{"id":"https://openalex.org/W7154490057","doi":"https://doi.org/10.48550/arxiv.2604.12709","title":"Information-Theoretic Optimization for Task-Adapted Compressed Sensing Magnetic Resonance Imaging","display_name":"Information-Theoretic Optimization for Task-Adapted Compressed Sensing Magnetic Resonance Imaging","publication_year":2026,"publication_date":"2026-04-14","ids":{"openalex":"https://openalex.org/W7154490057","doi":"https://doi.org/10.48550/arxiv.2604.12709"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.12709","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.12709","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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.2604.12709","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5113113919","display_name":"Xinyu Peng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peng, Xinyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073652872","display_name":"Ziyang Zheng","orcid":"https://orcid.org/0000-0001-9923-8016"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng, Ziyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133638479","display_name":"Wenrui Dai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dai, Wenrui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086442836","display_name":"Duoduo Xue","orcid":"https://orcid.org/0000-0002-6608-5700"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xue, Duoduo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100760682","display_name":"Shaohui Li","orcid":"https://orcid.org/0000-0002-9650-8874"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Shaohui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133686856","display_name":"Chenglin Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Chenglin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121765747","display_name":"Junni Zou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zou, Junni","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5002494284","display_name":"Hongkai Xiong","orcid":"https://orcid.org/0000-0003-4552-0029"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiong, Hongkai","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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.476500004529953,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.476500004529953,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10378","display_name":"Advanced MRI Techniques and Applications","score":0.2092999964952469,"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":0.1525000035762787,"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/compressed-sensing","display_name":"Compressed sensing","score":0.7878999710083008},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5774000287055969},{"id":"https://openalex.org/keywords/mutual-information","display_name":"Mutual information","score":0.560699999332428},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.44909998774528503},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.43700000643730164},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.43230000138282776},{"id":"https://openalex.org/keywords/optimization-problem","display_name":"Optimization problem","score":0.39649999141693115},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.3765000104904175},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.328000009059906}],"concepts":[{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.7878999710083008},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6754000186920166},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5774000287055969},{"id":"https://openalex.org/C152139883","wikidata":"https://www.wikidata.org/wiki/Q252973","display_name":"Mutual information","level":2,"score":0.560699999332428},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.44909998774528503},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.43700000643730164},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.43230000138282776},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4074000120162964},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.39649999141693115},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3869999945163727},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3862999975681305},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3765000104904175},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.35260000824928284},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.328000009059906},{"id":"https://openalex.org/C2781395549","wikidata":"https://www.wikidata.org/wiki/Q4680762","display_name":"Adaptive sampling","level":3,"score":0.31850001215934753},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.30809998512268066},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.30160000920295715},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3012999892234802},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30059999227523804},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.2913999855518341},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.28700000047683716},{"id":"https://openalex.org/C52622258","wikidata":"https://www.wikidata.org/wiki/Q131222","display_name":"Information theory","level":2,"score":0.2743000090122223},{"id":"https://openalex.org/C165021410","wikidata":"https://www.wikidata.org/wiki/Q55564","display_name":"Lossy compression","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.26589998602867126},{"id":"https://openalex.org/C68781425","wikidata":"https://www.wikidata.org/wiki/Q2052203","display_name":"Multi-objective optimization","level":2,"score":0.26409998536109924},{"id":"https://openalex.org/C94361409","wikidata":"https://www.wikidata.org/wiki/Q7882500","display_name":"Uncertainty reduction theory","level":2,"score":0.2605000138282776},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.25209999084472656},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.251800000667572}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.12709","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.12709","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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.2604.12709","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.12709","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":{"Task-adapted":[0],"compressed":[1],"sensing":[2],"magnetic":[3],"resonance":[4],"imaging":[5],"(CS-MRI)":[6],"is":[7],"emerging":[8],"to":[9,68,78,106,127,175,211,219],"address":[10,55],"the":[11,35,60,65,89,96,112,148,198,220,227],"specific":[12],"demands":[13],"of":[14,154],"downstream":[15],"clinical":[16,52,84,104,156],"tasks":[17,105],"with":[18,49,183],"significantly":[19],"fewer":[20],"k-space":[21,101],"measurements":[22,102],"than":[23],"required":[24],"by":[25,94,226],"Nyquist":[26],"sampling.":[27],"However,":[28],"existing":[29],"task-adapted":[30,62,90],"CS-MRI":[31,63,91],"methods":[32],"suffer":[33],"from":[34,64],"uncertainty":[36,74,113],"problem":[37,93],"for":[38,73,110,124,187],"medical":[39],"diagnosis":[40],"and":[41,76,82,103,119,132,166,179],"cannot":[42],"achieve":[43,70],"adaptive":[44],"sampling":[45,80,138],"in":[46],"end-to-end":[47,144],"optimization":[48,92,118],"reconstruction":[50,169],"or":[51],"tasks.":[53],"To":[54],"these":[56],"limitations,":[57],"we":[58,87],"propose":[59],"first":[61],"information-theoretic":[66],"perspective":[67],"simultaneously":[69],"probabilistic":[71,108],"inference":[72,109],"prediction":[75],"adapt":[77],"arbitrary":[79],"ratios":[81],"versatile":[83],"applications.":[85],"Specifically,":[86],"formalize":[88],"maximizing":[95],"mutual":[97,125],"information":[98,126],"between":[99],"undersampled":[100],"enable":[107],"addressing":[111],"problem.":[114],"We":[115],"leverage":[116],"amortized":[117],"construct":[120],"tractable":[121],"variational":[122],"bounds":[123],"jointly":[128],"optimize":[129],"sampling,":[130],"reconstruction,":[131,167,185],"task-inference":[133],"models,":[134],"which":[135],"enables":[136],"flexible":[137],"ratio":[139],"control":[140],"using":[141],"a":[142,159],"single":[143],"trained":[145],"model.":[146],"Furthermore,":[147],"proposed":[149,199],"framework":[150,200],"addresses":[151],"two":[152],"kinds":[153],"distinct":[155],"scenarios":[157],"within":[158],"unified":[160],"approach,":[161],"i.e.,":[162],"i)":[163],"joint":[164],"task":[165,177,181],"where":[168],"serves":[170],"as":[171,224],"an":[172],"auxiliary":[173],"process":[174],"enhance":[176],"performance;":[178],"ii)":[180],"implementation":[182],"suppressed":[184],"applicable":[186],"privacy":[188],"protection.":[189],"Extensive":[190],"experiments":[191],"on":[192,205],"large-scale":[193],"MRI":[194],"datasets":[195],"demonstrate":[196],"that":[197],"achieves":[201],"highly":[202],"competitive":[203],"performance":[204],"standard":[206],"metrics":[207],"like":[208],"Dice":[209],"compared":[210],"deterministic":[212],"counterpart":[213],"but":[214],"provides":[215],"better":[216],"distribution":[217,223],"matching":[218],"ground-truth":[221],"posterior":[222],"measured":[225],"generalized":[228],"energy":[229],"distance":[230],"(GED).":[231]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-16T00:00:00"}