ArticleJournal of digital imaging2020
Deep Learning for Low-Dose CT Denoising Using Perceptual Loss and Edge Detection Layer.
Article in Journal of digital imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Feasibility of AI-Denoised Ultra-Low-Dose CT for Detection of Acute Pelvic and Hip Fractures: A Pilot Multi-Reader Study.Diagnostics (Basel, Switzerland) · 2026Article
- A new hybrid model for enhancing low-dose CT images using EfficientNetV2 and WGAN-GP: a multi-loss approach.European journal of medical research · 2025Article
- SADiff: A Sinogram-Aware Diffusion Model for Low-Dose CT Image Denoising.Journal of imaging informatics in medicine · 2025Article
- Dose Reduction in Scintigraphic Imaging Through Enhanced Convolutional Autoencoder-Based Denoising.Journal of imaging · 2025Article
- Multi-stage deep learning artifact reduction for parallel-beam computed tomography.Journal of synchrotron radiation · 2025Article
- Federated Metadata-Constrained iRadonMAP Framework with Mutual Learning for All-in-One Computed Tomography Imaging.Cyborg and bionic systems (Washington, D.C.) · 2025Article
- HybridFormer: a convolutional neural network-Transformer architecture for low dose computed tomography image denoising.PeerJ. Computer science · 2025Article
- Deep learning-based low count whole-body positron emission tomography denoising incorporating computed tomography priors.Quantitative imaging in medicine and surgery · 2024Article
- A review of deep learning-based reconstruction methods for accelerated MRI using spatiotemporal and multi-contrast redundancies.Biomedical engineering letters · 2024Review
- Pure Vision Transformer (CT-ViT) with Noise2Neighbors Interpolation for Low-Dose CT Image Denoising.Journal of imaging informatics in medicine · 2024Article
- An AI-Based Low-Risk Lung Health Image Visualization Framework Using LR-ULDCT.Journal of imaging informatics in medicine · 2024Article
- Gradient Guided Co-Retention Feature Pyramid Network for LDCT Image Denoising.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- DeepVID v2: self-supervised denoising with decoupled spatiotemporal enhancement for low-photon voltage imaging.Neurophotonics · 2024Article
- DeepVID v2: Self-Supervised Denoising with Decoupled Spatiotemporal Enhancement for Low-Photon Voltage Imaging.bioRxiv : the preprint server for biology · 2024Article
- CT image denoising methods for image quality improvement and radiation dose reduction.Journal of applied clinical medical physics · 2024Review
- Fast reconstruction of SMS bSSFP myocardial perfusion images using noise map estimation network (NoiseMapNet): a head-to-head comparison with parallel imaging and iterative reconstruction.Frontiers in cardiovascular medicine · 2024Article
- Need for objective task-based evaluation of deep learning-based denoising methods: A study in the context of myocardial perfusion SPECT.Medical physics · 2023Article
- Breast-Conserving Surgery Margin Guidance Using Micro-Computed Tomography: Challenges When Imaging Radiodense Resection Specimens.Annals of surgical oncology · 2023Article
- Bilateral Weighted Relative Total Variation for Low-Dose CT Reconstruction.Journal of digital imaging · 2023Article
- Feasibility of Dedicated Breast Positron Emission Tomography Image Denoising Using a Residual Neural Network.Asia Oceania journal of nuclear medicine & biology · 2023Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 causing 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 changing the complexity of the network, minimally.
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