{"id":"https://openalex.org/W4225015076","doi":"https://doi.org/10.1109/isbi52829.2022.9761475","title":"Deep Unrolling for Magnetic Resonance Fingerprinting","display_name":"Deep Unrolling for Magnetic Resonance Fingerprinting","publication_year":2022,"publication_date":"2022-03-28","ids":{"openalex":"https://openalex.org/W4225015076","doi":"https://doi.org/10.1109/isbi52829.2022.9761475"},"language":"en","primary_location":{"id":"doi:10.1109/isbi52829.2022.9761475","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi52829.2022.9761475","pdf_url":null,"source":{"id":"https://openalex.org/S4363605129","display_name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://hdl.handle.net/20.500.11820/7418bcde-5e22-47e7-a796-dde195c5c668","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100364591","display_name":"Dongdong Chen","orcid":"https://orcid.org/0000-0002-7016-9288"},"institutions":[{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Dongdong Chen","raw_affiliation_strings":["University of Edinburgh,School of Engineering,UK","School of Engineering, University of Edinburgh, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Edinburgh,School of Engineering,UK","institution_ids":["https://openalex.org/I98677209"]},{"raw_affiliation_string":"School of Engineering, University of Edinburgh, UK","institution_ids":["https://openalex.org/I98677209"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057962470","display_name":"Mike E. Davies","orcid":"https://orcid.org/0000-0003-2327-236X"},"institutions":[{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Mike E. Davies","raw_affiliation_strings":["University of Edinburgh,School of Engineering,UK","School of Engineering, University of Edinburgh, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Edinburgh,School of Engineering,UK","institution_ids":["https://openalex.org/I98677209"]},{"raw_affiliation_string":"School of Engineering, University of Edinburgh, UK","institution_ids":["https://openalex.org/I98677209"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056404513","display_name":"Mohammad Golbabaee","orcid":"https://orcid.org/0000-0001-5822-2990"},"institutions":[{"id":"https://openalex.org/I51601045","display_name":"University of Bath","ror":"https://ror.org/002h8g185","country_code":"GB","type":"education","lineage":["https://openalex.org/I51601045"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Mohammad Golbabaee","raw_affiliation_strings":["University of Bath,Computer Science Department,UK","Computer Science Department, University of Bath, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Bath,Computer Science Department,UK","institution_ids":["https://openalex.org/I51601045"]},{"raw_affiliation_string":"Computer Science Department, University of Bath, UK","institution_ids":["https://openalex.org/I51601045"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.0291,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.93294555,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10378","display_name":"Advanced MRI Techniques and Applications","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10378","display_name":"Advanced MRI Techniques and Applications","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12015","display_name":"Photoacoustic and Ultrasonic Imaging","score":0.9833999872207642,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7179415225982666},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.6739840507507324},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.6097910404205322},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5676651000976562},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5600846409797668},{"id":"https://openalex.org/keywords/cartesian-coordinate-system","display_name":"Cartesian coordinate system","score":0.519365131855011},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4911517798900604},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.47591739892959595},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.424216091632843},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4206019341945648},{"id":"https://openalex.org/keywords/inverse-problem","display_name":"Inverse problem","score":0.4172603487968445},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38926082849502563},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.338345468044281},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15247437357902527}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7179415225982666},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.6739840507507324},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.6097910404205322},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5676651000976562},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5600846409797668},{"id":"https://openalex.org/C16038011","wikidata":"https://www.wikidata.org/wiki/Q62912","display_name":"Cartesian coordinate system","level":2,"score":0.519365131855011},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4911517798900604},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.47591739892959595},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.424216091632843},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4206019341945648},{"id":"https://openalex.org/C135252773","wikidata":"https://www.wikidata.org/wiki/Q1567213","display_name":"Inverse