{"id":"https://openalex.org/W7161846581","doi":"https://doi.org/10.1109/isbi61048.2026.11515575","title":"Super-Resolution MRI Using Latent Fusion and Flow Matching","display_name":"Super-Resolution MRI Using Latent Fusion and Flow Matching","publication_year":2026,"publication_date":"2026-04-08","ids":{"openalex":"https://openalex.org/W7161846581","doi":"https://doi.org/10.1109/isbi61048.2026.11515575"},"language":null,"primary_location":{"id":"doi:10.1109/isbi61048.2026.11515575","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi61048.2026.11515575","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE 23rd International Symposium on Biomedical Imaging (ISBI)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5136588475","display_name":"Yunzhi Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I1284762954","display_name":"Zhejiang A & F University","ror":"https://ror.org/02vj4rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I1284762954"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunzhi Xu","raw_affiliation_strings":["College of Biomedical Engineerin &#x0026; Instrument Science, Zhejiang University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Biomedical Engineerin &#x0026; Instrument Science, Zhejiang University","institution_ids":["https://openalex.org/I1284762954"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5136527753","display_name":"Li Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I1284762954","display_name":"Zhejiang A & F University","ror":"https://ror.org/02vj4rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I1284762954"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Zhao","raw_affiliation_strings":["College of Biomedical Engineerin &#x0026; Instrument Science, Zhejiang University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Biomedical Engineerin &#x0026; Instrument Science, Zhejiang University","institution_ids":["https://openalex.org/I1284762954"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I1284762954"],"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":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":0.7512000203132629,"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"}},"topics":[{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":0.7512000203132629,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.04569999873638153,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.03229999914765358,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5016999840736389},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44999998807907104},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.42500001192092896},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.39419999718666077},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.3125}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.614300012588501},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5182999968528748},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5016999840736389},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4975000023841858},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44999998807907104},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.42500001192092896},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.39419999718666077},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.3125},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2989000082015991},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.26829999685287476},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.258899986743927},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.2572000026702881}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isbi61048.2026.11515575","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi61048.2026.11515575","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE 23rd International Symposium on Biomedical Imaging (ISBI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4685331550","display_name":null,"funder_award_id":"2023YFE0118900","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320311649","display_name":"Ministry of Education","ror":"https://ror.org/036nq5137"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1935694390","https://openalex.org/W1994514622","https://openalex.org/W2085681975","https://openalex.org/W2121654909","https://openalex.org/W2156875969","https://openalex.org/W2166966312","https://openalex.org/W2950564960","https://openalex.org/W2962785568","https://openalex.org/W2963542386","https://openalex.org/W2989249732","https://openalex.org/W3028368183","https://openalex.org/W3132522774","https://openalex.org/W3155072588","https://openalex.org/W4226014430","https://openalex.org/W4312933868","https://openalex.org/W4318767763","https://openalex.org/W4402660161","https://openalex.org/W4414384617"],"related_works":[],"abstract_inverted_index":{"High-resolution":[0],"3D":[1,28,34,77,121],"Magnetic":[2],"Resonance":[3],"Images":[4],"are":[5,13],"important":[6],"for":[7],"clinical":[8],"and":[9,45,92,117,158],"neuroscience":[10],"applications,":[11],"but":[12,22],"timeconsuming.":[14],"2D":[15],"imaging":[16],"protocols":[17],"can":[18],"provide":[19],"motion-freeze":[20],"images,":[21],"their":[23],"low":[24],"through-plane":[25],"resolution":[26],"inhibits":[27],"analysis.":[29],"Therefore,":[30],"reconstruction":[31],"of":[32],"isotropic":[33],"MRI":[35],"from":[36,139],"low-resolution":[37,97],"scans":[38],"is":[39,61,72,81,105,133,162],"highly":[40],"demanded.":[41],"Although":[42],"diffusion":[43],"models":[44],"latent":[46,59,89,115,126],"space":[47,60,90,116,119],"have":[48],"been":[49],"used":[50],"in":[51,74,154],"super-resolution":[52],"reconstruction,":[53],"how":[54],"to":[55,86,95,107,135],"build":[56],"an":[57],"effective":[58],"still":[62],"unclear.":[63],"In":[64],"this":[65],"work,":[66],"a":[67,76,88,125],"novel":[68],"two-stage":[69],"generative":[70],"framework":[71],"proposed,":[73],"which":[75],"Variational":[78],"Autoencoder":[79],"(VAE)":[80],"trained":[82,106],"on":[83,130],"high-resolution":[84,122],"images":[85],"learn":[87,96],"prior":[91],"then":[93],"fine-tuned":[94],"image":[98,148],"features.":[99],"A":[100],"conditional":[101],"flow":[102],"matching":[103],"model":[104],"bridge":[108],"the":[109,113,118,140],"distribution":[110],"divergence":[111],"between":[112],"VAE":[114],"representing":[120],"MRIs.":[123,142],"Additionally,":[124],"fusion":[127],"strategy":[128],"based":[129],"gate":[131],"maps":[132],"proposed":[134,144],"effectively":[136],"integrate":[137],"information":[138],"lowresolution":[141],"The":[143],"method":[145],"demonstrates":[146],"superior":[147],"quality":[149],"compared":[150],"with":[151],"existing":[152],"techniques":[153],"both":[155],"quantitative":[156],"metrics":[157],"visual":[159],"fidelity.":[160],"Code":[161],"available":[163],"at:":[164],"https://github.com/yunzxu/LFFM_MRI.":[165]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-05-21T00:00:00"}
