ArticleComplex & intelligent systems2023
A review on Deep Learning approaches for low-dose Computed Tomography restoration.
Article in Complex & intelligent systems, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Evaluating the impact of deep learning-based image denoising on low-dose CT for lung cancer screening.Journal of applied clinical medical physics · 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
- Wavelet-domain frequency-mixing transformer unfolding network for low-dose computed tomography image denoising.Quantitative imaging in medicine and surgery · 2025Article
- Incorporating Radiologist Knowledge Into MRI Quality Metrics for Machine Learning Using Rank-Based Ratings.Journal of magnetic resonance imaging : JMRI · 2025Article
- Evaluating auto-contouring accuracy in reduced CT dose images for radiopharmaceutical therapies: Denoising and evaluation ofJournal of applied clinical medical physics · 2025Article
- AI-Driven Advances in Low-Dose Imaging and Enhancement-A Review.Diagnostics (Basel, Switzerland) · 2025Review
- Construction and Validation of a Machine Learning-Based Risk Prediction Model for Sleep Quality in Patients with OSA [Letter].Nature and science of sleep · 2025Article
- Sparse-View CT Joint Reconstruction Strategy with Sparse Sampling Encoding LayerCurrent medical imaging · 2025Article
- The data mining and high-performance network model of tourism electronic word of mouth for analysis of factors influencing tourists' purchasing behavior.Scientific reports · 2024Article
- Enhancement and evaluation for deep learning-based classification of volumetric neuroimaging with 3D-to-2D knowledge distillation.Scientific reports · 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
- Pure Vision Transformer (CT-ViT) with Noise2Neighbors Interpolation for Low-Dose CT Image Denoising.Journal of imaging informatics in medicine · 2024Article
- Systematic Review on Learning-based Spectral CT.ArXiv · 2024Article
- A CNN-based denoising method trained with images acquired with electron density phantoms for thin-sliced coronary artery calcium scans.Journal of applied clinical medical physics · 2024Article
- CT image denoising methods for image quality improvement and radiation dose reduction.Journal of applied clinical medical physics · 2024Review
- Systematic Review on Learning-based Spectral CT.IEEE transactions on radiation and plasma medical sciences · 2024Article
- Exploring the Intersection of Geophysics and Diagnostic Imaging in the Health Sciences.Diagnostics (Basel, Switzerland) · 2024Review
- Recent developments in X-ray diffraction/scattering computed tomography for materials science.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023Review
- Reducing scan time inJournal of applied clinical medical physics · 2023Article
- Dynamic controllable residual generative adversarial network for low-dose computed tomography imaging.Quantitative imaging in medicine and surgery · 2023Article
Corrections and comments
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Authors and funding
4 authors.
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Abstract
Computed Tomography (CT) is a widely use medical image modality in clinical medicine, because it produces excellent visualizations of fine structural details of the human body. In clinical procedures, it is desirable to acquire CT scans by minimizing the X-ray flux to prevent patients from being exposed to high radiation. However, these Low-Dose CT (LDCT) scanning protocols compromise the signal-to-noise ratio of the CT images because of noise and artifacts over the image space. Thus, various restoration methods have been published over the past 3 decades to produce high-quality CT images from these LDCT images. More recently, as opposed to conventional LDCT restoration methods, Deep Learning (DL)-based LDCT restoration approaches have been rather common due to their characteristics of being data-driven, high-performance, and fast execution. Thus, this study aims to elaborate on the role of DL techniques in LDCT restoration and critically review the applications of DL-based approaches for LDCT restoration. To achieve this aim, different aspects of DL-based LDCT restoration applications were analyzed. These include DL architectures, performance gains, functional requirements, and the diversity of objective functions. The outcome of the study highlights the existing limitations and future directions for DL-based LDCT restoration. To the best of our knowledge, there have been no previous reviews, which specifically address this topic.
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