{"id":"https://openalex.org/W2050885986","doi":"https://doi.org/10.1109/isbi.2008.4541178","title":"Bayesian PET image reconstruction incorporating anato-functional joint entropy","display_name":"Bayesian PET image reconstruction incorporating anato-functional joint entropy","publication_year":2008,"publication_date":"2008-05-01","ids":{"openalex":"https://openalex.org/W2050885986","doi":"https://doi.org/10.1109/isbi.2008.4541178","mag":"2050885986"},"language":"en","primary_location":{"id":"doi:10.1109/isbi.2008.4541178","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2008.4541178","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","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/A5101927628","display_name":"Jing Tang","orcid":"https://orcid.org/0000-0002-8961-138X"},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jing Tang","raw_affiliation_strings":["Department of Radiology, Johns Hopkins University, Baltimore, MD, USA","Dept. of Radiol., Johns Hopkins Univ., Baltimore, MD"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Radiology, Johns Hopkins University, Baltimore, MD, USA","institution_ids":["https://openalex.org/I145311948"]},{"raw_affiliation_string":"Dept. of Radiol., Johns Hopkins Univ., Baltimore, MD","institution_ids":["https://openalex.org/I145311948"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034237477","display_name":"B.M.W. Tsui","orcid":"https://orcid.org/0000-0001-7928-5093"},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Benjamin M. W. Tsui","raw_affiliation_strings":["Department of Radiology, Johns Hopkins University, Baltimore, MD, USA","Dept. of Radiol., Johns Hopkins Univ., Baltimore, MD"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Radiology, Johns Hopkins University, Baltimore, MD, USA","institution_ids":["https://openalex.org/I145311948"]},{"raw_affiliation_string":"Dept. of Radiol., Johns Hopkins Univ., Baltimore, MD","institution_ids":["https://openalex.org/I145311948"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021438906","display_name":"Arman Rahmim","orcid":"https://orcid.org/0000-0002-9980-2403"},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Arman Rahmim","raw_affiliation_strings":["Department of Radiology, Johns Hopkins University, Baltimore, MD, USA","Dept. of Radiol., Johns Hopkins Univ., Baltimore, MD"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Radiology, Johns Hopkins University, Baltimore, MD, USA","institution_ids":["https://openalex.org/I145311948"]},{"raw_affiliation_string":"Dept. of Radiol., Johns Hopkins Univ., Baltimore, MD","institution_ids":["https://openalex.org/I145311948"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I145311948"],"apc_list":null,"apc_paid":null,"fwci":2.5849,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.88953675,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"47","issue":null,"first_page":"1043","last_page":"1046"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10522","display_name":"Medical Imaging Techniques and Applications","score":1.0,"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":1.0,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.994700014591217,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9936000108718872,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.6927063465118408},{"id":"https://openalex.org/keywords/imaging-phantom","display_name":"Imaging phantom","score":0.6201245784759521},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.6023366451263428},{"id":"https://openalex.org/keywords/joint-entropy","display_name":"Joint entropy","score":0.5956119894981384},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5739549994468689},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.5726048946380615},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5533947348594666},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5335752367973328},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4934214651584625},{"id":"https://openalex.org/keywords/kernel-density-estimation","display_name":"Kernel density estimation","score":0.48876476287841797},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4668838083744049},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.4586304724216461},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.36009472608566284},{"id":"https://openalex.org/keywords/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.35791000723838806},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.19914034008979797},{"id":"https://openalex.org/keywords/nuclear-medicine","display_name":"Nuclear medicine","score":0.18950936198234558},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.108428955078125},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.081432044506073}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6927063465118408},{"id":"https://openalex.org/C104293457","wikidata":"https://www.wikidata.org/wiki/Q28324852","display_name":"Imaging phantom","level":2,"score":0.6201245784759521},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.6023366451263428},{"id":"https://openalex.org/C106752470","wikidata":"https://www.wikidata.org/wiki/Q1364826","display_name":"Joint entropy","level":3,"score":0.5956119894981384},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5739549994468689},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.5726048946380615},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5533947348594666},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5335752367973328},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4934214651584625},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.48876476287841797},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4668838083744049},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.4586304724216461},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.36009472608566284},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.35791000723838806},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.19914034008979797},{"id":"https://openalex.org/C2989005","wikidata":"https://www.wikidata.org/wiki/Q214963","display_name":"Nuclear medicine","level":1,"score":0.18950936198234558},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.108428955078125},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.081432044506073},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C24890656","wikidata":"https://www.wikidata.org/wiki/Q82811","display_name":"Acoustics","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isbi.2008.4541178","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2008.4541178","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1525391989","https://openalex.org/W1874027545","https://openalex.org/W1980288291","https://openalex.org/W1990323259","https://openalex.org/W1993002126","https://openalex.org/W1993388378","https://openalex.org/W2024903557","https://openalex.org/W2025968223","https://openalex.org/W2031368339","https://openalex.org/W2063596143","https://openalex.org/W2095765877","https://openalex.org/W2103707010","https://openalex.org/W2115780003","https://openalex.org/W2140935196","https://openalex.org/W2147712886","https://openalex.org/W2152693305","https://openalex.org/W2153929453","https://openalex.org/W2168530812","https://openalex.org/W2533038408","https://openalex.org/W2799061466","https://openalex.org/W4244494905","https://openalex.org/W6681698553"],"related_works":["https://openalex.org/W2389730489","https://openalex.org/W2377575454","https://openalex.org/W2889261288","https://openalex.org/W1519338247","https://openalex.org/W2949821122","https://openalex.org/W1672097335","https://openalex.org/W2034921015","https://openalex.org/W2147544021","https://openalex.org/W59993211","https://openalex.org/W2051944318"],"abstract_inverted_index":{"We":[0],"developed":[1],"a":[2,4],"maximum":[3],"posterior":[5],"(MAP)":[6],"reconstruction":[7,12],"method":[8,34],"for":[9,53,62],"PET":[10,23,46,69,75],"image":[11,15,26],"incorporating":[13],"MR":[14,25,48,77],"information,":[16],"with":[17],"the":[18,22,30,39,45,58,80,84,93,97,103,106,113,124],"joint":[19,40],"entropy":[20],"between":[21],"and":[24,47,66,76],"features":[27],"serving":[28],"as":[29,100,102],"prior.":[31],"A":[32],"non-parametric":[33],"was":[35,60,87,119],"used":[36],"to":[37,121],"estimate":[38],"probability":[41],"density":[42],"(JPD)":[43],"of":[44,57,83,112,132],"images.":[49,71],"The":[50],"sampling":[51],"rate":[52],"Parzen":[54,107],"window":[55,108],"estimation":[56],"JPD":[59],"studied":[61],"both":[63],"simulated":[64,74],"phantom":[65],"clinical":[67],"FDG":[68],"brain":[70,78],"Using":[72],"realistic":[73],"phantoms,":[79],"quantitative":[81],"performance":[82],"proposed":[85],"algorithm":[86],"investigated.":[88],"In":[89],"particular,":[90],"variations":[91],"in":[92,105,129],"weighting":[94],"factor":[95],"on":[96],"MAP":[98],"prior":[99],"well":[101],"variance":[104],"were":[109],"examined.":[110],"Incorporation":[111],"anatomical":[114],"information":[115],"via":[116],"this":[117],"technique":[118],"seen":[120],"noticeably":[122],"improve":[123],"noise":[125],"vs.":[126],"bias":[127],"tradeoff":[128],"various":[130],"regions":[131],"interest.":[133]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2015,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
