{"id":"https://openalex.org/W7133299966","doi":"https://doi.org/10.48550/arxiv.2603.00205","title":"Efficient Flow Matching for Sparse-View CT Reconstruction","display_name":"Efficient Flow Matching for Sparse-View CT Reconstruction","publication_year":2026,"publication_date":"2026-02-27","ids":{"openalex":"https://openalex.org/W7133299966","doi":"https://doi.org/10.48550/arxiv.2603.00205"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.00205","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.00205","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2603.00205","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126659759","display_name":"Jiayang Shi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shi, Jiayang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024505048","display_name":"Lincen Yang","orcid":"https://orcid.org/0000-0003-1936-2784"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Lincen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127897179","display_name":"Zhong Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Zhong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008696762","display_name":"Tristan van Leeuwen","orcid":"https://orcid.org/0000-0002-8794-6426"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"van Leeuwen, Tristan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037323866","display_name":"Dani\u00ebl M. Pelt","orcid":"https://orcid.org/0000-0002-8253-0851"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pelt, Daniel M.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128015918","display_name":"K. Joost Batenburg","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Batenburg, K. Joost","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/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.5230000019073486,"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/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.5230000019073486,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.07249999791383743,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12386","display_name":"Advanced X-ray and CT Imaging","score":0.0674000009894371,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.49959999322891235},{"id":"https://openalex.org/keywords/data-consistency","display_name":"Data consistency","score":0.4869000017642975},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.45419999957084656},{"id":"https://openalex.org/keywords/bounded-function","display_name":"Bounded function","score":0.4489000141620636},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.4320000112056732},{"id":"https://openalex.org/keywords/inverse-problem","display_name":"Inverse problem","score":0.39809998869895935},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.38929998874664307},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.38510000705718994},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.38179999589920044}],"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/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.49959999322891235},{"id":"https://openalex.org/C93361087","wikidata":"https://www.wikidata.org/wiki/Q4426698","display_name":"Data consistency","level":2,"score":0.4869000017642975},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.45419999957084656},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.45419999957084656},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.4489000141620636},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.4320000112056732},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4284000098705292},{"id":"https://openalex.org/C135252773","wikidata":"https://www.wikidata.org/wiki/Q1567213","display_name":"Inverse problem","level":2,"score":0.39809998869895935},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.38929998874664307},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.38510000705718994},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.38179999589920044},{"id":"https://openalex.org/C22324862","wikidata":"https://www.wikidata.org/wiki/Q652707","display_name":"Lipschitz continuity","level":2,"score":0.3698999881744385},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3628999888896942},{"id":"https://openalex.org/C91188154","wikidata":"https://www.wikidata.org/wiki/Q186247","display_name":"Vector field","level":2,"score":0.3626999855041504},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.35350000858306885},{"id":"https://openalex.org/C163716698","wikidata":"https://www.wikidata.org/wiki/Q841267","display_name":"Tomography","level":2,"score":0.3391000032424927},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.3384000062942505},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3287999927997589},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.32440000772476196},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.31949999928474426},{"id":"https://openalex.org/C2779898584","wikidata":"https://www.wikidata.org/wiki/Q7820109","display_name":"Reconstruction algorithm","level":3,"score":0.3167000114917755},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.31470000743865967},{"id":"https://openalex.org/C51955184","wikidata":"https://www.wikidata.org/wiki/Q1545585","display_name":"Stochastic differential equation","level":2,"score":0.3131999969482422},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.30820000171661377},{"id":"https://openalex.org/C51544822","wikidata":"https://www.wikidata.org/wiki/Q465274","display_name":"Ordinary differential equation","level":3,"score":0.30480000376701355},{"id":"https://openalex.org/C37279795","wikidata":"https://www.wikidata.org/wiki/Q2492305","display_name":"Consistency model","level":3,"score":0.30000001192092896},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.2985000014305115},{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.29269999265670776},{"id":"https://openalex.org/C93779851","wikidata":"https://www.wikidata.org/wiki/Q271977","display_name":"Partial differential equation","level":2,"score":0.2921999990940094},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.26170000433921814},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.26080000400543213}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.00205","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.00205","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.00205","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.00205","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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":{"Generative":[0],"models,":[1],"particularly":[2],"Diffusion":[3],"Models":[4],"(DM),":[5],"have":[6],"shown":[7],"strong":[8,108],"potential":[9],"for":[10,19,33],"Computed":[11],"Tomography":[12],"(CT)":[13],"reconstruction":[14,26,54,64,121,180],"serving":[15],"as":[16,75],"expressive":[17],"priors":[18],"solving":[20],"ill-posed":[21],"inverse":[22],"problems.":[23],"However,":[24],"diffusion-based":[25,189],"relies":[27],"on":[28],"Stochastic":[29],"Differential":[30,79],"Equations":[31],"(SDEs)":[32],"forward":[34],"diffusion":[35],"and":[36,60,124],"reverse":[37],"denoising,":[38],"where":[39],"such":[40],"stochasticity":[41],"can":[42],"interfere":[43],"with":[44,95,169,188],"repeated":[45,96],"data":[46,97,170],"consistency":[47,98,171],"corrections":[48],"in":[49,58],"CT":[50,53,120],"reconstruction.":[51],"Since":[52],"is":[55,66,92,165,193],"often":[56],"time-critical":[57],"clinical":[59],"interventional":[61],"scenarios,":[62],"improving":[63,150,184],"efficiency":[65,186],"essential.":[67],"In":[68],"contrast,":[69],"Flow":[70],"Matching":[71],"(FM)":[72],"models":[73],"sampling":[74],"a":[76],"deterministic":[77,90],"Ordinary":[78],"Equation":[80],"(ODE),":[81],"yielding":[82],"smooth":[83],"trajectories":[84],"without":[85],"stochastic":[86],"noise":[87],"injection.":[88],"This":[89],"formulation":[91],"naturally":[93],"compatible":[94],"operations.":[99,172],"Furthermore,":[100],"we":[101,116],"observe":[102],"that":[103,129,158,176],"FM-predicted":[104],"velocity":[105,133,163],"fields":[106,134],"exhibit":[107],"correlations":[109],"across":[110],"adjacent":[111],"steps.":[112],"Motivated":[113],"by":[114,162],"this,":[115],"propose":[117],"an":[118,125],"FM-based":[119],"framework":[122],"(FMCT)":[123],"efficient":[126],"variant":[127],"(EFMCT)":[128],"reuses":[130],"previously":[131],"predicted":[132],"over":[135],"consecutive":[136],"steps":[137],"to":[138],"substantially":[139],"reduce":[140],"the":[141,159],"number":[142],"of":[143],"Neural":[144],"network":[145],"Function":[146],"Evaluations":[147],"(NFEs),":[148],"thereby":[149],"inference":[151],"efficiency.":[152],"We":[153],"provide":[154],"theoretical":[155],"analysis":[156],"showing":[157],"error":[160],"introduced":[161],"reuse":[164],"bounded":[166],"when":[167],"combined":[168],"Extensive":[173],"experiments":[174],"demonstrate":[175],"FMCT/EFMCT":[177],"achieve":[178],"competitive":[179],"quality":[181],"while":[182],"significantly":[183],"computational":[185],"compared":[187],"methods.":[190],"The":[191],"codebase":[192],"open-sourced":[194],"at":[195],"https://github.com/EFMCT/EFMCT.":[196]},"counts_by_year":[],"updated_date":"2026-07-08T06:17:01.165560","created_date":"2026-03-04T00:00:00"}