{"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/coverblip-accelerated-and-scalable-iterative","title":"CoverBLIP: accelerated and scalable iterative matched-filtering for Magnetic Resonance Fingerprint reconstruction","arxiv_id":"1810.01967","date":"2018-10-03","proceeding":null,"authors":["Mohammad Golbabaee","Zhouye Chen","Yves Wiaux","Mike Davies"],"abstract":"Current popular methods for Magnetic Resonance Fingerprint (MRF) recovery are\nbottlenecked by the heavy computations of a matched-filtering step due to the\ngrowing size and complexity of the fingerprint dictionaries in multi-parametric\nquantitative MRI applications. We address this shortcoming by arranging\ndictionary atoms in the form of cover tree structures and adopt the\ncorresponding fast approximate nearest neighbour searches to accelerate\nmatched-filtering. For datasets belonging to smooth low-dimensional manifolds\ncover trees offer search complexities logarithmic in terms of data population.\nWith this motivation we propose an iterative reconstruction algorithm, named\nCoverBLIP, to address large-size MRF problems where the fingerprint dictionary\ni.e. discrete manifold of Bloch responses, encodes several intrinsic NMR\nparameters. We study different forms of convergence for this algorithm and we\nshow that provided with a notion of embedding, the inexact and non-convex\niterations of CoverBLIP linearly convergence toward a near-global solution with\nthe same order of accuracy as using exact brute-force searches. Our further\nexaminations on both synthetic and real-world datasets and using different\nsampling strategies, indicates between 2 to 3 orders of magnitude reduction in\ntotal search computations. Cover trees are robust against the\ncurse-of-dimensionality and therefore CoverBLIP provides a notion of\nscalability -- a consistent gain in time-accuracy performance-- for searching\nhigh-dimensional atoms which may not be easily preprocessed (i.e. for\ndimensionality reduction) due to the increasing degrees of non-linearities\nappearing in the emerging multi-parametric MRF dictionaries.","url_abs":"http://arxiv.org/abs/1810.01967v1","url_pdf":"http://arxiv.org/pdf/1810.01967v1.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":"coverblip-accelerated-and-scalable-iterative","repo_url":"https://github.com/mgolbabaee/CoverBLIP","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"},{"task_slug":"quantitative-mri","task_name":"Quantitative 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}