ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2008
Rician noise removal by non-Local Means filtering for low signal-to-noise ratio MRI: applications to DT-MRI.
Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 80 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
80 citing papers in PubMed.
- Impact of deep learning-based MRI reconstruction on image quality and apparent diffusion coefficient measurements in patients with intracranial tumors: a prospective intra-individual study.Quantitative imaging in medicine and surgery · 2026Article
- No Association Between Hypnotizability and Basal Ganglia Morphometry.Brain sciences · 2026Article
- Evaluating Transferability of ComBat Harmonization of Diffusion Tensor Magnetic Resonance Imaging Data.Annals of biomedical engineering · 2026Article
- Optimization of the Non-Local Means Algorithm for Breast Diffusion-Weighted Magnetic Resonance Imaging Using a 3D-Printed Breast-Mimicking Phantom.Life (Basel, Switzerland) · 2025Article
- Dynamics of Ionic and Cytotoxic Edema During Acute and Subacute Stages of Patients With Ischemic Stroke: Complementarity ofNMR in biomedicine · 2025Article
- MRI denoising with a non-blind deep complex-valued convolutional neural network.NMR in biomedicine · 2025Article
- Microstructural brain assessment in late-life depression and apathy using diffusion MRI multi-compartments models and tractometry.Scientific reports · 2024Article
- Main Paths of Brain Fibers in Diffusion Images Mixed with a Noise to Improve Performance of Tractography Algorithm-Evaluation in Phantom.Journal of biomedical physics & engineering · 2024Article
- Longitudinal changes in resting state fMRI brain self-similarity of asymptomatic high school American football athletes.Scientific reports · 2024Article
- Emerging Trends in Fast MRI Using Deep-Learning Reconstruction on Undersampled k-Space Data: A Systematic Review.Bioengineering (Basel, Switzerland) · 2023Review
- ASL MRI informs blood flow to chronic stroke lesions in patients with aphasia.Frontiers in physiology · 2023Article
- Article
- Identification of texture MRI brain abnormalities on first-episode psychosis and clinical high-risk subjects using explainable artificial intelligence.Translational psychiatry · 2022Article
- Alterations of brain microstructures in a mouse model of prenatal opioid exposure detected by diffusion MRI.Scientific reports · 2022Article
- Cerebellar engagement in the attachment behavioral system.Scientific reports · 2022Article
- What's new and what's next in diffusion MRI preprocessing.NeuroImage · 2022Review
- Article
- Chaos analysis of the brain topology in first-episode psychosis and clinical high risk patients.Frontiers in psychiatry · 2022Article
- MR Denoising Increases Radiomic Biomarker Precision and Reproducibility in Oncologic Imaging.Journal of digital imaging · 2021Article
- White matter hyperintensity volumes are related to processing speed in long-term survivors of childhood cerebellar tumors.Journal of neuro-oncology · 2021Article
20 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diffusion-Weighted MRI (DW-MRI) is subject to random noise yielding measures that are different from their real values, and thus biasing the subsequently estimated tensors. The Non-Local Means (NLMeans) filter has recently been proposed to denoise MRI with high signal-to-noise ratio (SNR). This filter has been shown to allow the best restoration of image intensities for the estimation of diffusion tensors (DT) compared to state-of-the-art methods. However, for DW-MR images with high b-values (and thus low SNR), the noise, which is strictly Rician-distributed, can no longer be approximated as additive white Gaussian, as implicitly assumed in the classical formulation of the NLMeans. High b-values are typically used in high angular resolution diffusion imaging (HARDI) or q-space imaging (QSI), for which an optimal restoration is critical. In this paper, we propose to adapt the NLMeans filter to Rician noise corrupted data. Validation is performed on synthetic data and on real data for both conventional MR images and DT images. Our adaptation outperforms the original NLMeans filter in terms of peak-signal-to-noise ratio (PSNR) for DW-MRI.
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.