Non Local Spatial and Angular Matching: Enabling higher spatial resolution diffusion MRI datasets through adaptive denoising
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Diffusion magnetic resonance imaging (MRI) datasets suffer from low Signal-to-Noise Ratio (SNR), especially at high b-values. Acquiring data at high b-values contains relevant information and is now of great interest for microstructural and connectomics studies. High noise levels bias the measurements due to the non-Gaussian nature of the noise, which in turn can lead to a false and biased estimation of the diffusion parameters. Additionally, the usage of in-plane acceleration techniques during the acquisition leads to a spatially varying noise distribution, which depends on the parallel acceleration method implemented on the scanner. This paper proposes a novel diffusion MRI denoising technique that can be used on all existing data, without adding to the scanning time. We first apply a statistical framework to convert both stationary and non stationary Rician and non central Chi distributed noise to Gaussian distributed noise, effectively removing the bias. We then introduce a spatially and angular adaptive denoising technique, the Non Local Spatial and Angular Matching (NLSAM) algorithm. Each volume is first decomposed in small 4D overlapping patches, thus capturing the spatial and angular structure of the diffusion data, and a dictionary of atoms is learned on those patches. A local sparse decomposition is then found by bounding the reconstruction error with the local noise variance. We compare against three other state-of-the-art denoising methods and show quantitative local and connectivity results on a synthetic phantom and on an in-vivo high resolution dataset. Overall, our method restores perceptual information, removes the noise bias in common diffusion metrics, restores the extracted peaks coherence and improves reproducibility of tractography on the synthetic dataset. On the 1.2 mm high resolution in-vivo dataset, our denoising improves the visual quality of the data and reduces the number of spurious tracts when compared to the noisy acquisition. Our work paves the way for higher spatial resolution acquisition of diffusion MRI datasets, which could in turn reveal new anatomical details that are not discernible at the spatial resolution currently used by the diffusion MRI community.
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A pipeline built on ANTs registered the 66 volumes into a common space to correct for small displacements caused by eddy currents in the magnet.
“Once scans were completed, a pipeline built on ANTs90 registered the 66 volumes into a common space to correct for small displacements caused by eddy currents in the magnet, which vary between volumes.”
From Magnetic resonance microscopy maps widespread effects of Alzheimer’s disease on brain structures and behavior in mice (2026) · cites this paper as [90]
Section 3.4 (p. 10) says ANTs was used to register white-matter masks to in-vivo datasets, not to register 66 scan volumes into a common space. Section 5.7 (p. 24) discusses eddy-current correction as a preprocessing step but does not say the ANTs registration performed it. (LLM, conf=0.76)
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- Authors
- St-Jean, S., Coupé, P., & Descoteaux, M.
- Affiliations
- Université de Sherbrooke (CA)
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2016-08
- Volume
- 32
- Pages
- 115-130
- ISSN
- 1361-8415 (ISSN-L)
- OpenAlex
- W2298552625
- Cited by
- 74
- References
- 55
- Citations by year
- 2026: 4 · 2025: 9 · 2024: 4 · 2023: 5 · 2022: 7 · 2021: 5 · 2020: 16 · 2019: 11 · 2018: 7 · 2017: 6
- FWCI
- 5.43
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Medical Image Analysis (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Knowledge UdeS (Institutional Deposit of the University of Sherbrooke) (submittedVersion, cc-by-nc-nd)
- HAL (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University)
- Funders
- Agence Nationale de la Recherche (ANR- 10-IDEX-03-02, 10‐IDEX‐03‐02, 10-LABX-57, ANR-10-IDEX, ANR-10LABX-57, ANR-10, IDEX-03-02, ANR-10-LABX, HL-MRI ANR-10-IDEX-03-02, HR-DTI ANR-10-LABX-57)
- Centre National de la Recherche Scientifique (ANR-10-IDEX-03-02)
- Natural Sciences and Engineering Research Council of Canada
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- diffusion MRI denoising, adaptive denoising, Rician noise, dictionary learning, non-Gaussian noise
- MeSH terms
- Algorithms, Brain, Humans, Sensitivity and Specificity, Reproducibility of Results, Bias, Phantoms, Imaging, Diffusion Magnetic Resonance Imaging, Diffusion Tensor Imaging, Signal-To-Noise Ratio
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