{"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/automatic-fast-and-robust-characterization-of","title":"Automatic, fast and robust characterization of noise distributions for diffusion MRI","arxiv_id":"1805.12071","date":"2018-05-30","proceeding":null,"authors":["Samuel St-Jean","Alberto De Luca","Max A. Viergever","Alexander Leemans"],"abstract":"Knowledge of the noise distribution in magnitude diffusion MRI images is the\ncenterpiece to quantify uncertainties arising from the acquisition process. The\nuse of parallel imaging methods, the number of receiver coils and imaging\nfilters applied by the scanner, amongst other factors, dictate the resulting\nsignal distribution. Accurate estimation beyond textbook Rician or noncentral\nchi distributions often requires information about the acquisition process\n(e.g. coils sensitivity maps or reconstruction coefficients), which is not\nusually available. We introduce a new method where a change of variable\nnaturally gives rise to a particular form of the gamma distribution for\nbackground signals. The first moments and maximum likelihood estimators of this\ngamma distribution explicitly depend on the number of coils, making it possible\nto estimate all unknown parameters using only the magnitude data. A rejection\nstep is used to make the method automatic and robust to artifacts. Experiments\non synthetic datasets show that the proposed method can reliably estimate both\nthe degrees of freedom and the standard deviation. The worst case errors range\nfrom below 2% (spatially uniform noise) to approximately 10% (spatially\nvariable noise). Repeated acquisitions of in vivo datasets show that the\nestimated parameters are stable and have lower variances than compared methods.","url_abs":"http://arxiv.org/abs/1805.12071v2","url_pdf":"http://arxiv.org/pdf/1805.12071v2.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":"automatic-fast-and-robust-characterization-of","repo_url":"https://github.com/samuelstjean/autodmri","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"automatic-fast-and-robust-characterization-of","repo_url":"https://github.com/samuelstjean/nlsam","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"diffusion-mri","task_name":"Diffusion  MRI"},{"task_slug":"noise-estimation","task_name":"Noise Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}