ArticleMagnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine2020
Deep Learning Based Noise Reduction for Brain MR Imaging: Tests on Phantoms and Healthy Volunteers.
Article in Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 125 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
125 citing papers in PubMed.
- Fast Submillimeter Whole-Brain TMagnetic resonance in medicine · 2026Article
- AI-based reconstruction from low-resolution acquisitions for diffusion MRI: evaluation of FOD similarity.Radiological physics and technology · 2026Article
- Interthalamic adhesion alterations in multiple sclerosis: associations with thalamic damage and cognition.Journal of neurology · 2026Article
- 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
- Deep Learning for Brain MRI Artifact Correction: Current Challenges and Future Directions.Bioengineering (Basel, Switzerland) · 2026Review
- Complex Deep Neural Networks for Denoising Ultra-Fast Submillimeter T2*-weighted Imaging and Quantitative Susceptibility Mapping.European journal of radiology artificial intelligence · 2026Article
- A Novel MR Cine Sialography Technique for Evaluating Salivary Gland Function: Correlation with Unstimulated and Stimulated Saliva Volumes.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026Article
- Enhancing resolution and image quality in musculoskeletal MRI using deep learning reconstruction.European radiology experimental · 2026Article
- Artificial Intelligence Across the Radiology Workflow: A Nine-Stage Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Accelerating imaging: deep learning for enhancedJapanese journal of radiology · 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 reconstruction for temporomandibular joint MRI: diagnostic interchangeability, image quality, and scan time reduction.European radiology · 2026Article
- Deep learning-enhanced zero echo time silent brain magnetic resonance imaging in infants without sedation.Pediatric radiology · 2026Article
- Super-Resolution Deep Learning Reconstruction for T2*-Weighted Images: Improvement in Microbleed Lesion Detection and Image Quality.Journal of imaging informatics in medicine · 2026Article
- Visualization of the Eustachian Tube Cartilage on Delayed Contrast-enhanced 3D-MR Imaging: A Preliminary Comparison between Ionic and Non-ionic Gadolinium-based Contrast Agents.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026Article
- Ensemble Deep Learning Denoising (EDLD) model and optimized OTSU segmentation for Alzheimer's disease diagnosis using MRI images.Frontiers in artificial intelligence · 2026Article
- Image quality evaluation of neonatal brain magnetic resonance imaging using a deep learning reconstruction algorithm: A quantitative and multireader study using variable denoising levels at 3 tesla.Journal of clinical imaging science · 2026Article
- Effects of Artificial Intelligence-based Reconstruction on Image Quality and Voxel-based Morphometry Analysis of Atrophy in Accelerated MPRAGE.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026Article
- Ultra-fast whole-brain T2-weighted imaging in 7 seconds using dual-type deep learning reconstruction with single-shot acquisition: clinical feasibility and comparison with conventional methods.Japanese journal of radiology · 2026Article
- Impact of AIR™ Recon DL on magnetic resonance imaging-based quantitative brain structure measurements.Psychoradiology · 2026Article
65 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
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo test whether our proposed denoising approach with deep learning-based reconstruction (dDLR) can effectively denoise brain MR images.
methodsIn an initial experimental study, we obtained brain images from five volunteers and added different artificial noise levels. Denoising was applied to the modified images using a denoising convolutional neural network (DnCNN), a shrinkage convolutional neural network (SCNN), and dDLR. Using these brain MR images, we compared the structural similarity (SSIM) index and peak signal-to-noise ratio (PSNR) between the three denoising methods. Two neuroradiologists assessed the image quality of the three types of images. In the clinical study, we evaluated the denoising effect of dDLR in brain images with different levels of actual noise such as thermal noise. Specifically, we obtained 2D-T
resultsIn the experimental study, PSNR and SSIM for dDLR were statistically higher than those of DnCNN and SCNN (P < 0.001). The image quality of dDLR was also superior to DnCNN and SCNN. In the clinical study, dDLR-NAQ2 was significantly better than NAQ2 images for SSIM and PSNR in all three sequences (P < 0.05), except for PSNR in FLAIR. For all qualitative items, dDLR-NAQ2 had equivalent or better image quality than NAQ5, and superior quality to that of NAQ2 (P < 0.05), for all criteria except artifact. The inter-observer agreement ranged from substantial to near perfect.
conclusiondDLR reduces image noise while preserving image quality on brain MR images.
Indexed as
Identifiers
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.