{"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/incorporation-of-prior-knowledge-of-the","title":"Incorporation of prior knowledge of the signal behavior into the reconstruction to accelerate the acquisition of MR diffusion data","arxiv_id":"1702.02743","date":"2017-02-09","proceeding":null,"authors":["Juan F P J Abascal","Manuel Desco","Juan Parra-Robles"],"abstract":"Diffusion MRI measurements using hyperpolarized gases are generally acquired\nduring patient breath hold, which yields a compromise between achievable image\nresolution, lung coverage and number of b-values. In this work, we propose a\nnovel method that accelerates the acquisition of MR diffusion data by\nundersampling in both spatial and b-value dimensions, thanks to incorporating\nknowledge about the signal decay into the reconstruction (SIDER). SIDER is\ncompared to total variation (TV) reconstruction by assessing their effect on\nboth the recovery of ventilation images and estimated mean alveolar dimensions\n(MAD). Both methods are assessed by retrospectively undersampling diffusion\ndatasets of normal volunteers and COPD patients (n=8) for acceleration factors\nbetween x2 and x10. TV led to large errors and artefacts for acceleration\nfactors equal or larger than x5. SIDER improved TV, presenting lower errors and\nhistograms of MAD closer to those obtained from fully sampled data for\naccelerations factors up to x10. SIDER preserved image quality at all\nacceleration factors but images were slightly smoothed and some details were\nlost at x10. In conclusion, we have developed and validated a novel compressed\nsensing method for lung MRI imaging and achieved high acceleration factors,\nwhich can be used to increase the amount of data acquired during a breath-hold.\nThis methodology is expected to improve the accuracy of estimated lung\nmicrostructure dimensions and widen the possibilities of studying lung diseases\nwith MRI.","url_abs":"http://arxiv.org/abs/1702.02743v1","url_pdf":"http://arxiv.org/pdf/1702.02743v1.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":"incorporation-of-prior-knowledge-of-the","repo_url":"https://github.com/HGGM-LIM/compressed-sensing-diffusion-lung-MRI","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"diffusion-mri","task_name":"Diffusion  MRI"},{"task_slug":"compressed-sensing","task_name":"compressed sensing"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}