{"id":"https://openalex.org/W4409156402","doi":"https://doi.org/10.1109/ieeeconf60004.2024.10942921","title":"INFusion: Diffusion Regularized Implicit Neural Representations for 2D and 3D Accelerated MRI Reconstruction","display_name":"INFusion: Diffusion Regularized Implicit Neural Representations for 2D and 3D Accelerated MRI Reconstruction","publication_year":2024,"publication_date":"2024-10-27","ids":{"openalex":"https://openalex.org/W4409156402","doi":"https://doi.org/10.1109/ieeeconf60004.2024.10942921"},"language":"en","primary_location":{"id":"doi:10.1109/ieeeconf60004.2024.10942921","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ieeeconf60004.2024.10942921","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 58th Asilomar Conference on Signals, Systems, and Computers","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5071952825","display_name":"Yamin Arefeen","orcid":"https://orcid.org/0000-0003-2194-1945"},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yamin Arefeen","raw_affiliation_strings":["The University of Texas at Austin,Chandra Family Department of Electrical and Computer Engineering"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Texas at Austin,Chandra Family Department of Electrical and Computer Engineering","institution_ids":["https://openalex.org/I86519309"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077641697","display_name":"Brett Levac","orcid":"https://orcid.org/0000-0002-1182-3438"},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Brett Levac","raw_affiliation_strings":["The University of Texas at Austin,Chandra Family Department of Electrical and Computer Engineering"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Texas at Austin,Chandra Family Department of Electrical and Computer Engineering","institution_ids":["https://openalex.org/I86519309"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093580742","display_name":"Zach Stoebner","orcid":null},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zach Stoebner","raw_affiliation_strings":["The University of Texas at Austin,Chandra Family Department of Electrical and Computer Engineering"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Texas at Austin,Chandra Family Department of Electrical and Computer Engineering","institution_ids":["https://openalex.org/I86519309"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056163027","display_name":"Jonathan I. Tamir","orcid":"https://orcid.org/0000-0001-9113-9566"},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jonathan I Tamir","raw_affiliation_strings":["The University of Texas at Austin,Chandra Family Department of Electrical and Computer Engineering"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Texas at Austin,Chandra Family Department of Electrical and Computer Engineering","institution_ids":["https://openalex.org/I86519309"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I86519309"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1886","last_page":"1890"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11205","display_name":"Numerical methods in inverse problems","score":0.9598000049591064,"subfield":{"id":"https://openalex.org/subfields/2610","display_name":"Mathematical Physics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11205","display_name":"Numerical methods in inverse problems","score":0.9598000049591064,"subfield":{"id":"https://openalex.org/subfields/2610","display_name":"Mathematical Physics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10378","display_name":"Advanced MRI Techniques and Applications","score":0.9391000270843506,"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/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.9375,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5797747373580933},{"id":"https://openalex.org/keywords/diffusion-mri","display_name":"Diffusion MRI","score":0.5255107879638672},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.511031985282898},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43319380283355713},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.34220650792121887},{"id":"https://openalex.org/keywords/magnetic-resonance-imaging","display_name":"Magnetic resonance imaging","score":0.24331516027450562},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.16562828421592712},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.13915902376174927},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.12043243646621704}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5797747373580933},{"id":"https://openalex.org/C149550507","wikidata":"https://www.wikidata.org/wiki/Q899360","display_name":"Diffusion MRI","level":3,"score":0.5255107879638672},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.511031985282898},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43319380283355713},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.34220650792121887},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.24331516027450562},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.16562828421592712},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.13915902376174927},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.12043243646621704},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ieeeconf60004.2024.10942921","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ieeeconf60004.2024.10942921","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 58th Asilomar Conference on Signals, Systems, and Computers","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4075909502","display_name":null,"funder_award_id":"CCF-2239687,IFML 2019844","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W2101675075","https://openalex.org/W2111388536","https://openalex.org/W2117649283","https://openalex.org/W2604388535","https://openalex.org/W2773850766","https://openalex.org/W2944761405","https://openalex.org/W2962785568","https://openalex.org/W3039236647","https://openalex.org/W3195875706","https://openalex.org/W4249760698","https://openalex.org/W4287756134","https://openalex.org/W4324018677","https://openalex.org/W4379660302","https://openalex.org/W4389665200","https://openalex.org/W4400918887","https://openalex.org/W4401990391","https://openalex.org/W4402667896","https://openalex.org/W6773041083","https://openalex.org/W6780179280","https://openalex.org/W6786375611","https://openalex.org/W6801478552","https://openalex.org/W6838452192","https://openalex.org/W6852431683","https://openalex.org/W6862131698"],"related_works":["https://openalex.org/W2772917594","https://openalex.org/W2036807459","https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398"],"abstract_inverted_index":{"Implicit":[0],"Neural":[1],"Representations":[2],"(INRs)":[3],"are":[4,27],"a":[5,58,114,138],"learning-based":[6],"approach":[7,141],"to":[8,85],"accelerate":[9],"Magnetic":[10],"Resonance":[11],"Imaging":[12],"(MRI)":[13],"acquisitions,":[14],"particularly":[15],"in":[16],"scan-specific":[17],"settings":[18],"when":[19],"only":[20],"data":[21],"from":[22,106,122],"the":[23,66,107,118],"under-sampled":[24,123],"scan":[25],"itself":[26],"available.":[28],"Previous":[29,71],"work":[30,72,111],"demonstrates":[31],"that":[32,69,116,146],"INRs":[33,51,121],"improve":[34],"rapid":[35],"MRI":[36],"through":[37],"inherent":[38],"regularization":[39,76,145,176],"imposed":[40],"by":[41,47,56],"neural":[42,49],"network":[43],"architectures.":[44],"Typically":[45],"parameterized":[46],"fully-connected":[48],"networks,":[50],"support":[52],"continuous":[53],"image":[54,103,132],"representations":[55],"taking":[57],"physical":[59],"coordinate":[60],"location":[61],"as":[62,99],"input":[63],"and":[64,81,166],"outputting":[65],"intensity":[67],"at":[68],"coordinate.":[70],"has":[73],"applied":[74],"unlearned":[75],"priors":[77,104],"during":[78],"INR":[79,148,159,172],"training":[80,160,173],"have":[82,95],"been":[83],"limited":[84],"2D":[86,155],"or":[87],"low-resolution":[88],"3D":[89,140,152,167,178],"acquisitions.":[90],"Meanwhile,":[91],"diffusion-based":[92],"generative":[93],"models":[94,129],"received":[96],"recent":[97],"attention":[98],"they":[100],"learn":[101],"powerful":[102],"decoupled":[105],"measurement":[108],"model.":[109],"This":[110],"proposes":[112],"INFusion,":[113],"technique":[115],"regularizes":[117],"optimization":[119],"of":[120,171,181],"MR":[124,153],"measurements":[125],"with":[126,142,161,174],"pre-trained":[127],"diffusion":[128,144,164,175],"for":[130],"improved":[131,158],"reconstruction.":[133],"In":[134],"addition,":[135],"we":[136],"propose":[137],"hybrid":[139],"our":[143,162],"enables":[147],"application":[149],"on":[150,177],"large-scale":[151],"datasets.":[154],"experiments":[156,168],"demonstrate":[157,169],"proposed":[163],"regularization,":[165],"feasibility":[170],"matrix":[179],"sizes":[180],"<tex":[182],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[183],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$256\\times":[184],"256\\times":[185],"80$</tex>.":[186]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}