{"id":"https://openalex.org/W7137958863","doi":"https://doi.org/10.1109/access.2026.3674726","title":"Denoising Diffusion Probabilistic Models for Magnetic Resonance Fingerprinting","display_name":"Denoising Diffusion Probabilistic Models for Magnetic Resonance Fingerprinting","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7137958863","doi":"https://doi.org/10.1109/access.2026.3674726"},"language":"en","primary_location":{"id":"doi:10.1109/access.2026.3674726","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3674726","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3674726","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5037114688","display_name":"Perla Mayo","orcid":"https://orcid.org/0000-0002-5224-9014"},"institutions":[{"id":"https://openalex.org/I36234482","display_name":"University of Bristol","ror":"https://ror.org/0524sp257","country_code":"GB","type":"education","lineage":["https://openalex.org/I36234482"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Perla Mayo","raw_affiliation_strings":["University of Bristol, Bristol, U.K"],"raw_orcid":"https://orcid.org/0000-0002-5224-9014","affiliations":[{"raw_affiliation_string":"University of Bristol, Bristol, U.K","institution_ids":["https://openalex.org/I36234482"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004509039","display_name":"Carolin M. Pirkl","orcid":"https://orcid.org/0000-0002-5759-5290"},"institutions":[{"id":"https://openalex.org/I4210153902","display_name":"Siemens Healthineers (Germany)","ror":"https://ror.org/0449c4c15","country_code":"DE","type":"company","lineage":["https://openalex.org/I4210153902"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Carolin M. Pirkl","raw_affiliation_strings":["GE HealthCare, Munich, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"GE HealthCare, Munich, Germany","institution_ids":["https://openalex.org/I4210153902"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001624119","display_name":"Alin Achim","orcid":"https://orcid.org/0000-0002-0982-7798"},"institutions":[{"id":"https://openalex.org/I36234482","display_name":"University of Bristol","ror":"https://ror.org/0524sp257","country_code":"GB","type":"education","lineage":["https://openalex.org/I36234482"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Alin M. Achim","raw_affiliation_strings":["University of Bristol, Bristol, U.K"],"raw_orcid":"https://orcid.org/0000-0002-0982-7798","affiliations":[{"raw_affiliation_string":"University of Bristol, Bristol, U.K","institution_ids":["https://openalex.org/I36234482"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002068604","display_name":"Bjoern Menze","orcid":"https://orcid.org/0000-0003-4136-5690"},"institutions":[{"id":"https://openalex.org/I202697423","display_name":"University of Zurich","ror":"https://ror.org/02crff812","country_code":"CH","type":"education","lineage":["https://openalex.org/I202697423"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Bjoern H. Menze","raw_affiliation_strings":["University of Zurich, Z&#x00FC;rich, Switzerland"],"raw_orcid":"https://orcid.org/0000-0003-4136-5690","affiliations":[{"raw_affiliation_string":"University of Zurich, Z&#x00FC;rich, Switzerland","institution_ids":["https://openalex.org/I202697423"]}]},{"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/I36234482","display_name":"University of Bristol","ror":"https://ror.org/0524sp257","country_code":"GB","type":"education","lineage":["https://openalex.org/I36234482"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Mohammad Golbabaee","raw_affiliation_strings":["University of Bristol, Bristol, U.K"],"raw_orcid":"https://orcid.org/0000-0001-5822-2990","affiliations":[{"raw_affiliation_string":"University of Bristol, Bristol, U.K","institution_ids":["https://openalex.org/I36234482"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":5.3071,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.9376372,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"14","issue":null,"first_page":"48198","last_page":"48211"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12603","display_name":"NMR spectroscopy and applications","score":0.1720999926328659,"subfield":{"id":"https://openalex.org/subfields/3106","display_name":"Nuclear and High Energy Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12603","display_name":"NMR spectroscopy and applications","score":0.1720999926328659,"subfield":{"id":"https://openalex.org/subfields/3106","display_name":"Nuclear and High Energy Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10378","display_name":"Advanced MRI Techniques and Applications","score":0.1615000069141388,"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/T10241","display_name":"Functional Brain Connectivity Studies","score":0.07109999656677246,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.6862000226974487},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.54830002784729},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46549999713897705},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.43799999356269836},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.4251999855041504},{"id":"https://openalex.org/keywords/stochastic-resonance","display_name":"Stochastic resonance","score":0.41929998993873596},{"id":"https://openalex.org/keywords/noise-measurement","display_name":"Noise measurement","score":0.35600000619888306}],"concepts":[{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6862000226974487},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5726000070571899},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.54830002784729},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49140000343322754},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46549999713897705},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.44040000438690186},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.43799999356269836},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.4251999855041504},{"id":"https://openalex.org/C207658827","wikidata":"https://www.wikidata.org/wiki/Q1999781","display_name":"Stochastic