ArticleNeurocomputing2016
Denoising Magnetic Resonance Images Using Collaborative Non-Local Means.
Article in Neurocomputing, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Feasibility of Three-Dimensional Balanced Steady-State Free Precession Cine Magnetic Resonance Imaging Combined with an Image Denoising Technique to Evaluate Cardiac Function in Children with Repaired Tetralogy of Fallot.Korean journal of radiology · 2021Article
- Investigating the Performance of Generative Adversarial Networks for Prostate Tissue Detection and Segmentation.Journal of imaging · 2020Article
- Denoising of Diffusion MRI Data via Graph Framelet Matching in x-q Space.IEEE transactions on medical imaging · 2019Article
- Noise reduction in diffusion MRI using non-local self-similar information in joint x-q space.Medical image analysis · 2019Article
- Article
- Angular Upsampling in Infant Diffusion MRI Using Neighborhood Matching inFrontiers in neuroinformatics · 2018Article
- Neighborhood Matching for Curved Domains with Application to Denoising in Diffusion MRI.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2017Article
- Image mosaicking using SURF features of line segments.PloS one · 2017Article
- XQ-NLM: Denoising Diffusion MRI Data viaMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Noise artifacts in magnetic resonance (MR) images increase the complexity of image processing workflows and decrease the reliability of inferences drawn from the images. It is thus often desirable to remove such artifacts beforehand for more robust and effective quantitative analysis. It is important to preserve the integrity of relevant image information while removing noise in MR images. A variety of approaches have been developed for this purpose, and the non-local means (NLM) filter has been shown to be able to achieve state-of-the-art denoising performance. For effective denoising, NLM relies heavily on the existence of repeating structural patterns, which however might not always be present within a single image. This is especially true when one considers the fact that the human brain is complex and contains a lot of unique structures. In this paper we propose to leverage the repeating structures from
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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.