{"id":"https://openalex.org/W4401750786","doi":"https://doi.org/10.1109/isbi56570.2024.10635766","title":"Inference Stage Denoising for Undersampled MRI Reconstruction","display_name":"Inference Stage Denoising for Undersampled MRI Reconstruction","publication_year":2024,"publication_date":"2024-05-27","ids":{"openalex":"https://openalex.org/W4401750786","doi":"https://doi.org/10.1109/isbi56570.2024.10635766"},"language":"en","primary_location":{"id":"doi:10.1109/isbi56570.2024.10635766","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/isbi56570.2024.10635766","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Symposium on Biomedical Imaging (ISBI)","raw_type":"proceedings-article"},"type":"article","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/A5008578775","display_name":"Yuyang Xue","orcid":"https://orcid.org/0000-0002-2281-9418"},"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":true,"raw_author_name":"Yuyang Xue","raw_affiliation_strings":["The University of Edinburgh,School of Engineering,Edinburgh,UK,EH9 3FG"],"affiliations":[{"raw_affiliation_string":"The University of Edinburgh,School of Engineering,Edinburgh,UK,EH9 3FG","institution_ids":["https://openalex.org/I98677209"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101550172","display_name":"Chen Qin","orcid":"https://orcid.org/0000-0002-4494-0817"},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Chen Qin","raw_affiliation_strings":["Imperial College,Department of Electrical and Electronic Engineering and I-X,London,UK,SW7 2AZ"],"affiliations":[{"raw_affiliation_string":"Imperial College,Department of Electrical and Electronic Engineering and I-X,London,UK,SW7 2AZ","institution_ids":["https://openalex.org/I47508984"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054437456","display_name":"Sotirios A. Tsaftaris","orcid":"https://orcid.org/0000-0002-8795-9294"},"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":"Sotirios A. Tsaftaris","raw_affiliation_strings":["The University of Edinburgh,School of Engineering,Edinburgh,UK,EH9 3FG"],"affiliations":[{"raw_affiliation_string":"The University of Edinburgh,School of Engineering,Edinburgh,UK,EH9 3FG","institution_ids":["https://openalex.org/I98677209"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5008578775"],"corresponding_institution_ids":["https://openalex.org/I98677209"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.22760304,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10378","display_name":"Advanced MRI Techniques and Applications","score":0.9919999837875366,"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":0.9919999837875366,"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/T11993","display_name":"Atomic and Subatomic Physics Research","score":0.9661999940872192,"subfield":{"id":"https://openalex.org/subfields/3107","display_name":"Atomic and Molecular Physics, and Optics"},"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/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.9611999988555908,"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/inference","display_name":"Inference","score":0.695689857006073},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6475910544395447},{"id":"https://openalex.org/keywords/stage","display_name":"Stage (stratigraphy)","score":0.5995355248451233},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.5634732246398926},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5532825589179993},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.47394660115242004},{"id":"https://openalex.org/keywords/image-denoising","display_name":"Image denoising","score":0.4440036714076996},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.37494558095932007},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34046807885169983},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.09476757049560547}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.695689857006073},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6475910544395447},{"id":"https://openalex.org/C146357865","wikidata":"https://www.wikidata.org/wiki/Q1123245","display_name":"Stage (stratigraphy)","level":2,"score":0.5995355248451233},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.5634732246398926},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5532825589179993},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.47394660115242004},{"id":"https://openalex.org/C2983327147","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Image denoising","level":3,"score":0.4440036714076996},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.37494558095932007},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34046807885169983},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.09476757049560547},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isbi56570.2024.10635766","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/isbi56570.2024.10635766","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Symposium on Biomedical Imaging (ISBI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3074771375","display_name":null,"funder_award_id":"EP/X017680/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G7363613089","display_name":null,"funder_award_id":"EP/Y002016/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G7386102364","display_name":null,"funder_award_id":"EP/X039277/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"}],"funders":[{"id":"https://openalex.org/F4320320005","display_name":"Royal Academy of Engineering","ror":"https://ror.org/0526snb40"},{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"},{"id":"https://openalex.org/F4320338440","display_name":"HORIZON EUROPE Health","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W2024634962","https://openalex.org/W2140404878","https://openalex.org/W2603777577","https://openalex.org/W2773850766","https://openalex.org/W2900756484","https://openalex.org/W2950936580","https://openalex.org/W3003650457","https://openalex.org/W3100730608","https://openalex.org/W3116694041","https://openalex.org/W3172058019","https://openalex.org/W3176521625","https://openalex.org/W3204069956","https://openalex.org/W3204290769","https://openalex.org/W3204825373","https://openalex.org/W4200533862","https://openalex.org/W4241414120","https://openalex.org/W4283213903","https://openalex.org/W4312282303","https://openalex.org/W4386352873","https://openalex.org/W6680531176","https://openalex.org/W6755625687","https://openalex.org/W6796851144","https://openalex.org/W6846849159"],"related_works":["https://openalex.org/W2895947835","https://openalex.org/W2087258800","https://openalex.org/W2810018092","https://openalex.org/W2387428419","https://openalex.org/W4401571043","https://openalex.org/W2098237619","https://openalex.org/W1974034585","https://openalex.org/W2353444452","https://openalex.org/W2001438600","https://openalex.org/W2499707420"],"abstract_inverted_index":{"Reconstruction":[0],"of":[1,82,91,123],"magnetic":[2],"resonance":[3],"imaging":[4],"(MRI)":[5],"data":[6,41,60],"has":[7],"been":[8],"positively":[9],"affected":[10,47],"by":[11,48,72],"deep":[12],"learning.":[13],"A":[14],"key":[15],"challenge":[16],"still":[17],"remains:":[18],"to":[19,22,34,139],"improve":[20],"generalisation":[21],"distribution":[23],"shifts":[24],"between":[25],"the":[26,57,66,80,109,121,129],"training":[27],"and":[28,54,132],"testing":[29],"data.":[30],"Most":[31],"approaches":[32],"aim":[33],"address":[35],"this":[36,70],"via":[37],"inductive":[38],"design":[39],"or":[40],"augmentation.":[42],"However,":[43],"they":[44],"can":[45],"be":[46],"misleading":[49],"data,":[50],"e.g.":[51],"random":[52],"noise,":[53],"cases":[55],"where":[56],"inference":[58,110],"stage":[59],"do":[61],"not":[62],"match":[63],"assumptions":[64],"in":[65,135],"modelled":[67],"shifts.":[68],"In":[69],"work,":[71],"employing":[73],"a":[74,115],"conditional":[75],"hyperparameter":[76,116],"network,":[77],"we":[78,113],"eliminate":[79],"need":[81],"augmentation,":[83],"yet":[84],"maintain":[85],"robust":[86],"performance":[87],"under":[88],"various":[89,100],"levels":[90,103],"Gaussian":[92],"noise.":[93],"We":[94],"demonstrate":[95],"that":[96,119],"our":[97],"model":[98],"withstands":[99],"input":[101],"noise":[102],"while":[104],"producing":[105],"high-quality":[106],"reconstructions":[107],"during":[108],"stage.":[111],"Moreover,":[112],"present":[114],"sampling":[117],"strategy":[118],"accelerates":[120],"convergence":[122],"training.":[124],"Our":[125],"proposed":[126],"method":[127],"achieves":[128],"highest":[130],"accuracy":[131],"image":[133],"quality":[134],"all":[136],"settings":[137],"compared":[138],"baseline":[140],"methods.":[141]},"counts_by_year":[],"updated_date":"2025-12-26T23:08:49.675405","created_date":"2025-10-10T00:00:00"}