{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/non-local-spatial-and-angular-matching","title":"Non Local Spatial and Angular Matching : Enabling higher spatial resolution diffusion MRI datasets through adaptive denoising","arxiv_id":"1606.07239","date":"2016-06-23","proceeding":null,"authors":["Samuel St-Jean","Pierrick Coupé","Maxime Descoteaux"],"abstract":"Diffusion magnetic resonance imaging datasets suffer from low Signal-to-Noise\nRatio, especially at high b-values. Acquiring data at high b-values contains\nrelevant information and is now of great interest for microstructural and\nconnectomics studies. High noise levels bias the measurements due to the\nnon-Gaussian nature of the noise, which in turn can lead to a false and biased\nestimation of the diffusion parameters. Additionally, the usage of in-plane\nacceleration techniques during the acquisition leads to a spatially varying\nnoise distribution, which depends on the parallel acceleration method\nimplemented on the scanner. This paper proposes a novel diffusion MRI denoising\ntechnique that can be used on all existing data, without adding to the scanning\ntime. We first apply a statistical framework to convert the noise to Gaussian\ndistributed noise, effectively removing the bias. We then introduce a spatially\nand angular adaptive denoising technique, the Non Local Spatial and Angular\nMatching (NLSAM) algorithm. Each volume is first decomposed in small 4D\noverlapping patches to capture the structure of the diffusion data and a\ndictionary of atoms is learned on those patches. A local sparse decomposition\nis then found by bounding the reconstruction error with the local noise\nvariance. We compare against three other state-of-the-art denoising methods and\nshow quantitative local and connectivity results on a synthetic phantom and on\nan in-vivo high resolution dataset. Overall, our method restores perceptual\ninformation, removes the noise bias in common diffusion metrics, restores the\nextracted peaks coherence and improves reproducibility of tractography. Our\nwork paves the way for higher spatial resolution acquisition of diffusion MRI\ndatasets, which could in turn reveal new anatomical details that are not\ndiscernible at the spatial resolution currently used by the diffusion MRI\ncommunity.","url_abs":"http://arxiv.org/abs/1606.07239v1","url_pdf":"http://arxiv.org/pdf/1606.07239v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"non-local-spatial-and-angular-matching","repo_url":"https://github.com/samuelstjean/nlsam","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"diffusion-mri","task_name":"Diffusion  MRI"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}