{"id":"https://openalex.org/W7166686851","doi":"https://doi.org/10.48550/arxiv.2606.28448","title":"Measured-Subspace Consistency: A Plug-and-Play Operator for Diffusion Posterior Sampling in Accelerated MRI Reconstruction","display_name":"Measured-Subspace Consistency: A Plug-and-Play Operator for Diffusion Posterior Sampling in Accelerated MRI Reconstruction","publication_year":2026,"publication_date":"2026-06-26","ids":{"openalex":"https://openalex.org/W7166686851","doi":"https://doi.org/10.48550/arxiv.2606.28448"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.28448","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.28448","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.2606.28448","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139662408","display_name":"Junhyeok Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Junhyeok","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5006749140","display_name":"Kyu Sung Choi","orcid":"https://orcid.org/0000-0002-5175-3307"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Choi, Kyu Sung","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/T11304","display_name":"Advanced Neuroimaging Techniques and Applications","score":0.8226000070571899,"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/T11304","display_name":"Advanced Neuroimaging Techniques and Applications","score":0.8226000070571899,"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/T10378","display_name":"Advanced MRI Techniques and Applications","score":0.1265999972820282,"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":0.029100000858306885,"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/diffusion-mri","display_name":"Diffusion MRI","score":0.5264999866485596},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.46619999408721924},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.45170000195503235},{"id":"https://openalex.org/keywords/scanner","display_name":"Scanner","score":0.4120999872684479},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.3637000024318695},{"id":"https://openalex.org/keywords/dispersion","display_name":"Dispersion (optics)","score":0.3431999981403351},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.3400000035762787},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.3382999897003174},{"id":"https://openalex.org/keywords/coupling","display_name":"Coupling (piping)","score":0.325300008058548},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.3100000023841858}],"concepts":[{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.553600013256073},{"id":"https://openalex.org/C149550507","wikidata":"https://www.wikidata.org/wiki/Q899360","display_name":"Diffusion MRI","level":3,"score":0.5264999866485596},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4823000133037567},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.46619999408721924},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.46549999713897705},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.45170000195503235},{"id":"https://openalex.org/C2779751349","wikidata":"https://www.wikidata.org/wiki/Q1474480","display_name":"Scanner","level":2,"score":0.4120999872684479},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.3637000024318695},{"id":"https://openalex.org/C177562468","wikidata":"https://www.wikidata.org/wiki/Q182893","display_name":"Dispersion (optics)","level":2,"score":0.3431999981403351},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.3400000035762787},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.3382999897003174},{"id":"https://openalex.org/C131584629","wikidata":"https://www.wikidata.org/wiki/Q4308705","display_name":"Coupling (piping)","level":2,"score":0.325300008058548},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3215999901294708},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.3100000023841858},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.30799999833106995},{"id":"https://openalex.org/C2776639384","wikidata":"https://www.wikidata.org/wiki/Q840396","display_name":"Ideal (ethics)","level":2,"score":0.3027999997138977},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.29649999737739563},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2896000146865845},{"id":"https://openalex.org/C100906024","wikidata":"https://www.wikidata.org/wiki/Q205692","display_name":"Poisson distribution","level":2,"score":0.2870999872684479},{"id":"https://openalex.org/C175291020","wikidata":"https://www.wikidata.org/wiki/Q1156822","display_name":"Offset (computer