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unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["denoising-algorithm","diffusion-mri","machine-learning","python"],"created_at":"2026-03-02T01:31:09.207Z","updated_at":"2026-03-02T01:31:10.109Z","avatar_url":"https://github.com/samuelstjean.png","language":"Python","readme":"# Non Local Spatial and Angular Matching (NLSAM) denoising\n\n[release]: https://github.com/samuelstjean/nlsam/releases\n[DOI]: http://dx.doi.org/doi:10.1016/j.media.2016.02.010\n[URL]: http://www.sciencedirect.com/science/article/pii/S1361841516000335\n[paper]: https://arxiv.org/pdf/1606.07239.pdf\n[autodmri_paper]: https://www.sciencedirect.com/science/article/pii/S1361841520301225\n[nlsam_data]: https://github.com/samuelstjean/nlsam_data\n[spams]: http://spams-devel.gforge.inria.fr/\n[rtd]: https://nlsam.readthedocs.io/en/latest/\n[koay_bias]: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2765718/\n\nThe reference implementation for the Non Local Spatial and Angular Matching (NLSAM) denoising algorithm for diffusion MRI.\n\n## Quick links\n\n+ [Source downloads + precompiled binaries](https://github.com/samuelstjean/nlsam/releases)\n+ [Example + Usage guide](example/README.md)\n\nYou can find the latest documentation and installation instructions over [here](http://nlsam.readthedocs.io/en/latest) with a downloadable version of the documentation [here](https://readthedocs.org/projects/nlsam/downloads).\n\n## How to install\n\nIf you have a working python setup already, the next command should give you everything you need.\n\n```shell\npip install nlsam\n```\n\nYou can also download the datasets used in the paper over [here][nlsam_data].\n\n## Using the NLSAM algorithm\n\nThe process is to first transform your data to Gaussian distributed signals if your dataset is\nRician or Noncentral chi distributed and then proceed to the NLSAM denoising part itself.\n\nA quickstart example call would be\n\n```bash\nnlsam_denoising dwi.nii.gz dwi_nlsam.nii.gz bvals bvecs -m mask.nii.gz\n```\n\nFor more fine grained control and explanation of arguments,\nhave a look at the possible command line options with nlsam_denoising --help\n\nYou can find a detailed usage example and assorted dataset to try out in the\n[example](example) folder.\n\n## Questions / Need help / Think this is great software?\n\nIf you need help or would like more information, don't hesitate to drop me a\nline at samuel.st_jean@university, where university needs to be replaced with med.lu.se\n\n## References\n\nThe NLSAM denoising algorithm itself is detailed in\n\n\u003e St-Jean, S., Coupé, P., \u0026 Descoteaux, M. (2016).\n\u003e \"[Non Local Spatial and Angular Matching : Enabling higher spatial resolution diffusion MRI datasets through adaptive denoising][paper]\"\n\u003e Medical Image Analysis, 32(2016), 115–130. [DOI] [URL]\n\nThe bias correction framework is a reimplementation of\n\n\u003e Koay, CG, Özarslan, E and Basser, PJ\n\u003e [A signal transformational framework for breaking the noise floor and its applications in MRI][koay_bias],\n\u003e Journal of Magnetic Resonance, Volume 197, Issue 2, 2009\n\nThe automatic estimation of the noise distribution is computed with\n\n\u003e St-Jean S, De Luca A, Tax C.M.W., Viergever M.A, Leemans A. (2020)\n\u003e \"[Automated characterization of noise distributions in diffusion MRI data.][autodmri_paper]\"\n\u003e Medical Image Analysis, October 2020:101758. doi:10.1016/j.media.2020.101758\n\nAnd here is a premade bibtex entry.\n\n    @article{St-Jean2016a,\n      author = {St-Jean, Samuel and Coup{\\'{e}}, Pierrick and Descoteaux, Maxime},\n      doi = {10.1016/j.media.2016.02.010},\n      journal = {Medical Image Analysis},\n      pages = {115--130},\n      title = {{Non Local Spatial and Angular Matching : Enabling higher spatial resolution diffusion MRI datasets through adaptive denoising}},\n      volume = {32},\n      year = {2016}\n      }\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsamuelstjean%2Fnlsam","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsamuelstjean%2Fnlsam","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsamuelstjean%2Fnlsam/lists"}