoration](https://mlsanity.com/paper/2403.11423/) (2024)
- [MedMamba: Vision Mamba for Medical Image Classification](https://mlsanity.com/paper/2403.03849/) (2024)
- [A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction](https://mlsanity.com/paper/1610.09736/) (2016)
- [Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers](https://mlsanity.com/paper/2105.08059/) (2021)

## Built on by (most cited first, 11 of 11)

- [Physics-Driven Autoregressive State Space Models for Medical Image Reconstruction](https://mlsanity.com/paper/2412.09331/) (2024)
- [VasTSD: Learning 3D Vascular Tree-state Space Diffusion Model for Angiography Synthesis](https://mlsanity.com/paper/2503.12758/) (2025)
- [Generative Autoregressive Transformers for Model-Agnostic Federated MRI Reconstruction](https://mlsanity.com/paper/2502.04521/) (2025)
- [All-in-One Medical Image Restoration with Latent Diffusion-Enhanced Vector-Quantized Codebook Prior](https://mlsanity.com/paper/2507.19874/) (2025)
- [TAT: Task-Adaptive Transformer for All-in-One Medical Image Restoration](https://mlsanity.com/paper/2512.14550/) (2025)
- [D-PerceptCT: Deep Perceptual Enhancement for Low-Dose CT Images](https://mlsanity.com/paper/2511.14518/) (2025)
- [Toward Better Optimization of Low-Dose CT Enhancement: A Critical Analysis of Loss Functions and Image Quality Assessment Metrics](https://mlsanity.com/paper/2511.00698/) (2025)
- [NeuroSSM: Multiscale Differential State-Space Modeling for Context-Aware fMRI Analysis](https://mlsanity.com/paper/2601.01229/) (2026)
- [A Strong Linear Baseline for Whole-Heart Cardiac Shape Completion on CT, with an Open Eleven-Structure Statistical Shape Model](https://mlsanity.com/paper/2608.19932/) (2026)
- [MARR: Decoupling Policy, Execution, and Calibration for All-in-One Medical Image Restoration](https://mlsanity.com/paper/2609.06645/) (2026)
- [UniH3: Unifying Hierarchical Homogeneity and Heterogeneity for All-in-One Medical Image Restoration](https://mlsanity.com/paper/2609.11156/) (2026)

## Abstract (arXiv)

Low-dose computed tomography (LDCT) lower potential risks linked to radiation exposure while relying on advanced denoising algorithms to maintain diagnostic quality in reconstructed images. The reigning paradigm in LDCT denoising is based on neural network models that learn data-driven image priors to separate noise evoked by dose reduction from underlying tissue signals. Naturally, the fidelity of these priors depend on the model's ability to capture the broad range of contextual features evident in CT images. Earlier convolutional neural networks (CNN) are highly adept at efficiently capturing short-range spatial context, but their limited receptive fields reduce sensitivity to interactions over longer distances. Although transformers based on self-attention mechanisms have recently been posed to increase sensitivity to long-range context, they can suffer from suboptimal performance and efficiency due to elevated model complexity, particularly for high-resolution CT images. For high-quality restoration of LDCT images, here we introduce DenoMamba, a novel denoising method based on state-space modeling (SSM), that efficiently captures short- and long-range context in medical images. Following an hourglass architecture with encoder-decoder stages, DenoMamba employs a spatial SSM module to encode spatial context and a novel channel SSM module equipped with a secondary gated convolution network to encode latent features of channel context at each stage. Feature maps from the two modules are then consolidated with low-level input features via a convolution fusion module (CFM). Comprehensive experiments on LDCT datasets with 25% and 10% dose reduction demonstrate that DenoMamba outperforms state-of-the-art denoisers with average improvements of 1.4dB PSNR, 1.1% SSIM, and 1.6% RMSE in recovered image quality.