{"id":"https://openalex.org/W7165624456","doi":"https://doi.org/10.48550/arxiv.2606.21588","title":"Unsupervised Susceptibility Distortion Correction of EPI without Calibration Scans via Image Translation-Based Registration","display_name":"Unsupervised Susceptibility Distortion Correction of EPI without Calibration Scans via Image Translation-Based Registration","publication_year":2026,"publication_date":"2026-06-19","ids":{"openalex":"https://openalex.org/W7165624456","doi":"https://doi.org/10.48550/arxiv.2606.21588"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.21588","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.21588","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.21588","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5024852459","display_name":"Wooseung Kim","orcid":"https://orcid.org/0009-0007-1794-9748"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Wooseung","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139163631","display_name":"Sung-Hong Park","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Park, Sung-Hong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10378","display_name":"Advanced MRI Techniques and Applications","score":0.9901000261306763,"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":0.9901000261306763,"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":0.0032999999821186066,"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"}},{"id":"https://openalex.org/T12603","display_name":"NMR spectroscopy and applications","score":0.0010999999940395355,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6308000087738037},{"id":"https://openalex.org/keywords/distortion","display_name":"Distortion (music)","score":0.5454000234603882},{"id":"https://openalex.org/keywords/image-registration","display_name":"Image registration","score":0.5437999963760376},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.5328999757766724},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45570001006126404},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4259999990463257},{"id":"https://openalex.org/keywords/translation","display_name":"Translation (biology)","score":0.41280001401901245},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.4049000144004822}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.757099986076355},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6507999897003174},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6308000087738037},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5971999764442444},{"id":"https://openalex.org/C126780896","wikidata":"https://www.wikidata.org/wiki/Q899871","display_name":"Distortion (music)","level":4,"score":0.5454000234603882},{"id":"https://openalex.org/C166704113","wikidata":"https://www.wikidata.org/wiki/Q861092","display_name":"Image registration","level":3,"score":0.5437999963760376},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.5328999757766724},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45570001006126404},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4259999990463257},{"id":"https://openalex.org/C149364088","wikidata":"https://www.wikidata.org/wiki/Q185917","display_name":"Translation (biology)","level":4,"score":0.41280001401901245},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.4049000144004822},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.39480000734329224},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.3700000047683716},{"id":"https://openalex.org/C2779751349","wikidata":"https://www.wikidata.org/wiki/Q1474480","display_name":"Scanner","level":2,"score":0.3422999978065491},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3407000005245209},{"id":"https://openalex.org/C96442724","wikidata":"https://www.wikidata.org/wiki/Q242188","display_name":"Invertible matrix","level":2,"score":0.3249000012874603},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.31150001287460327},{"id":"https://openalex.org/C125045340","wikidata":"https://www.wikidata.org/wiki/Q6002224","display_name":"Image formation","level":3,"score":0.30160000920295715},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2897999882698059},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.28459998965263367},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.2623000144958496},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.25839999318122864},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.2506999969482422}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.21588","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.21588","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.21588","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.21588","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Functional":[0],"magnetic":[1,24],"resonance":[2],"imaging":[3,7],"(fMRI)":[4],"utilizes":[5],"echo-planar":[6],"(EPI)":[8],"to":[9,23,111,141,162,211],"capture":[10],"blood-oxygen-level-dependent":[11],"(BOLD)":[12],"signals":[13],"with":[14,188],"high":[15],"temporal":[16],"resolution.":[17],"However,":[18],"EPI":[19],"is":[20],"inherently":[21],"sensitive":[22],"field":[25,50],"inhomogeneities,":[26],"resulting":[27],"in":[28,61,146],"susceptibility-induced":[29],"geometric":[30,79],"distortions":[31,80],"along":[32],"the":[33,107,113,134,143],"phase-encoding":[34],"(PE)":[35],"direction.":[36],"To":[37,63],"correct":[38],"these":[39],"distortions,":[40],"conventional":[41],"approaches":[42],"rely":[43],"on":[44,166,176,203],"additional":[45],"calibration":[46,71],"scans,":[47],"such":[48],"as":[49,106],"maps":[51],"or":[52],"reverse":[53],"PE":[54,97],"acquisitions,":[55],"which":[56],"are":[57],"not":[58],"always":[59],"available":[60,226],"practice.":[62],"overcome":[64],"this":[65],"limitation,":[66],"we":[67,157],"propose":[68],"SACRED,":[69],"a":[70,87,95,126,137],"scan-free":[72],"susceptibility":[73],"distortion":[74,191],"correction":[75,192],"framework":[76],"that":[77,197],"corrects":[78],"via":[81],"image":[82,93,108],"translation-based":[83],"registration":[84,144],"using":[85],"only":[86],"routinely":[88],"acquired":[89],"anatomical":[90],"T1-weighted":[91],"(T1w)":[92],"and":[94,118,181,185,206,213],"unidirectional":[96],"BOLD":[98,117,153],"image.":[99],"SACRED":[100,173,198],"employs":[101],"an":[102,147],"invertible":[103],"neural":[104],"network":[105,145],"translation":[109],"backbone":[110],"bridge":[112],"contrast":[114],"gap":[115],"between":[116],"T1w":[119],"images":[120],"while":[121],"enforcing":[122],"structural":[123],"consistency":[124],"through":[125],"modality":[127],"independent":[128],"neighborhood":[129],"descriptor.":[130],"This":[131],"design":[132],"enables":[133],"use":[135],"of":[136],"mono-contrast":[138],"similarity":[139],"objective":[140],"train":[142],"unsupervised":[148],"manner":[149],"without":[150],"requiring":[151],"distortion-corrected":[152],"images.":[154],"In":[155],"addition,":[156],"incorporate":[158],"test-time":[159],"adaptation":[160],"(TTA)":[161],"further":[163],"enhance":[164],"performance":[165],"out-of-distribution":[167],"(OOD)":[168],"data":[169],"at":[170],"inference":[171],"time.":[172],"was":[174,186],"evaluated":[175],"one":[177],"in-distribution":[178],"(ID)":[179],"dataset":[180],"two":[182],"OOD":[183,207],"datasets,":[184,208],"compared":[187],"representative":[189],"fMRI":[190],"methods.":[193],"The":[194,220],"results":[195],"demonstrate":[196],"significantly":[199],"outperforms":[200],"competing":[201],"methods":[202],"both":[204],"ID":[205],"exhibiting":[209],"robustness":[210],"scanner":[212],"population":[214],"shifts,":[215],"partly":[216],"enabled":[217],"by":[218],"TTA.":[219],"code":[221],"will":[222],"be":[223],"made":[224],"publicly":[225],"upon":[227],"acceptance.":[228]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-24T00:00:00"}
