ReviewJournal of applied clinical medical physics2024
CT image denoising methods for image quality improvement and radiation dose reduction.
Review in Journal of applied clinical medical physics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed.
- Comparative Analysis of Bilateral-Based Noise Reduction Algorithms in Low-Dose Pediatric Computed Tomographic Images.Diagnostics (Basel, Switzerland) · 2026Article
- Brain cone-beam CT image quality improvement using a deep-learning-based denoising method: a multicenter retrospective study.Neuroradiology · 2026Article
- Cross-Modality Deep Learning Denoising for Low-Dose μSPECT: Transfer of PET-Trained U‑Net and Diffusion Models.Chemical & biomedical imaging · 2026Article
- FMDNet: Spatial-frequency feature routing for low-dose CT denoising.Journal of applied clinical medical physics · 2026Article
- Wavelet-enhanced autoencoder based image denoising with CNN fusion.Scientific reports · 2026Article
- Amplified Light Absorption with Nanomaterials for Enhanced Photoacoustic Imaging in Biomedical Research: A Review.Bioengineering (Basel, Switzerland) · 2026Review
- Feasibility of Artificial Intelligence-Processed Low-Dose Cone-Beam Computed Tomography in Dental Imaging.Bioengineering (Basel, Switzerland) · 2026Article
- Quantitative evaluation of a deep learning-based noise reduction algorithm in digital radiography using noise power spectrum analysis.Journal of applied clinical medical physics · 2026Article
- Evaluating the impact of deep learning-based image denoising on low-dose CT for lung cancer screening.Journal of applied clinical medical physics · 2026Article
- Current Status of AI-Assisted Screening for Opportunistic Osteoporosis.Current osteoporosis reports · 2026Review
- Recent advances in artificial intelligence-assisted medical imaging education.Frontiers in medicine · 2026Review
- A Review of Neural Network-Based Image Noise Processing Methods.Sensors (Basel, Switzerland) · 2025Review
- Centiloid values from deep learning-based CT parcellation: a valid alternative to freesurfer.Alzheimer's research & therapy · 2025Article
- ErisNet: A Deep Learning Model for Noise Reduction in CT Images.Bioengineering (Basel, Switzerland) · 2025Article
- Enhancing Image Quality in Dental-Maxillofacial CBCT: The Impact of Iterative Reconstruction and AI on Noise Reduction-A Systematic Review.Journal of clinical medicine · 2025Review
- Ultra-High-Resolution Photon-Counting-Detector CT with a Dedicated Denoising Convolutional Neural Network for Enhanced Temporal Bone Imaging.AJNR. American journal of neuroradiology · 2025Article
- CT/MRI technical pitfalls for diagnosis and treatment response assessment using LI-RADS and how to optimize.Abdominal radiology (New York) · 2025Review
- AI-Driven Advances in Low-Dose Imaging and Enhancement-A Review.Diagnostics (Basel, Switzerland) · 2025Review
- Towards ultra-low-dose CT for detecting pulmonary nodules using DenseNet.Physical and engineering sciences in medicine · 2025Article
- Improved lung nodule segmentation with a squeeze excitation dilated attention based residual UNet.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
With the ever-increasing use of computed tomography (CT), concerns about its radiation dose have become a significant public issue. To address the need for radiation dose reduction, CT denoising methods have been widely investigated and applied in low-dose CT images. Numerous noise reduction algorithms have emerged, such as iterative reconstruction and most recently, deep learning (DL)-based approaches. Given the rapid advancements in Artificial Intelligence techniques, we recognize the need for a comprehensive review that emphasizes the most recently developed methods. Hence, we have performed a thorough analysis of existing literature to provide such a review. Beyond directly comparing the performance, we focus on pivotal aspects, including model training, validation, testing, generalizability, vulnerability, and evaluation methods. This review is expected to raise awareness of the various facets involved in CT image denoising and the specific challenges in developing DL-based models.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.