ArticleJapanese journal of radiology2018
Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network.
Article in Japanese journal of radiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
66 citing papers in PubMed.
- Deep learning-based high-resolution united compressed sensing for gadoxetic acid-enhanced liver magnetic resonance imaging in the detection of colorectal liver metastases.Quantitative imaging in medicine and surgery · 2026Article
- Input Layer Regularization and Automated Regularization Hyperparameter Tuning for Myelin Water Estimation Using Deep Learning.NMR in biomedicine · 2026Article
- Complex Deep Neural Networks for Denoising Ultra-Fast Submillimeter T2*-weighted Imaging and Quantitative Susceptibility Mapping.European journal of radiology artificial intelligence · 2026Article
- Visualization of the Trochlear Nerve Using Deep Learning-enhanced 3D T2-weighted MR Imaging at 3T.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026Article
- Deep learning in fetal, infant, and toddler neuroimaging research.Developmental cognitive neuroscience · 2026Review
- Real-world unified denoising for multi-organ fast MRI: a large-scale prospective validation.NPJ digital medicine · 2026Article
- Validation and feasibility of a deep learning-based reconstruction technology in 5.0 tesla knee joint MR imaging.Frontiers in radiology · 2026Article
- Accelerating cardiac diffusion tensor imaging with deep learning-based tensor de-noising and breath hold reduction. A step towards improved efficiency and clinical feasibility.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2025Article
- Fast machine learning image reconstruction of radially undersampled k-space data for low-latency real-time MRI.PloS one · 2025Article
- Deep learning-based super-resolution and denoising algorithm improves reliability of dynamic contrast-enhanced MRI in diffuse glioma.Scientific reports · 2024Article
- Denoising of volumetric magnetic resonance imaging using multi-channel three-dimensional convolutional neural network with applications on fast spin echo acquisitions.Quantitative imaging in medicine and surgery · 2024Article
- Article
- Usefulness of pituitary high-resolution 3D MRI with deep-learning-based reconstruction for perioperative evaluation of pituitary adenomas.Neuroradiology · 2024Article
- Thin-slice Two-dimensional T2-weighted Imaging with Deep Learning-based Reconstruction: Improved Lesion Detection in the Brain of Patients with Multiple Sclerosis.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2024Article
- Contrast-enhanced to non-contrast-enhanced image translation to exploit a clinical data warehouse of T1-weighted brain MRI.BMC medical imaging · 2024Article
- FONDUE: Robust resolution-invariant denoising of MR images using Nested UNets.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Deep learning denoising reconstruction for improved image quality in fetal cardiac cine MRI.Frontiers in cardiovascular medicine · 2024Article
- 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
- Present and Future Innovations in AI and Cardiac MRI.Radiology · 2024Review
- Deep learning-based magnetic resonance imaging reconstruction for improving the image quality of reduced-field-of-view diffusion-weighted imaging of the pancreas.World journal of radiology · 2023Article
6 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo test if the proposed deep learning based denoising method denoising convolutional neural networks (DnCNN) with residual learning and multi-channel strategy can denoise three dimensional MR images with Rician noise robustly. MATERIALS AND
methodsMulti-channel DnCNN (MCDnCNN) method with two training strategies was developed to denoise MR images with and without a specific noise level, respectively. To evaluate our method, three datasets from two public data sources of IXI dataset and Brainweb, including T1 weighted MR images acquired at 1.5 and 3 T as well as MR images simulated with a widely used MR simulator, were randomly selected and artificially added with different noise levels ranging from 1 to 15%. For comparison, four other state-of-the-art denoising methods were also tested using these datasets.
resultsIn terms of the highest peak-signal-to-noise-ratio and global of structure similarity index, our proposed MCDnCNN model for a specific noise level showed the most robust denoising performance in all three datasets. Next to that, our general noise-applicable model also performed better than the rest four methods in two datasets. Furthermore, our training model showed good general applicability.
conclusionOur proposed MCDnCNN model has been demonstrated to robustly denoise three dimensional MR images with Rician noise.
Indexed as
Identifiers
29982919What 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.