# Deep Learning for Low-Dose CT Denoising

arXiv: https://arxiv.org/abs/1902.10127 · HTML: https://mlsanity.com/paper/1902.10127/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2019.

## Built on by (most cited first, 14 of 14)

- [Eformer: Edge Enhancement based Transformer for Medical Image Denoising](https://mlsanity.com/paper/2109.08044/) (2021)
- [Diffusion Probabilistic Priors for Zero-Shot Low-Dose CT Image Denoising](https://mlsanity.com/paper/2305.15887/) (2023)
- [Perceptual Loss for Robust Unsupervised Homography Estimation](https://mlsanity.com/paper/2104.10011/) (2021)
- [Lesion-Inspired Denoising Network: Connecting Medical Image Denoising and Lesion Detection](https://mlsanity.com/paper/2104.08845/) (2021)
- [Low-dose CT Denoising with Language-engaged Dual-space Alignment](https://mlsanity.com/paper/2403.06128/) (2024)
- [CT-Mamba: A Hybrid Convolutional State Space Model for Low-Dose CT Denoising](https://mlsanity.com/paper/2411.07930/) (2024)
- [Need for Objective Task-based Evaluation of Deep Learning-Based Denoising Methods: A Study in the Context of Myocardial Perfusion SPECT](https://mlsanity.com/paper/2303.02110/) (2023)
- [Low-resolution Prior Equilibrium Network for CT Reconstruction](https://mlsanity.com/paper/2401.15663/) (2024)
- [Three-dimensional visualization of X-ray micro-CT with large-scale datasets: Efficiency and accuracy for real-time interaction](https://mlsanity.com/paper/2601.15098/) (2026)
- [Learning Perspective Deformation in X-Ray Transmission Imaging](https://mlsanity.com/paper/2202.06366/) (2022)
- [Deterministic Medical Image Translation via High-fidelity Brownian Bridges](https://mlsanity.com/paper/2503.22531/) (2025)
- [BioAtt: Anatomical Prior Driven Low-Dose CT Denoising](https://mlsanity.com/paper/2504.01662/) (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)
- [Visible Singularities Guided Correlation Network for Limited-Angle CT Reconstruction](https://mlsanity.com/paper/2602.00184/) (2026)

## Abstract (arXiv)

Low-dose CT denoising is a challenging task that has been studied by many researchers. Some studies have used deep neural networks to improve the quality of low-dose CT images and achieved fruitful results. In this paper, we propose a deep neural network that uses dilated convolutions with different dilation rates instead of standard convolution helping to capture more contextual information in fewer layers. Also, we have employed residual learning by creating shortcut connections to transmit image information from the early layers to later ones. To further improve the performance of the network, we have introduced a non-trainable edge detection layer that extracts edges in horizontal, vertical, and diagonal directions. Finally, we demonstrate that optimizing the network by a combination of mean-square error loss and perceptual loss preserves many structural details in the CT image. This objective function does not suffer from over smoothing and blurring effects caused by per-pixel loss and grid-like artifacts resulting from perceptual loss. The experiments show that each modification to the network improves the outcome while only minimally changing the complexity of the network.