resonance","level":4,"score":0.41929998993873596},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.35600000619888306},{"id":"https://openalex.org/C2908826020","wikidata":"https://www.wikidata.org/wiki/Q899360","display_name":"Diffusion-Weighted Magnetic Resonance Imaging","level":4,"score":0.35269999504089355},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.31299999356269836},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.30660000443458557},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.28850001096725464},{"id":"https://openalex.org/C153946474","wikidata":"https://www.wikidata.org/wiki/Q333921","display_name":"Magnetometer","level":3,"score":0.2831999957561493},{"id":"https://openalex.org/C13944312","wikidata":"https://www.wikidata.org/wiki/Q7512748","display_name":"Signal-to-noise ratio (imaging)","level":2,"score":0.2815000116825104},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.273499995470047},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2653000056743622},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.25369998812675476},{"id":"https://openalex.org/C2983327147","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Image denoising","level":3,"score":0.25290000438690186},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.2502000033855438}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/access.2026.3674726","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3674726","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:research-information.bris.ac.uk:openaire/8d0b3563-eaba-493f-9713-1c19f2d3e760","is_oa":true,"landing_page_url":"https://research-information.bris.ac.uk/en/publications/8d0b3563-eaba-493f-9713-1c19f2d3e760","pdf_url":null,"source":{"id":"https://openalex.org/S4306400895","display_name":"Bristol Research (University of Bristol)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I36234482","host_organization_name":"University of Bristol","host_organization_lineage":["https://openalex.org/I36234482"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Mayo Diaz de Leon, P J, Pirkl, C, Achim, A, Menze, B & Golbabaee, M 2026, 'Denoising Diffusion Probabilistic Models for Magnetic Resonance Fingerprinting', IEEE Access, vol. 14, pp. 48198-48211. https://doi.org/10.1109/ACCESS.2026.3674726","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:doaj.org/article:6a574e8e91854fd0b3c2ac9cd681ef9f","is_oa":true,"landing_page_url":"https://doaj.org/article/6a574e8e91854fd0b3c2ac9cd681ef9f","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 14, Pp 48198-48211 (2026)","raw_type":"article"},{"id":"pmh:oai:research-information.bris.ac.uk:publications/8d0b3563-eaba-493f-9713-1c19f2d3e760","is_oa":true,"landing_page_url":"https://hdl.handle.net/1983/8d0b3563-eaba-493f-9713-1c19f2d3e760","pdf_url":null,"source":{"id":"https://openalex.org/S4306400895","display_name":"Bristol Research (University of Bristol)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I36234482","host_organization_name":"University of Bristol","host_organization_lineage":["https://openalex.org/I36234482"],"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":"Mayo Diaz de Leon, P J, Pirkl, C, Achim, A, Menze, B & Golbabaee, M 2026, 'Denoising Diffusion Probabilistic Models for Magnetic Resonance Fingerprinting', IEEE Access, vol. 14, pp. 48198-48211. https://doi.org/10.1109/ACCESS.2026.3674726","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3674726","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3674726","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8514421286","display_name":"Deep compressive quantitative MRI imaging","funder_award_id":"EP/X001091/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"}],"funders":[{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Magnetic":[0],"Resonance":[1],"Fingerprinting":[2],"(MRF)":[3],"is":[4],"a":[5,19,92],"time-efficient":[6],"approach":[7,128,150],"to":[8,96,183],"quantitative":[9,78,105,117],"MRI,":[10],"enabling":[11],"the":[12,51,66,126],"mapping":[13],"of":[14,54,157,173,177,187],"multiple":[15],"tissue":[16],"properties":[17],"from":[18,107],"single,":[20],"accelerated":[21,32,108,144],"scan.":[22],"However,":[23],"achieving":[24],"accurate":[25,104],"reconstructions":[26],"remains":[27],"challenging,":[28],"particularly":[29],"in":[30,65,142,153],"highly":[31,143],"and":[33,62,80,116,134,161,165],"undersampled":[34],"acquisitions,":[35],"which":[36],"are":[37,112],"crucial":[38],"for":[39,59,77,84,103,139,163],"reducing":[40],"scan":[41,122],"times.":[42],"While":[43],"deep":[44,132],"learning":[45,133],"techniques":[46],"have":[47,72],"advanced":[48],"image":[49],"reconstruction,":[50],"recent":[52],"introduction":[53],"diffusion":[55,70,94],"models":[56,71],"offers":[57],"newpossibilities":[58],"imaging":[60],"tasks":[61],"their":[63],"application":[64],"medical":[67],"field.":[68],"Notably,":[69],"only":[73],"recently":[74],"been":[75],"explored":[76],"MRI":[79,106],"remain":[81],"largely":[82],"unstudied":[83],"MRF":[85,98,140],"reconstruction.":[86],"In":[87,146],"this":[88],"work,":[89],"we":[90],"propose":[91],"conditional":[93],"model":[95],"reconstruct":[97],"data,":[99,123],"demonstrating":[100,124],"its":[101],"potential":[102],"acquisitions.":[109],"Our":[110],"findings":[111],"supported":[113],"by":[114],"qualitative":[115],"comparisons":[118],"on":[119],"in-vivo":[120],"brain":[121],"that":[125],"proposed":[127],"can":[129],"outperform":[130],"established":[131],"classical":[135],"compressed":[136],"sensing":[137],"algorithms":[138],"reconstruction":[141],"regimes.":[145],"our":[147,149,188],"experiments,":[148],"achieves":[151],"reductions":[152],"mean":[154],"percentage":[155],"errors":[156],"at":[158,169],"least":[159],"0.71%":[160],"2.15%":[162],"T1":[164],"T2":[166],"reconstructions,":[167],"respectively,":[168],"an":[170],"acceleration":[171],"factor":[172],"R=5.":[174],"A":[175],"range":[176],"ablation":[178],"studies":[179],"also":[180],"explore":[181],"strategies":[182],"improve":[184],"computational":[185],"efficiency":[186],"approach.":[189]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2026-03-18T00:00:00"}
