{"id":"https://openalex.org/W7161832120","doi":"https://doi.org/10.1109/isbi61048.2026.11515791","title":"Bayesian Pet Reconstruction with Learned Normalizing Flow Priors","display_name":"Bayesian Pet Reconstruction with Learned Normalizing Flow Priors","publication_year":2026,"publication_date":"2026-04-08","ids":{"openalex":"https://openalex.org/W7161832120","doi":"https://doi.org/10.1109/isbi61048.2026.11515791"},"language":null,"primary_location":{"id":"doi:10.1109/isbi61048.2026.11515791","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi61048.2026.11515791","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":"article","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/A5106502715","display_name":"Hengjia Ran","orcid":null},"institutions":[{"id":"https://openalex.org/I4210158570","display_name":"Zhejiang University-University of Edinburgh Institute","ror":"https://ror.org/04jth1r26","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210158570","https://openalex.org/I76130692","https://openalex.org/I98677209"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hengjia Ran","raw_affiliation_strings":["ZJU-UIUC Institute"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ZJU-UIUC Institute","institution_ids":["https://openalex.org/I4210158570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136585297","display_name":"Yuxuan Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I4210158570","display_name":"Zhejiang University-University of Edinburgh Institute","ror":"https://ror.org/04jth1r26","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210158570","https://openalex.org/I76130692","https://openalex.org/I98677209"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuxuan Lin","raw_affiliation_strings":["ZJU-UIUC Institute"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ZJU-UIUC Institute","institution_ids":["https://openalex.org/I4210158570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074641460","display_name":"H Liu","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":"Huafeng Liu","raw_affiliation_strings":["College of Biomedical Engineering and Instrument Science, Zhejiang University,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Biomedical Engineering and Instrument Science, Zhejiang University,China","institution_ids":["https://openalex.org/I1284762954"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103114339","display_name":"Bo Zhao","orcid":"https://orcid.org/0000-0002-2120-2571"},"institutions":[{"id":"https://openalex.org/I4210158570","display_name":"Zhejiang University-University of Edinburgh Institute","ror":"https://ror.org/04jth1r26","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210158570","https://openalex.org/I76130692","https://openalex.org/I98677209"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Zhao","raw_affiliation_strings":["ZJU-UIUC Institute"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ZJU-UIUC Institute","institution_ids":["https://openalex.org/I4210158570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5106502715"],"corresponding_institution_ids":["https://openalex.org/I4210158570"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.93622587,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"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/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.163100004196167,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.163100004196167,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11830","display_name":"Point processes and geometric inequalities","score":0.11980000138282776,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.10209999978542328,"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/prior-probability","display_name":"Prior probability","score":0.7157999873161316},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5357999801635742},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.4212000072002411},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40869998931884766},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.2992999851703644}],"concepts":[{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.7157999873161316},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5467000007629395},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5357999801635742},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5156999826431274},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.4212000072002411},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40869998931884766},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.34299999475479126},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33550000190734863},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.31200000643730164},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.2992999851703644},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.2971999943256378},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2644999921321869},{"id":"https://openalex.org/C68022304","wikidata":"https://www.wikidata.org/wiki/Q842217","display_name":"Bayes estimator","level":3,"score":0.2614000141620636}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isbi61048.2026.11515791","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi61048.2026.11515791","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":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1494024482","https://openalex.org/W1963933039","https://openalex.org/W2014351445","https://openalex.org/W2069629287","https://openalex.org/W2074859074","https://openalex.org/W2082399300","https://openalex.org/W2105814974","https://openalex.org/W2112155856","https://openalex.org/W2798538010","https://openalex.org/W2918247176","https://openalex.org/W2962782629","https://openalex.org/W3037333647","https://openalex.org/W3092370246","https://openalex.org/W4380442795","https://openalex.org/W4387211495","https://openalex.org/W4387211598","https://openalex.org/W4387211900","https://openalex.org/W4391560826","https://openalex.org/W4402925060","https://openalex.org/W4410295080","https://openalex.org/W4413887333"],"related_works":[],"abstract_inverted_index":{"Image":[0],"reconstruction":[1],"for":[2,70],"positron":[3],"emission":[4],"tomography":[5],"(PET)":[6],"is":[7],"an":[8,77],"ill-posed":[9],"Poisson":[10],"inverse":[11],"problem.":[12],"While":[13],"traditional":[14],"methods":[15],"like":[16],"MLEM":[17],"and":[18,31,104,119],"MAP-TV":[19],"suffer":[20],"from":[21,93],"noise":[22,102],"or":[23],"artifacts,":[24],"deep":[25],"learning":[26],"approaches":[27],"can":[28],"be":[29],"data-hungry":[30],"lack":[32],"interpretability.":[33],"We":[34,51],"propose":[35],"a":[36,41,47],"novel":[37],"Bayesian":[38,83],"framework":[39],"using":[40,65],"pre-trained":[42],"Normalizing":[43],"Flow":[44],"(NF)":[45],"as":[46,55],"powerful":[48],"image":[49,117],"prior.":[50],"reformulate":[52],"the":[53,59,66,91],"problem":[54],"posterior":[56],"sampling":[57],"in":[58,123],"NF's":[60],"simple,":[61],"Gaussian":[62],"latent":[63],"space,":[64],"Metropolis-Adjusted":[67],"Langevin":[68],"Algorithm":[69],"efficient":[71],"inference.":[72],"This":[73],"latent-space":[74],"approach":[75],"combines":[76],"expressive":[78],"learned":[79],"prior":[80,92],"with":[81],"rigorous":[82],"inference,":[84],"enabling":[85],"robust":[86],"uncertainty":[87],"quantification.":[88],"By":[89],"decoupling":[90],"measurement":[94],"physics,":[95],"our":[96,111],"unsupervised":[97],"method":[98,112],"adapts":[99],"to":[100],"varying":[101],"levels":[103],"forward":[105],"models":[106],"without":[107],"retraining.":[108],"Experiments":[109],"show":[110],"outperforms":[113],"baselines,":[114],"yielding":[115],"superior":[116],"quality":[118],"detail":[120],"preservation,":[121],"especially":[122],"low-count":[124],"scenarios.":[125]},"counts_by_year":[],"updated_date":"2026-05-22T06:13:13.366637","created_date":"2026-05-21T00:00:00"}