# Low-dose CT denoising with convolutional neural network

arXiv: https://arxiv.org/abs/1610.00321 · HTML: https://mlsanity.com/paper/1610.00321/

## Key facts

- Cited by 18 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 2 Oct 2016.

## Built on by (most cited first, 18 of 18)

- [Image Denoising: The Deep Learning Revolution and Beyond -- A Survey Paper --](https://mlsanity.com/paper/2301.03362/) (2023)
- [All-In-One Medical Image Restoration via Task-Adaptive Routing](https://mlsanity.com/paper/2405.19769/) (2024)
- [Deformed2Self: Self-Supervised Denoising for Dynamic Medical Imaging](https://mlsanity.com/paper/2106.12175/) (2021)
- [A comparative study between paired and unpaired Image Quality Assessment in Low-Dose CT Denoising](https://mlsanity.com/paper/2304.05359/) (2023)
- [Learning Domain-Aware Task Prompt Representations for Multi-Domain All-in-One Image Restoration](https://mlsanity.com/paper/2603.01725/) (2026)
- [Spectral2Spectral: Image-spectral Similarity Assisted Spectral CT Deep Reconstruction without Reference](https://mlsanity.com/paper/2210.01125/) (2022)
- [Region Attention Transformer for Medical Image Restoration](https://mlsanity.com/paper/2407.09268/) (2024)
- [Total-Body Low-Dose CT Image Denoising using Prior Knowledge Transfer Technique with Contrastive Regularization Mechanism](https://mlsanity.com/paper/2112.00729/) (2021)
- [Faster Diffusion Cardiac MRI with Deep Learning-based breath hold reduction](https://mlsanity.com/paper/2206.10543/) (2022)
- [3D U-NetR: Low Dose Computed Tomography Reconstruction via Deep Learning and 3 Dimensional Convolutions](https://mlsanity.com/paper/2105.14130/) (2021)
- [Residual Back Projection With Untrained Neural Networks](https://mlsanity.com/paper/2210.14416/) (2022)
- [Filter2Noise: A Framework for Interpretable and Zero-Shot Low-Dose CT Image Denoising](https://mlsanity.com/paper/2504.13519/) (2025)
- [Deep-Learning Driven Noise Reduction for Reduced Flux Computed Tomography](https://mlsanity.com/paper/2101.07376/) (2021)
- [Multi-Cycle-Consistent Adversarial Networks for Edge Denoising of Computed Tomography Images](https://mlsanity.com/paper/2104.12044/) (2021)
- [Window-Level is a Strong Denoising Surrogate](https://mlsanity.com/paper/2105.07153/) (2021)
- [Leveraging Multiphase CT for Quality Enhancement of Portal Venous CT: Utility for Pancreas Segmentation](https://mlsanity.com/paper/2501.14013/) (2025)
- [Task-based Regularization in Penalized Least-Squares for Binary Signal Detection Tasks in Medical Image Denoising](https://mlsanity.com/paper/2501.18418/) (2025)
- [Resolution-Agnostic Neural Operators for Multi-Rate Sparse-View CT](https://mlsanity.com/paper/2512.12236/) (2025)

## Abstract (arXiv)

To reduce the potential radiation risk, low-dose CT has attracted much attention. However, simply lowering the radiation dose will lead to significant deterioration of the image quality. In this paper, we propose a noise reduction method for low-dose CT via deep neural network without accessing original projection data. A deep convolutional neural network is trained to transform low-dose CT images towards normal-dose CT images, patch by patch. Visual and quantitative evaluation demonstrates a competing performance of the proposed method.