# Structure-sensitive Multi-scale Deep Neural Network for Low-Dose CT Denoising

arXiv: https://arxiv.org/abs/1805.00587 · HTML: https://mlsanity.com/paper/1805.00587/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2018.

## Built on by (most cited first, 22 of 22)

- [Recurrent Mask Refinement for Few-Shot Medical Image Segmentation](https://mlsanity.com/paper/2108.00622/) (2021)
- [Momentum Contrastive Voxel-wise Representation Learning for Semi-supervised Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2105.07059/) (2021)
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- [Class-Aware Adversarial Transformers for Medical Image Segmentation](https://mlsanity.com/paper/2201.10737/) (2022)
- [Self-supervised Dialogue Learning for Spoken Conversational Question Answering](https://mlsanity.com/paper/2106.02182/) (2021)
- [Prototype Correlation Matching and Class-Relation Reasoning for Few-Shot Medical Image Segmentation](https://mlsanity.com/paper/2406.05054/) (2024)
- [A Decentralized Reinforcement Learning Framework for Efficient Passage of Emergency Vehicles](https://mlsanity.com/paper/2111.00278/) (2021)
- [EMVLight: a Multi-agent Reinforcement Learning Framework for an Emergency Vehicle Decentralized Routing and Traffic Signal Control System](https://mlsanity.com/paper/2206.13441/) (2022)
- [MEGAN: Memory Enhanced Graph Attention Network for Space-Time Video Super-Resolution](https://mlsanity.com/paper/2110.15327/) (2021)
- [Lesion-Inspired Denoising Network: Connecting Medical Image Denoising and Lesion Detection](https://mlsanity.com/paper/2104.08845/) (2021)
- [Assessing the capacity of a denoising diffusion probabilistic model to reproduce spatial context](https://mlsanity.com/paper/2309.10817/) (2023)
- [Graph-in-Graph Network for Automatic Gene Ontology Description Generation](https://mlsanity.com/paper/2206.05311/) (2022)
- [Multi-Scale Texture Loss for CT denoising with GANs](https://mlsanity.com/paper/2403.16640/) (2024)
- [GAN-based Medical Image Small Region Forgery Detection via a Two-Stage Cascade Framework](https://mlsanity.com/paper/2205.15170/) (2022)
- [Deep High-Resolution Network for Low Dose X-ray CT Denoising](https://mlsanity.com/paper/2102.00599/) (2021)
- [Multi-Cycle-Consistent Adversarial Networks for Edge Denoising of Computed Tomography Images](https://mlsanity.com/paper/2104.12044/) (2021)
- [EMVLight: A Decentralized Reinforcement Learning Framework for Efficient Passage of Emergency Vehicles](https://mlsanity.com/paper/2109.05429/) (2021)
- [Perceptual Influence: Improving the Perceptual Loss Design for Low-Dose CT Enhancement](https://mlsanity.com/paper/2509.23025/) (2025)
- [PLOT-CT: Pre-log Voronoi Decomposition Assisted Generation for Low-dose CT Reconstruction](https://mlsanity.com/paper/2602.11625/) (2026)

## Abstract (arXiv)

Computed tomography (CT) is a popular medical imaging modality in clinical applications. At the same time, the x-ray radiation dose associated with CT scans raises public concerns due to its potential risks to the patients. Over the past years, major efforts have been dedicated to the development of Low-Dose CT (LDCT) methods. However, the radiation dose reduction compromises the signal-to-noise ratio (SNR), leading to strong noise and artifacts that down-grade CT image quality. In this paper, we propose a novel 3D noise reduction method, called Structure-sensitive Multi-scale Generative Adversarial Net (SMGAN), to improve the LDCT image quality. Specifically, we incorporate three-dimensional (3D) volumetric information to improve the image quality. Also, different loss functions for training denoising models are investigated. Experiments show that the proposed method can effectively preserve structural and texture information from normal-dose CT (NDCT) images, and significantly suppress noise and artifacts. Qualitative visual assessments by three experienced radiologists demonstrate that the proposed method retrieves more detailed information, and outperforms competing methods.