problem","level":2,"score":0.4172603487968445},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38926082849502563},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.338345468044281},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15247437357902527},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/isbi52829.2022.9761475","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi52829.2022.9761475","pdf_url":null,"source":{"id":"https://openalex.org/S4363605129","display_name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.ed.ac.uk:openaire/7418bcde-5e22-47e7-a796-dde195c5c668","is_oa":true,"landing_page_url":"https://hdl.handle.net/20.500.11820/7418bcde-5e22-47e7-a796-dde195c5c668","pdf_url":null,"source":{"id":"https://openalex.org/S4306400321","display_name":"Edinburgh Research Explorer (University of Edinburgh)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I98677209","host_organization_name":"University of Edinburgh","host_organization_lineage":["https://openalex.org/I98677209"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Chen, D, Davies, M E & Golbabaee, M 2022, Deep Unrolling for Magnetic Resonance Fingerprinting. in 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). Institute of Electrical and Electronics Engineers, IEEE 19th International Symposium on Biomedical Imaging (ISBI) 2022, 28/03/22. https://doi.org/10.1109/ISBI52829.2022.9761475","raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"pmh:oai:pure.ed.ac.uk:publications/7418bcde-5e22-47e7-a796-dde195c5c668","is_oa":true,"landing_page_url":"https://www.research.ed.ac.uk/en/publications/7418bcde-5e22-47e7-a796-dde195c5c668","pdf_url":null,"source":{"id":"https://openalex.org/S4306400321","display_name":"Edinburgh Research Explorer (University of Edinburgh)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I98677209","host_organization_name":"University of Edinburgh","host_organization_lineage":["https://openalex.org/I98677209"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":""}],"best_oa_location":{"id":"pmh:oai:pure.ed.ac.uk:openaire/7418bcde-5e22-47e7-a796-dde195c5c668","is_oa":true,"landing_page_url":"https://hdl.handle.net/20.500.11820/7418bcde-5e22-47e7-a796-dde195c5c668","pdf_url":null,"source":{"id":"https://openalex.org/S4306400321","display_name":"Edinburgh Research Explorer (University of Edinburgh)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I98677209","host_organization_name":"University of Edinburgh","host_organization_lineage":["https://openalex.org/I98677209"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Chen, D, Davies, M E & Golbabaee, M 2022, Deep Unrolling for Magnetic Resonance Fingerprinting. in 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). Institute of Electrical and Electronics Engineers, IEEE 19th International Symposium on Biomedical Imaging (ISBI) 2022, 28/03/22. https://doi.org/10.1109/ISBI52829.2022.9761475","raw_type":"info:eu-repo/semantics/conferenceObject"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1510815927","https://openalex.org/W1901129140","https://openalex.org/W1963427860","https://openalex.org/W2035808554","https://openalex.org/W2061708033","https://openalex.org/W2144288697","https://openalex.org/W2914314139","https://openalex.org/W2950000029","https://openalex.org/W2962689831","https://openalex.org/W2969418397","https://openalex.org/W2980111715","https://openalex.org/W3092052495","https://openalex.org/W3110516481","https://openalex.org/W3115858671","https://openalex.org/W4287558222","https://openalex.org/W6639824700","https://openalex.org/W6767152940","https://openalex.org/W6779966144","https://openalex.org/W6787005777"],"related_works":["https://openalex.org/W2352472571","https://openalex.org/W4390516098","https://openalex.org/W2181948922","https://openalex.org/W1020620338","https://openalex.org/W2384362569","https://openalex.org/W4205302943","https://openalex.org/W2119949815","https://openalex.org/W2561132942","https://openalex.org/W2142795561","https://openalex.org/W3155418658"],"abstract_inverted_index":{"Magnetic":[0],"Resonance":[1],"Fingerprinting":[2],"(MRF)":[3],"has":[4],"emerged":[5],"as":[6,100],"a":[7,63,101],"promising":[8],"quantitative":[9],"MR":[10],"imaging":[11],"approach.":[12],"Deep":[13],"learning":[14,81],"methods":[15],"have":[16],"been":[17],"proposed":[18,62],"for":[19,52],"MRF":[20,126],"and":[21,74,117],"demonstrated":[22],"improved":[23],"performance":[24],"over":[25],"classical":[26],"compressed":[27],"sensing":[28],"algorithms.":[29],"However":[30],"many":[31],"of":[32,40,109],"these":[33],"end-to-end":[34],"models":[35,77],"are":[36],"physics-free,":[37],"while":[38],"consistency":[39],"the":[41,46,71,87,113,119],"predictions":[42],"with":[43,128],"respect":[44],"to":[45,103,111],"physical":[47],"forward":[48,72],"model":[49,89],"is":[50],"crucial":[51],"reliably":[53],"solving":[54],"inverse":[55],"problems.":[56],"To":[57],"address":[58],"this,":[59],"recently":[60],"[1]":[61,84],"proximal":[64,114],"gradient":[65],"descent":[66],"framework":[67],"that":[68],"directly":[69],"incorporates":[70],"acquisition":[73],"Bloch":[75],"dynamic":[76],"within":[78],"an":[79],"unrolled":[80,88],"mechanism.":[82],"However,":[83],"only":[85],"evaluated":[86],"on":[90,123],"synthetic":[91],"data":[92],"using":[93],"Cartesian":[94],"sampling":[95,131],"trajectories.":[96,132],"In":[97],"this":[98],"paper,":[99],"complementary":[102],"[1],":[104],"we":[105],"investigate":[106],"other":[107],"choices":[108],"encoders":[110],"build":[112],"neural":[115],"network,":[116],"evaluate":[118],"deep":[120],"unrolling":[121],"algorithm":[122],"real":[124],"accelerated":[125],"scans":[127],"non-Cartesian":[129],"k-space":[130]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":5}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