science)","level":2,"score":0.2842999994754791},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28189998865127563},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.2766999900341034},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.27129998803138733},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.2703999876976013},{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.26829999685287476},{"id":"https://openalex.org/C2776029896","wikidata":"https://www.wikidata.org/wiki/Q3935810","display_name":"Relaxation (psychology)","level":2,"score":0.26820001006126404},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.26809999346733093},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.2669000029563904},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.26429998874664307},{"id":"https://openalex.org/C137209882","wikidata":"https://www.wikidata.org/wiki/Q1403517","display_name":"Measurement uncertainty","level":2,"score":0.2635999917984009},{"id":"https://openalex.org/C57691317","wikidata":"https://www.wikidata.org/wiki/Q1289248","display_name":"Scalar (mathematics)","level":2,"score":0.25600001215934753},{"id":"https://openalex.org/C2780378346","wikidata":"https://www.wikidata.org/wiki/Q1349983","display_name":"Leak","level":2,"score":0.2513999938964844},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.250900000333786}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.28448","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.28448","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.2606.28448","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.28448","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Diffusion":[0],"posterior":[1,19,89,114],"samplers":[2,156,177],"for":[3,175,182,201],"accelerated":[4],"MRI":[5,139,159],"can":[6],"reconstruct":[7],"accurately":[8],"yet":[9],"still":[10],"disagree":[11],"on":[12,21],"the":[13,23,43,48,55,130,138,144],"acquired":[14],"k-space":[15,70],"across":[16,184],"samples,":[17],"placing":[18],"variability":[20],"coefficients":[22,54],"scanner":[24,56],"has":[25,57],"already":[26,58],"measured.":[27],"We":[28,74],"identify":[29],"this":[30,62],"measured-subspace":[31,173],"leakage":[32],"as":[33,111,197],"a":[34,38,80,92,112,120,165,198],"physical-admissibility":[35],"failure.":[36],"Under":[37],"hard-constraint":[39],"model":[40],"it":[41,110],"violates":[42],"measurement":[44],"constraint":[45],"and":[46,68,116,142,157,195],"inflates":[47],"reported":[49],"uncertainty":[50],"with":[51,91,211],"disagreement":[52],"about":[53],"determined.":[59],"To":[60],"quantify":[61],"leakage,":[63],"we":[64,127],"introduce":[65],"complementary":[66],"measured-":[67],"unmeasured-subspace":[69,193],"dispersion":[71,174],"metrics":[72],"(MSD/USD).":[73],"then":[75],"present":[76],"Measured-Subspace":[77],"Consistency":[78],"(MSC),":[79],"training-free":[81],"terminal":[82],"correction":[83,117],"that":[84,129],"wraps":[85],"any":[86],"compatible":[87],"image-space":[88],"sampler":[90],"standard":[93],"multi-coil":[94],"consistency":[95],"lock.":[96],"The":[97],"ideal":[98,131],"lock":[99],"follows":[100],"classical":[101],"range/null-space":[102],"data":[103],"consistency.":[104],"Our":[105],"contribution":[106],"is":[107],"to":[108,137,189],"repurpose":[109],"black-box":[113],"audit":[115],"rather":[118],"than":[119],"new":[121],"reconstructor":[122],"or":[123,207,215],"learned":[124],"sampler.":[125],"Theoretically,":[126],"prove":[128],"transform":[132],"confines":[133],"pairwise":[134],"sample":[135],"differences":[136],"null":[140],"space":[141],"bound":[143],"residual":[145],"cross-subspace":[146],"coupling":[147],"left":[148],"by":[149],"practical":[150],"sensitivity-weighted":[151],"implementations.":[152],"Across":[153],"six":[154],"base":[155],"two":[158],"anatomies,":[160],"including":[161],"out-of-distribution":[162],"transfer":[163],"where":[164],"knee":[166],"prior":[167],"reconstructs":[168],"brain,":[169],"MSC":[170,205],"substantially":[171],"reduces":[172],"Soft":[176],"(a":[178],"median":[179],"16.5x":[180],"reduction":[181],"DPS":[183],"five":[185],"brain":[186],"contrasts,":[187],"up":[188],"~29x),":[190],"while":[191],"preserving":[192],"diversity":[194],"acting":[196],"near-identity":[199],"map":[200],"Consistent":[202],"ones.":[203],"Furthermore,":[204],"maintains":[206],"modestly":[208],"improves":[209],"PSNR/SSIM,":[210],"no":[212],"retraining,":[213],"retuning,":[214],"significant":[216],"computational":[217],"overhead.":[218]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-01T00:00:00"}
