ArticleMedical image analysis2019
Noise reduction in diffusion MRI using non-local self-similar information in joint x-q space.
Article in Medical image analysis, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 28 citations in OpenAlex.
- Integrating Time and Frequency Domain Features of fMRI Time Series for Alzheimer's Disease Classification Using Graph Neural Networks.Interdisciplinary sciences, computational life sciences · 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
- Towards Accurate Microstructure Estimation via 3D Hybrid Graph Transformer.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- Arterial spin labeling MR image denoising and reconstruction using unsupervised deep learning.NMR in biomedicine · 2022Article
- Recent Advances in Parameter Inference for Diffusion MRI Signal Models.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2022Review
- Rician Denoising Based on Correlated Local Features LMMSE Approach.Journal of medical systems · 2021Article
- Gaussianization of Diffusion MRI Data Using Spatially Adaptive Filtering.Medical image analysis · 2021Article
- The sensitivity of diffusion MRI to microstructural properties and experimental factors.Journal of neuroscience methods · 2021Review
- Denoising Diffusion MRI via Graph Total Variance in Spatioangular Domain.Computational and mathematical methods in medicine · 2021Article
- Estimating Tissue Microstructure with Undersampled Diffusion Data via Graph Convolutional Neural Networks.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2020Article
- Multi-modal latent space inducing ensemble SVM classifier for early dementia diagnosis with neuroimaging data.Medical image analysis · 2020Article
- The impact of position-orientation adaptive smoothing in diffusion weighted imaging-From diffusion metrics to fiber tractography.PloS one · 2020Article
- Denoising of Diffusion MRI Data via Graph Framelet Matching in x-q Space.IEEE transactions on medical imaging · 2019Article
- Medical Image Enhancement by a Bilateral Filter Using Optimization Technique.Journal of medical systems · 2019Article
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Corrections and comments
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Authors and funding
4 authors at 2 institutions in 3 countries.
Funding
Abstract
Diffusion MRI affords valuable insights into white matter microstructures, but suffers from low signal-to-noise ratio (SNR), especially at high diffusion weighting (i.e., b-value). To avoid time-intensive repeated acquisition, post-processing algorithms are often used to reduce noise. Among existing methods, non-local means (NLM) has been shown to be particularly effective. However, most NLM algorithms for diffusion MRI focus on patch matching in the spatial domain (i.e., x-space) and disregard the fact that the data live in a combined 6D space covering both spatial domain and diffusion wavevector domain (i.e., q-space). This drawback leads to inaccurate patch matching in curved white matter structures and hence the inability to effectively use recurrent information for noise reduction. The goal of this paper is to overcome this limitation by extending NLM to the joint x-q space. Specifically, we define for each point in the x-q space a spherical patch from which we extract rotation-invariant features for patch matching. The ability to perform patch matching across q-samples allows patches from differentially orientated structures to be used for effective noise removal. Extensive experiments on synthetic, repeated-acquisition, and HCP data demonstrate that our method outperforms state-of-the-art methods, both qualitatively and quantitatively.
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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.