ArticleMedical physics2024
Benchmarking deep learning-based low-dose CT image denoising algorithms.
Article in Medical physics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Technical feasibility of AI-based longitudinal multi-organ volumetry on low-dose PET/CT over 13 years.Japanese journal of radiology · 2026Article
- Brain cone-beam CT image quality improvement using a deep-learning-based denoising method: a multicenter retrospective study.Neuroradiology · 2026Article
- Low-dose CT imaging using a regularization-enhanced efficient diffusion probabilistic model.Medical physics · 2026Article
- A tissue-informed deep learning-based method for positron range correction in preclinical [Formula: see text]Ga PET imaging.EJNMMI physics · 2026Article
- CT Radiation Dose Reduction With Preserved Diagnostic Performance: How Far Have We Come Over 25 Years?AJR. American journal of roentgenology · 2026Review
- Ultra-Low-Dose CTPA Using Sparse Sampling CT Combined with the U-Net for Deep Learning-Based Artifact Reduction: An Exploratory Study.Journal of imaging informatics in medicine · 2026Article
- Impact of Simulated Radiation Dose Reduction on Deep Learning-Based Renal Segmentation Performance: A Simulation Study Using the KiTS21 (2021 Kidney and Kidney Tumor Segmentation Challenge) Dataset.International neurourology journal · 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
- Enhancing low-dose CT denoising via multi-view knowledge transfer without paired data.Frontiers in radiology · 2026Article
- Dual-Domain Self-Supervised Deep Learning with Graph Convolution for Low-Dose Computed Tomography Reconstruction.Journal of imaging informatics in medicine · 2025Article
- Low-Dose CT Imaging Using a Regularization-Enhanced Efficient Diffusion Probabilistic Model.ArXiv · 2025Article
- No Reproducibility, No Progress: Rethinking CT Benchmarking.Journal of imaging · 2025Article
- Noise-augmented deep denoising: A method to boost CT image denoising networks.Medical physics · 2025Article
- ErisNet: A Deep Learning Model for Noise Reduction in CT Images.Bioengineering (Basel, Switzerland) · 2025Article
- Deep learning based super-resolution for CBCT dose reduction in radiotherapy.Medical physics · 2025Article
- Reconstructing and analyzing the invariances of low-dose CT image denoising networks.Medical physics · 2025Article
- Benchmarking deep learning-based low-dose CT image denoising algorithms.Medical physics · 2024Article
- A systematic review of deep learning-based denoising for low-dose computed tomography from a perceptual quality perspective.Biomedical engineering letters · 2024Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundLong-lasting efforts have been made to reduce radiation dose and thus the potential radiation risk to the patient for computed tomography (CT) acquisitions without severe deterioration of image quality. To this end, various techniques have been employed over the years including iterative reconstruction methods and noise reduction algorithms. PURPOSE: Recently, deep learning-based methods for noise reduction became increasingly popular and a multitude of papers claim ever improving performance both quantitatively and qualitatively. However, the lack of a standardized benchmark setup and inconsistencies in experimental design across studies hinder the verifiability and reproducibility of reported results.
methodsIn this study, we propose a benchmark setup to overcome those flaws and improve reproducibility and verifiability of experimental results in the field. We perform a comprehensive and fair evaluation of several state-of-the-art methods using this standardized setup.
resultsOur evaluation reveals that most deep learning-based methods show statistically similar performance, and improvements over the past years have been marginal at best.
conclusionsThis study highlights the need for a more rigorous and fair evaluation of novel deep learning-based methods for low-dose CT image denoising. Our benchmark setup is a first and important step towards this direction and can be used by future researchers to evaluate their algorithms.
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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.